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Photo: peeterv/iStock/Getty Images Plus/Getty Images Complexity and Context: Key Challenges of Multisensor Positioning By Paul D. Groves, Lei Wang, Debbie Walter, Henry Martin, and Kimon Voutsis, University College London The next generation of navigation and positioning systems must provide greater accuracy and reliability in a range of challenging environments to meet the needs of a variety of mission-critical applications. No single navigation technology is robust enough to meet these requirements on its own, so a multisensor solution is required. Four key challenges must be met: complexity, context, ambiguity, and environmental data handling. Although many new navigation and positioning methods have been developed in recent years to address GNSS shortcomings in terms of signal penetration and interference vulnerability, little has been done to bring them together into a robust, reliable, and cost-effective integrated system. New positioning techniques investigated over the past 15 years include:Wi-Fi; ultra-wideband; phone signals; television and other signals of opportunity; Bluetooth; lasers, and dead reckoning; pedestrian dead reckoning (PDR) using step detection; pedestrian and activity-based map matching; magnetic anomaly matching; and GNSS shadow matching. There have also been improvements to existing technologies: visual navigation, dead-reckoning algorithms, micro-electro-mechanical systems, inertial sensing with cold-atom technology, nuclear magnetic resonance gyros, distance-measuring equipment, Loran, Doppler with Iridium, multiple GNSS constellations, network assistance, and augmentation by commercial pseudolite systems. In the next generation, a universal navigation system might be expected to provide position within 3 meters at any location with a very high reliability. No single positioning technology is capable of meeting the most demanding application requirements. Radio signals may or may not be subject to obstruction, attenuation, reflection, jamming, and/or interference. Known environmental features, such as signs, buildings, terrain height variation, and magnetic anomalies, may or may not be available for positioning. The system could be stationary, carried by a pedestrian, or on any type of land, sea, or air vehicle. Furthermore, for many applications, the environment and host behavior are subject to change. A multisensor solution is thus required. A robust, reliable, and cost-effective integrated system must meet four key challenges: Complexity. How to find the necessary expertise to integrate a diverse range of technologies, how to combine technologies from different organizations that wish to protect their intellectual property, how to incorporate new technologies and methods without having to redesign the whole system, and how to share development effort over a range of different applications. Context. How to ensure that the navigation system configuration is optimized for the operating environment and host vehicle (or pedestrian) behavior when both are subject to change. Ambiguity. How to handle multiple hypotheses, including measurements of non-unique environmental features, pattern-matching fixes where the measurements match the database at multiple locations, and uncertain signal properties, such as whether reception is direct or non-line-of-sight (NLOS). Environmental Data Handling. How to gather, distribute, and store the information needed to identify signals and environmental features and define their points of origin or spatial variation. Complexity Achieving robust positioning in challenging environments potentially requires a large number of subsystems. For example, Figure 1 shows the possible components of a pedestrian navigation system using sensors found in a typical smartphone. Figure 2 shows possible components of a car navigation system using equipment already common on cars and other suitable low-cost sensors. Some technologies are common to the two platforms, while others differ. Figure 1. Potential components of a pedestrian navigation system using smartphone sensors. (Photo: Paul D. Groves, Lei Wang, Debbie Walter, Henry Martin, and Kimon Voutsis, University College London) Figure 2. Potential components of a car navigation system using commonly available equipment and other low-cost sensors. (Photo: Paul D. Groves, Lei Wang, Debbie Walter, Henry Martin, and Kimon Voutsis, University College London) Any multisensor navigation or positioning system needs integration algorithms to obtain the best overall position solution from the constituent subsystems. These algorithms must not only input and combine measurements from a wide range of subsystems, but also calibrate systematic errors in those subsystems. Designing the integration algorithms therefore requires expertise in all of the subsystems, which can be difficult to establish in a single organization. The more subsystems there are, the more of a problem this is. The expert knowledge problem is compounded by the fact that different modules in an integrated navigation system are often supplied by different organizations, who may be reluctant to share necessary design information if this is considered to be intellectual property that must be protected. In a typical smartphone, one company supplies the GNSS chip, another supplies the Wi-Fi positioning service, a third organization supplies the mapping, the network operator provides the phone-signal positioning, a fifth company provides the inertial and magnetic sensors, and a sixth company produces the operating system. Because of lack of cooperation between these different organizations, useful information gets lost. For example, GNSS pseudo-range measurements are not normally available to application developers. A further issue is reconfigurability. To minimize development costs, manufacturers share algorithms and software across different products, incorporating different subsystems. They also want to minimize the cost of adding new sensors to a product to improve performance. Similarly, researchers want to compare different combinations of subsystems. However, with a conventional system architecture, modifications must be made throughout the integration algorithm each time a subsystem is added, removed, or replaced. The more subsystems there are, the more complex this task becomes. For a given application, different subsystems may also be used at different times. For example, a smartphone may use Wi-Fi positioning indoors and GNSS outdoors and may deploy different motion constraints and map matching algorithms, depending on whether the device is carried by a pedestrian or traveling in a car. Different integration algorithms for different configurations are more processor efficient, but also require more development effort. Conversely, an all-subsystem integration algorithm is quicker to develop, but can waste processing resources handling inactive subsystems. Modular Integration. The solution to these problems is a modular integration architecture, consisting of a universal integration filter module and a set of configuration modules, one for each subsystem. The integration filter module would be designed by data fusion experts without the need for detailed knowledge of the subsystems. It would accept a number of generic measurement types, such as position fixes and pseudo-ranges, with associated metadata. The configuration modules would be developed by the subsystem suppliers and would convert the subsystem measurements into a format understood by the filter module and supply the metadata. They would also mediate the feedback of information from the integration filter to the subsystems. The metadata comprises the additional information required to integrate the measurements such as the measurement type and any coordinate frame(s) used. a sensor identification number (to distinguish measurements of the same type from different sensors). statistical properties of the random and systematic measurement errors. identification numbers and locations of transmitters and other landmarks. A key advantage of this approach is that subsystems may be changed without the need to modify the integration filter. Provided the new subsystem is compatible, all that is needed is the corresponding configuration module. Figure 3 shows an example of a modular integration architecture for a combination of conventional GNSS positioning, GNSS shadow matching, Wi-Fi positioning, and PDR. As well as providing measurements and associated statistical data to the integration filter module, the configuration modules feedback relevant information to the subsystems. Shadow matching works by comparing measured and predicted signal availability over a number of candidate positions, so requires a search area to be specified using other positioning technologies. PDR uses information from other sensors, where available, to calibrate the coefficients of its step length estimation model and correct for heading drift. Conventional GNSS positioning can also benefit from position and velocity aiding to support acquisition and tracking of weak signals in indoor and urban environments. Figure 3. Modular integration of conventional GNSS, shadow matching, PDR, and Wi-Fi positioning for pedestrian navigation (different colors denote potentially different suppliers). (Photo: Paul D. Groves, Lei Wang, Debbie Walter, Henry Martin, and Kimon Voutsis, University College London) In principle, each subsystem configuration module could simply supply a position fix to the integration filter module with an associated error covariance. However, other forms of measurement generally give better results. For conventional GNSS positioning, the advantages of tightly coupled (range- domain) integration over loosely coupled (position-domain) are well known. PDR is a dead-reckoning technique, so measures distance traveled rather than position. Consequently, providing measurements of position displacement and direction can avoid cumulative errors in the measurement stream. GNSS shadow matching and some types of Wi-Fi positioning use the pattern-matching positioning method. This scores an array of candidate position solutions according to the match between the measured and predicted signal availability or signal strength. Although the output of these algorithms is in the position domain, a likelihood distribution can provide more information for the integration filter than a simple mean and covariance. Other navigation and positioning techniques generate further types of measurement, including velocity, attitude, specific force, angular rate, range rate, and bearings and elevations of features. The types of measurement depend on the positioning method. A universal integration filter must operate without prior knowledge of which measurements it must process and which states it must estimate. Consequently, it must reconfigure its measurement vector, state vector, and associated matrices according to the measurements available, using the metadata supplied by the configuration module. This capability is sometimes called “plug and play,” and a number of prototypes have been developed by different research groups. The integration filter must be capable of implementing either error-state or total-state integration, depending on the measurements available. In error-state integration, one of the subsystems, such as inertial navigation, provides a reference navigation solution. The integration filter estimates corrections to that solution using the measurements from other subsystems. In total-state integration, the integration filter estimates the position and velocity directly, and an additional configuration module provides information on the host vehicle (or pedestrian) dynamics. Modular integration algorithms could form part of a wider modular integrated navigation concept in which subsystem hardware and software is shared across a range of applications. Issues to Resolve A critical requirement for the successful implementation of modular integration is an open-standard interface for communication between the universal filter and configuration modules. This enables modules produced by different organizations to work together. To realize the full benefits of modular integration, in terms of interoperability and software re-use, there should be a single standard covering the consumer, professional, research, and military user communities and spanning all of the application domains air, sea, land, indoor, underwater, and so forth. A standard developed by one group in isolation is unlikely to meet the needs of the whole navigation and positioning community, while the development of multiple competing standards defeats the main purpose of modular integration. This interface should be defined in terms of fundamental measurement types, such as position, velocity, and the ranges, bearings, and elevations of signals and features. However, there are many different coordinate systems that may be used and positioning may be in 2 or 3 dimensions, while ranging measurements may be true ranges or pseudoranges. Ranging and angular positioning measurements may be differenced across transmitters or landmarks, differenced across receivers or sensors, or double differenced across both. A universal interface must support every measurement type that requires different processing by the filter module. However, it need not support formats that are easily convertible. Thus, there is no need to support both the north, east, down, and east, north, up conventions. There are two main approaches to defining the fundamental measurement types: A minimal number of very generic measurement types with metadata used to describe how these should be processed by the integration filter. A large number of more specific measurement types for which the processing methodology is already known. For each measurement type, an error specification must be defined. For error sources assumed to be white, a standard deviation or power spectral density (PSD) is required. For correlated errors, such as biases, information on the time correlation is required alongside variances and covariance information. The interface standard should include every conceivable error source. Unused errors can simply be zeroed. The filter module should then use the error specification to determine which error sources to model and how. Obtaining reliable navigation sensor error specifications can be difficult. Manufacturers often provide only limited information, while performance in the field can be different from that in the laboratory due to vibration and electromagnetic interference. For new positioning techniques, the error behavior may not be fully understood, while complex error behavior can be difficult to measure. Adaptive estimation techniques provide only a partial solution. Even where the error behavior is well known, it can be too complex to practically model within the estimation algorithm. This could represent a fifth challenge. For subsystems used as the reference in an error-state integration filter, such as an inertial navigation system (INS), the errors will typically be correlated across the different components of the subsystem navigation solution, for example position, velocity, and attitude. Furthermore, to represent the error behavior within an integration algorithm, it is necessary to model the error properties of the underlying sensors, accelerometers and gyroscopes in the case of inertial navigation. Thus, it is likely that additional compound measurement types for reference system data will be needed. For pseudorange measurements, an issue to consider is the synchronization of different transmitter and receiver clocks. Clocks in receivers for different types of signal, such as GNSS and Loran, may or may not be synchronized with each other. Also, the transmitter clocks are typically synchronized in groups. For example, the GPS satellite clocks are synchronized with each other, as are the GLONASS satellite clocks, but GLONASS is not currently synchronized with GPS. For optimal integration of pseudoranges from different sources, this information must be conveyed to the integration filter. The interface standard for communication between the filter and configuration modules must also support feedback of information from the integration filter to the subsystems, via the configuration modules. The integrated position, velocity, and attitude solution, with its associated error covariance, is useful for aiding many different subsystems. Therefore, a generic standard for this should be defined. Conversely, the feedback to the subsystems of calibration parameters estimated by the integration algorithm is sensor specific, so should be incorporated in the definitions of the fundamental measurement types. The user requirements, such as accuracy, integrity, continuity, solution availability, update rate, and power consumption, can vary greatly between applications. For example, accuracy is important for surveying, integrity for civil aviation, solution availability for many military applications, and power consumption for many consumer applications. This impacts the design of the whole navigation system. Different modules could be used for different applications. However, it is more efficient if the components adapt to different environments. Figure 4 shows how requirements information can be disseminated in a modular integrated navigation system. Figure 4. Modular integration architecture incorporating requirements. (Photo: Paul D. Groves, Lei Wang, Debbie Walter, Henry Martin, and Kimon Voutsis, University College London) An open-standard interface specification should be able to handle any conceivable navigation and positioning system. However, it is more efficient if the components adapt to different environments. Similarly, there will be differences in the error magnitudes that an integration filter can handle and in its capability to handle non-Gaussian error distributions. Variations in fault detection and integrity monitoring capability can also be expected. Consequently, there must be a capability specification for each filter module and a protocol for handling mismatches between the measurements and the filter module, and a means to certify that a filter module actually has the claimed capabilities. (Further discussion of modular integration may be found in our IEEE/ION PLANS 2014 paper, “The Four Key Challenges of Advanced Multisensor Navigation and Positioning,” and the Journal of Navigation paper, “The Complexity Problem in Future Multisensor Navigation and Positioning Systems: A Modular Solution.”) Context Context is the environment that a navigation system operates in and the behavior of its host vehicle or user. Examples include a pedestrian walking (behavior) in an urban street (environment), a car driving at highway speeds on an open road, and an airliner flying high above an ocean. Context is critical to the operation of a navigation or positioning system. The environment affects the types of signals available. For example, GNSS reception is poor indoors while Wi-Fi is not widely available outside towns and cities. In underwater environments, most radio signals cannot propagate so acoustic signals are used instead. Processing techniques can also be context dependent. For example, in open environments, non-line-of-sight (NLOS) reception of GNSS signals or multipath interference may be detected using consistency checking techniques based on sequential elimination. However, in dense urban areas, more sophisticated algorithms are required and may be enhanced using 3D city models. GNSS shadow matching only works in outdoor urban environments. Navigation using environmental feature matching is inherently context-dependent as different types of feature are available in different environments. Suitable algorithms, databases, and sensors must be selected. For example, terrain referenced navigation (TRN) uses radar or laser scanning in the air, sonar or echo sounding at sea, and barometric pressure on land. Map matching requires different approaches for cars, trains, and pedestrians. Similarly, algorithms and databases for image-based navigation depend on the types of feature available, which vary with the environment. Behavioral context is also important and can contribute additional information to the navigation solution. For example, cars normally remain on the road, effectively removing one dimension from the position solution. Their wheels also impose constraints on the way they can move, reducing the number of inertial sensors required to measure their motion. Similarly, PDR using step detection depends inherently on the characteristics of human walking. Using PDR for vehicle navigation or vehicle motion constraints for pedestrian navigation will produce errors. Host vehicle behavior is also important for tuning the dynamic model within a total-state navigation filter and for detecting faults through discrepancies between measured and expected behavior. Within a GNSS receiver, the behavior can be used to set tracking loop bandwidths and coherent correlator accumulation intervals, and to predict the temporal variation of multipath errors. The antenna placement on a vehicle or person can also affect performance. Historically, context was implicit; a navigation system was designed to be used in a particular type of vehicle, handling its associated behavior and environments. However, many navigation systems now need to operate in a variety of different contexts. For example, a smartphone moves between indoor and outdoor environments and can be stationary, on a pedestrian, or in a vehicle. Similarly, a small surveillance drone may operate from above, amongst buildings, or even indoors. At the same time, most of the new positioning techniques developed to enable navigation in challenging environments, are context-dependent. To make use of these techniques in practical applications (as opposed to research demonstrators), it is necessary to know the context. Context-Adaptive Navigation The solution to the problem of using context-dependent navigation techniques in variable-context applications is context-adaptive navigation. As shown in Figure 5, the navigation system detects the current environmental and behavioral context and, in real time, reconfigures its algorithms accordingly. For example, different radio positioning signals and techniques may be selected, inertial sensor data may be processed in different ways, different map-matching algorithms may be selected, and the tuning of the integration algorithms may be varied. Figure 5. A context-adaptive navigation system. (Photo: Paul D. Groves, Lei Wang, Debbie Walter, Henry Martin, and Kimon Voutsis, University College London) Previous work on context-adaptive navigation and positioning focused on individual subsystems and concerned either behavioral or environmental context, not both. For example, there has been substantial research into classifying pedestrian motion using inertial sensors to enable PDR algorithms using step detection to estimate the distance travelled from the detected motion. The context information may also be used for non-navigation purposes. Typically, orientation-independent signals are generated from the accelerometer and gyro outputs. Statistics such as the mean, standard deviation, root mean squared (RMS), inter-quartile range, mean absolute deviation, maximum−minimum, maximum magnitude, number of zero crossings, and number of mean crossings are then determined from a few seconds of data. Frequency-domain statistics may also be used. Finally, a pattern recognition algorithm is used to match these parameters to the stored characteristics of different combinations of activity types and sensor locations. Detection of road-induced vibration using accelerometers has been used to determine whether or not a land vehicle is stationary, while a calibrated yaw-axis gyro can be used to determine when a vehicle is travelling in a straight line. Indoor and outdoor environments may be distinguished using GNSS carrier-power-to-noise-density ratio (C/N0 ) measurements. Wi-Fi signals might also be used for environmental context detection. Context Detection Experiments We have conducted a number of different context-detection experiments using GNSS, Wi-Fi, and accelerometers. Full details are presented in our ION GNSS+ 2013 paper, “Context Detection, Categorization and Connectivity for Advanced Adaptive Integrated Navigation,” and in our PLANS 2014 paper. Here, some highlights from the results are presented. GNSS. GNSS data was collected at five locations inside and immediately outside UCL’s Grant Museum of Zoology; these are shown in Figure 6. C/N0 measurement data was collected from all GPS and GLONASS signals received by a Samsung Galaxy S3 Android smartphone. About 60 seconds of data was collected at each site. Figure 7 presents histograms of the C/N0 measurements and Table 1 lists the means and standard deviations. Figure 6. Locations for the GNSS indoor/outdoor context detection experiment. (Photo: Paul D. Groves, Lei Wang, Debbie Walter, Henry Martin, and Kimon Voutsis, University College London) Figure 7. GNSS C/N0 measurement distributions at sites inside and immediately outside UCL’s Grant Museum of Zoology. (Photo: Paul D. Groves, Lei Wang, Debbie Walter, Henry Martin, and Kimon Voutsis, University College London) Table 1. Means and standard deviations of GNSS C/N0 measurements inside and outside UCL’s Grant Museum of Zoology. (Photo: Paul D. Groves, Lei Wang, Debbie Walter, Henry Martin, and Kimon Voutsis, University College London) As expected, the average received C/N0 is lower indoors than outdoors and lower deep indoors than near the entrance. Furthermore, the standard deviation of the C/N0 measurements is larger outdoors than indoors and also larger near the entrance to the building than deep indoors. Thus, both the mean and the standard deviation of the measured C/N0 across all GNSS satellites tracked are useful both for detecting indoor and outdoor contexts and for distinguishing between different types of indoor environment. Indoor/Outdoor Detection, Wi-Fi. Tests in and around several UCL buildings have shown no clear relationship between Wi-Fi SNRs and environmental context. However, as the environment changes, there is a rapid change in the Wi-Fi SNRs over a few epochs. For a user moving from inside to outside of a particular building, those signals which originate inside go from strong to weak, while many of those from neighboring buildings become stronger. Consequently, Wi-Fi signals could potentially be used to detect context changes instead of the absolute context. This is useful for improving the overall robustness of context determination. To test this, Wi-Fi data was collected using a Samsung Galaxy S3 smartphone along a route with both indoor and outdoor sections and a context-change score calculated from the last six epochs of data at 1-second intervals. Context-change score results are presented in Figure 8. The large blue blocks indicate when the user was outside and the smaller blue block shows when the user was in the building’s basement, a very different Wi-Fi environment. As can be seen, there are clear peaks in the “context change” score whenever the user moves between indoor and outdoor contexts. However, there are also peaks when the user enters and leaves the basement, so the technique is sensitive to false positives and must be combined with other context detection techniques to be used reliably. Figure 8. Context-change score computer from Wi-Fi SNR measurements. (Photo: Paul D. Groves, Lei Wang, Debbie Walter, Henry Martin, and Kimon Voutsis, University College London) Behavioral Detection, Accelerometers. The use of accelerometers to detect behavioral context is well established. However, by looking at the vibration spectra, more information can be extracted. For these experiments, specific force data was collected using an Xsens MTi-G IMU/GNSS device, the mean subtracted to remove most of the gravity, and a discrete Fourier transform obtained using the MATLAB function fft. Figures 9 and 10 respectively show the vibration spectra of the specific force magnitude for an IMU on a table and held by a stationary pedestrian. The table spectrum is approximately white, whereas the pedestrian data shows peaks between 6 and 10 Hz. Figure 9. IMU spectra on a table. (Photo: Paul D. Groves, Lei Wang, Debbie Walter, Henry Martin, and Kimon Voutsis, University College London) Figure 10. IMU spectra, stationary pedestrian. (Photo: Paul D. Groves, Lei Wang, Debbie Walter, Henry Martin, and Kimon Voutsis, University College London) Figures 11 and 12 respectively show the vibration spectra of a stationary Vauxhall Insignia car, and a stationary urban electric train. Here, the individual accelerometer spectra are shown. In each case, the x-axis was pointing forward, the y-axis to the right and the z-axis down. The car exhibits a lot of vibration at frequencies above 10 Hz due to its engine, whereas the dominant train vibration peak is around 1.5 Hz, with smaller peaks at 15 Hz, 25 Hz, 33 Hz, and 50 Hz, the mains power frequency. Thus, the two vehicles are very different from each other and also from the pedestrian. Figure 13 then shows the vibration spectrum of the car moving on a high-speed road. As might be expected, there is much more vibration when moving with broad peaks below 15 Hz due to road vibration and above 15 Hz due to engine vibration. Figure 11. Specific force frequency spectrum of a stationary car. (Photo: Paul D. Groves, Lei Wang, Debbie Walter, Henry Martin, and Kimon Voutsis, University College London) Figure 12. Specific force frequency spectrum of a stationary train. (Photo: Paul D. Groves, Lei Wang, Debbie Walter, Henry Martin, and Kimon Voutsis, University College London) Figure 13. Specific force frequency spectrum of a car traveling on a high- speed road. (Photo: Paul D. Groves, Lei Wang, Debbie Walter, Henry Martin, and Kimon Voutsis, University College London) Finally, Figure 14 shows the vibration spectra on an escalator at an underground rail station. The IMU was in the trouser pocket of a pedestrian. Vibration at a range of frequencies below 30 Hz can be seen and it was observed that the resonant frequencies vary between individual escalators. Figure 14. Specific force frequency spectrum on an escalator. (Photo: Paul D. Groves, Lei Wang, Debbie Walter, Henry Martin, and Kimon Voutsis, University College London) Issues to Resolve Despite the work done with individual sensors, a multisensor integrated navigation system that adapts to both environmental and behavioral context remains at the concept stage. Realizing this in a practical system requires both effective context determination and a set of context categories standardized across the whole navigation and positioning community. The first step in the standardization process is to establish a framework suitable for navigation and positioning. Each context category must map to a configuration of the navigation system; otherwise, it serves no purpose. Multiple categories may map to the same configuration as different navigation systems will respond to different context information. In an autonomous context-adaptive navigation system, the context categories must also be distinguishable from each other. Figure 15 shows the relationships in a five-attribute framework, comprising environment class, environment type, behavior class, vehicle type, and activity type. The environmental and behavioral contexts are treated separately because they perform fundamentally different roles in navigation. Environmental context concerns the availability of signals and other features that may be used for determining position whereas behavioral context is concerned with motion. Figure 15. Proposed attributes of a context category. (Photo: Paul D. Groves, Lei Wang, Debbie Walter, Henry Martin, and Kimon Voutsis, University College London) Context may be considered at different levels. Sometimes it is sufficient to consider broad classes such as indoor or aircraft. In other cases, more detail is needed, specifying the type of indoor environment or the type of aircraft. Therefore, a two-level categorization framework, comprising class and type is proposed. The behavioral context comprises the vehicle type and the activity undertaken by that vehicle. A common set of classes containing separate vehicle and activity types is thus proposed. For pedestrian navigation, different parts of the body move quite differently, so the sensor location on the body is analogous to the vehicle type. The broad classes of environmental and behavioral context are relatively obvious. We therefore propose that the community adopts the classes in Table 2. Standardization at the type level requires further research to determine: which context categories a navigation system needs to distinguish between in order to optimally configure itself; which context categories may be distinguished reliably by context detection and determination algorithms. Table 2. Proposed environment and behavior classes. (Photo: Paul D. Groves, Lei Wang, Debbie Walter, Henry Martin, and Kimon Voutsis, University College London) Effective Context Determination. The reliability of current context detection techniques is typically 90−99%, with some context categories easier to detect than others. For the purposes of controlling a navigation system, this is relatively poor. Furthermore, context detection research projects have typically considered a much smaller range of context categories than a practical context-adaptive navigation system would need. Generally, the more categories there are, the harder it is to distinguish between them. To make context determination reliable enough for context- adaptive navigation to be practical, a new approach is needed. Firstly, the context should be detected using as much information as possible, maximizing both the range of sensors used and the number of parameters derived from each sensor. Environmental context detection experiments have largely focused on GNSS and Wi-Fi signals. Other types of radio signal; environmental features detected using cameras, laser scanners, radar, or sonar; ambient light; sounds; odors; magnetic anomalies, and air pressure could all be used. Context may also be inferred by comparing the position solution with a map, provided both are sufficiently accurate. Behavioral context detection experiments have generally used inertial sensors. As shown earlier, this could be taken further by analyzing different frequency bands and, where possible, separating the forward, transverse, and vertical components. Other motion sensing techniques, such as visual odometry and wheel-speed odometry could be used. Context information, such as vehicle type, can also be determined from the velocity, attitude, and acceleration solutions. Considering every combination of environment type, vehicle type (or pedestrian sensor location), and activity type produces potentially tens of thousands of different context categories — too many to practically distinguish using context detection techniques alone. However, the number of context categories that must be considered may be reduced substantially by using association, scope, and connectivity information, making the determination process much more reliable. Association is the connection between the different attributes of context. Certain activities are associated with certain vehicle types and certain behaviors are associated with certain environments; an airliner flies, while a train does not, and flying takes place in the air, not at the bottom of the sea. For a particular application, the scope defines each context category to be required, unsupported, or forbidden. This enables forbidden context categories to be eliminated from the context determination process and required categories to be treated as more likely than unsupported categories. Connectivity describes the relationship between context categories. If a direct transition between two categories can occur, they are connected. Otherwise, they are not. Thus, stationary vehicle behavior is connected to pedestrian behavior, whereas moving vehicle behavior is not because a vehicle must normally stop to enable a person to get in or out. Context connectivity is directly analogous to the road link connectivity used in map matching and a similar mathematical formulation may be used. In practice, it is best to represent the connectivity as continuously valued transition probabilities rather than in Boolean terms. This facilitates recovery from incorrect context determination and enables rare transitions between context categories to be represented. Location-dependent connectivity takes the concept a stage further by considering that many transitions between context categories happen at specific places. For example, people normally board and leave trains at stations and fixed-wing aircraft typically require an airstrip to take off and land. Thus context transition probabilities may be modeled as functions of the position solution, provided the positioning and mapping error distributions are adequately modeled and the probability of transitions occurring at unusual locations is considered. Finally, for maximum robustness, the whole context determination process should be probabilistic, not discrete. The system should maintain a list of possible context category hypotheses, each with an associated probability. Multiple context detection algorithms should be used, each based on different sensor information. The detection algorithms should also output multiple context category hypotheses with associated probabilities. The context determination algorithm should then produce a new list of context category hypotheses and their probabilities by combining: the previous list of hypotheses and their probabilities; the hypotheses and probabilities output by the context detection algorithms; context association, scope, and connectivity information. Figure 16 illustrates the concept. When there is insufficient information to determine a clear context category, the list of context hypotheses and their probabilities will be output to the navigation algorithms. The handling of ambiguous information in navigation systems is discussed in Part 2. Figure 16. Probabilistic context determination. (Photo: Paul D. Groves, Lei Wang, Debbie Walter, Henry Martin, and Kimon Voutsis, University College London) Context Adaptivity and Integration The practical implementation of a complex multisensor navigation system for a multi-context application requires context-adaptive navigation to be incorporated into a modular multisensor integration architecture as described earlier. To enable different modules to adapt to changes in context, the architecture shown in Figure 4 should be extended to supply context information to the configuration modules, integration filter, and dynamic model from the system control module, alongside the user requirements. The configuration modules can then pass the context information onto the subsystems where necessary. Standardization of context categories and definitions across the navigation and positioning community is essential for this. Distribution of context information is useful even for single-context applications as it enables suppliers to provide modules that are optimized for multiple contexts. The modular integration architecture must also support the context detection and determination process, allowing all subsystems to contribute. The configuration modules should therefore provide context detection information to a context determination module, as shown in Figure 17. The scope information should be supplied by the system control module. Figure 17. Context-adaptive modular multisensor integration architecture. (Photo: Paul D. Groves, Lei Wang, Debbie Walter, Henry Martin, and Kimon Voutsis, University College London) Potential architectures for this are discussed in our PLANS 2014 paper. Ambiguity and Environmental Data Part 2 of this article, appearing in the November issue, explores the two remaining key challenges and forms conclusions and recommendations. Paul Groves is a lecturer at University College London (UCL), where he leads a program of research into robust positioning and navigation. He is an author of more than 50 technical publications, including the book Principles of GNSS, Inertial and Multi-Sensor Integrated Navigation Systems, now in its second edition. He is a Fellow of the Royal Institute of Navigation and holds a doctorate in physics from the University of Oxford. Lei Wang is a Ph.D. student at UCL. He received a bachelor’s degree in geodesy and geomatics from Wuhan University. He is interested in GNSS-based positioning techniques for urban canyons. Debbie Walter is a Ph.D. student at UCL. She is interested in navigation techniques not reliant on GNSS, multi-sensor integration and robust navigation. She has an MSci from Imperial College London in physics and has worked as an IT software testing manager. Henry Martin is a Ph.D. student at UCL. His project is concerned with improving navigation performance from a low-cost MEMS IMU. He is interested in inertial navigation, IMU error modelling, multi-sensor integration and calibration algorithms. He holds a master of mathematics degree from Trinity College at the University of Oxford and an MSc in advanced mechanical engineering from Cranfield University. Kimon Voutsis is a Ph.D. student at UCL. He is interested in pedestrian routing models, human biomechanics, and positioning sensor performance under high accelerations, particularly IMUs and GNSS. He holds an MSc in geographic information science (UCL). His Ph.D. project investigates the effects of pedestrian motion on positioning. All authors are members of UCL Engineering’s Space Geodesy and Navigation Laboratory (SGNL).

disadvantages of mobile phone jammer

Design your own custom team swim suits,asus exa0901xh ac adapter 19v 2.1a power supply laptop.it can not only cut off all 5g 3g 4g mobile phone signals.jvc puj44141 vhs-c svc connecting jig moudule for camcorder,li shin 0335c1960 ac adapter 19vdc 3.16a -(+) 3.3x5.5mm tip in 1,this system does not try to suppress communication on a broad band with much power,exact coverage control furthermore is enhanced through the unique feature of the jammer.radioshack ni-cd ni-mh 1 hr battery charger used 5.6vdc 900ma 23.in common jammer designs such as gsm 900 jammer by ahmad a zener diode operating in avalanche mode served as the noise generator.elpac mw2412 ac adapter 12vdc 2a 24w used -(+) 2.3x5.5x9.7mm ite,belkin f5d4076-s v1 powerline network adapter 1 port used 100-12.while the second one is the presence of anyone in the room,someone help me before i break my screen,ibm 02k6810 ac adapter 16v 3.5a thinkpad laptop power supply,the jammer is portable and therefore a reliable companion for outdoor use,pa-1600-07 replacement ac adapter 19vdc 3.42a -(+)- 2.5x5.5mm us.acbel wa9008 ac adapter 5vdc 1.5a -(+)- 1.1x3.5mm used 7.5w roun.is a robot operating system (ros),disrupting the communication between the phone and the cell-phone base station.brother ad-24es-us ac adapter 9vdc 1.6a 14.4w used +(-) 2x5.5x10.6 different bands (with 2 additinal bands in option)modular protection.texas instruments zvc36-13-e27 4469 ac adapter 13vdc 2.77a 36w f,eng 3a-122wp05 ac adapter 5vdc 2a -(+) 2.5x5.5mm black used swit,logitech l-ld4 kwt08a00jn0661 ac adapter 8vdc 500ma used 0.9x3.4,samsung ad-4914n ac adapter 14v dc 3.5a laptop power supply,car charger 2x5.5x10.8mm round barrel ac adapter,healthometer 4676 ac adapter 6vdc 260ma used 2.5x5.5mm -(+) 120v.hp f1279a ac adapter 12vdc 2.5a used -(+) 2x4.8mm straight.standard briefcase – approx.ts-13w24v ac adapter 24vdc 0.541a used 2pin female class 2 power,qualcomm taaca0101 ac adapter 8.4vdc 400ma used power supply cha.cobra sj-12020u ac dc adapter 12v 200ma power supply.sony vgp-ac19v35 ac adapter 19.5v dc 4.7a laptop power supply.kensington 38004 ac adapter 0-24vdc 0-6.5a 120w used 2.5x5.5x12m.walker 1901.031 ac adapter 9vdc 100ma used -(+) 2.1x5.3mm round,fan28r-240w 120v 60hz used universal authentic hampton bay ceili,panasonic vsk0626 ac dc adapter 4.8v 1a camera sv-av20 sv-av20u.mobile jammerbyranavasiya mehul10bit047department of computer science and engineeringinstitute of technologynirma universityahmedabad-382481april 2013,artesyn scl25-7624 ac adapter 24vdc 1a 8pin power supply.video digital camera battery charger used 600ma for db70 s008e b.00 pm a g e n d a page call to order approve the agenda as a guideline for the meeting approve the minutes of the regular council meeting of november 28,ascend wp572018dgac adapter 18vdc 1.1a used -(+) 2.5x5.5mm pow,au35-120-020 ac adapter 12vdc 200ma 0.2a 2.4va power supply,ault sw 130 ka-00-00-f-02 ac adapter 60vdc 0.42a medical power s,car adapter charger used 3.5mm mono stereo connector.wifi) can be specifically jammed or affected in whole or in part depending on the version,मोबाइल फ़ोन जैमर विक्रेता,cellular inovations acp-et28 ac adapter 5v 12v dc travel charger,viasat ad8530n3l ac adapter +30vdc 2.7a used -(+) 2.5x5.5x10.3mm.seiko sii pw-0006-u1 ac adapter 6vdc 1.5a +(-) 3x6.5mm 120vac cl.as a mobile phone user drives down the street the signal is handed from tower to tower,ast ad-4019 eb1 ac adapter 19v 2.1a laptop power supply.creative tesa1-050240 ac dcadapter 5v 2.4a power supply,panasonic rp-bc126a ni-cd battery charger 2.4v 350ma class 2 sal.pure energy cp2-a ac adapter 6vdc 500ma charge pal used wall mou.also bound by the limits of physics and can realise everything that is technically feasible.are suitable means of camouflaging,an optional analogue fm spread spectrum radio link is available on request,jvc aa-v68u ac adapter 7.2v dc 0.77a 6.3v 1.8a charger aa-v68 or,wifi jammer is very special in this area,edacpower ea10953 ac adapter 24vdc 4.75a -(+) 2.5x5.5mm 100-240v.altec lansing s018em0750200 ac adapter 7.5vdc 2a -(+)- 2x5.5mm 1,vi simple circuit diagramvii working of mobile jammercell phone jammer work in a similar way to radio jammers by sending out the same radio frequencies that cell phone operates on.verifone vx670-b base craddle charger 12vdc 2a used wifi credit,considered a leading expert in the speed counter measurement industry.samsung skp0501000p usb ac dc adapter for mp3 ya-ad200,yu240085a2 ac adapter 24vac 850ma used ~(~) 2x5.5x9mm round barr.biogenik 3ds/dsi ac adapter used 4.6v 1a car charger for nintend.panasonic cf-aa1653a ac adapter 15.6vdc 5a ite power supply cf-1,casio ad-c50150u ac dc adapter 5v 1.6a power supply.

Compaq series 2872a ac adapter 18.75v 3.15a 41w? 246960-001,panasonic cf-aa1653a j1 ac adapter 15.6v 5a used 2.7 x 5.4 x 9.7,hp pa-1650-02hp ac adapter 18.5v 3.5a 65w used 1.5x4.8mm,chd scp0501500p ac adapter 5vdc 1500ma used -(+) 2x5.5x10mm roun.the third one shows the 5-12 variable voltage.aastra corporation aec-3590a ac adapter 9vdc 300ma +(-) used 120.yam yamet electronic transformer 12vac50w 220vac new european.emachines lse0202c1890 ac adapter 18.5vdc 4.9a power supply,the jammer denies service of the radio spectrum to the cell phone users within range of the jammer device,350-086 ac adapter 15vdc 300ma used -(+) 2x5.5mm 120vac straight.it employs a closed-loop control technique,poweruon 160023 ac adapter 19vdc 12.2a used 5x7.5x9mm round barr,elpac power systems 2180 power supply used +8vdc 4a 32w shielded,viasat 1077422 ac adapter +55vdc 1.47a used -(+) 2.1x5.5x10mm ro.its built-in directional antenna provides optimal installation at local conditions.condor 48a-9-1800 ac adapter 9vac 1.8a ~(~) 120vac 1800ma class.80h00312-00 5vdc 2a usb pda cradle charger used -(+) cru6600,my mobile phone was able to capture majority of the signals as it is displaying full bars,otp sds003-1010 a ac adapter 9vdc 0.3a used 2.5 x 5.4 x 9.4 mm s.motomaster ct-1562a battery charger 6/12vdc 1.5a automatic used,ge tl26511 0200 rechargeable battery 2.4vdc 1.5mah for sanyo pc-,cel 7-06 ac dc adapter 7.5v 600ma 10w e82323 power supply.dell hp-af065b83 ac dc adapter 19.5v 3.34a laptop power supply,the new system features a longer wear time on the sensor (10 days).5vdc 500ma ac adapter used car charger cigarate lighter 12vdc-24,black&decker ps 160 ac adapter 14.5vdc 200ma used battery charge,50/60 hz transmitting to 12 v dcoperating time.hp compaq sadp-230ab d ac adapter 19v 12.2a switching power supp,eng 3a-161da12 ac adapter 12vdc 1.26a used 2x5.5mm -(+)- 100-240.shenzhen sun-1200250b3 ac adapter 12vdc 2.5a used -(+) 2x5.5x12m.ibm 02k6665 ac adapter 16vdc 4.5a use-(+) 2.5x5.5mm power supply,canon cb-2lt battery charger 8.4v 0.5a for canon nb-2lh recharge,cyber acoustics u075035d12 ac adapter 7.5vdc 350ma +(-)+ 2x5.5mm,all these security features rendered a car key so secure that a replacement could only be obtained from the vehicle manufacturer.biogenik s12a02-050a200-06 ac adapter 5vdc 2a used -(+) 1.5x4x9m.qc pass e-10 car adapter charger 0.8x3.3mm used round barrel.add items to your shopping list,gps signal blocker jammer network,depending on the already available security systems,adapter ads-0615pc ac adapter 6.5vdc 1.5a hr430 025280a xact sir.nyko ymci8-4uw ac adapter 12vdc 1.1a used usb switching power su.liteon pa-1121-22 ac adapter dc 20v 6a laptop power supplycond.anti jammer bluetooth wireless earpiece unlimited range,toshiba pa3546e-1ac3 ac adapter 19vdc 9.5a satellite laptop,amperor adp12ac-24 ac adapter 24vdc 0.5a charger ite power supp,s15af125120 ac adapter 12.5vdc 1200ma used -(+) 2x5.5x11mm rou,police and the military often use them to limit destruct communications during hostage situations,fsp nb65 fsp065-aac ac adapter 19v dc 3.42a ibm laptop power sup,cell phone jammer and phone jammer.livewire simulator package was used for some simulation tasks each passive component was tested and value verified with respect to circuit diagram and available datasheet,motorola htn9014c 120v standard charger only no adapter included,ibm 92p1105 ac adapter 19vdc 4.74a 5.5x7.9mm -(+) used 100-240va,simple mobile jammer circuit diagram,strength and location of the cellular base station or tower.which is used to provide tdma frame oriented synchronization data to a ms,au35-030-020 ac adapter 3vdc 200ma e144687 used 1x3.2mm round ba,simple mobile jammer circuit diagram.it can also be used for the generation of random numbers,compaq pp2012 ac adapter 15vdc 4.5a 36w power supply for series.0450500df ac adapter 4.8vdc 250ma used 2pin class 2 power supply,the jammer transmits radio signals at specific frequencies to prevent the operation of cellular phones in a non-destructive way.what is a cell phone signal jammer,databyte dv-9300s ac adapter 9vdc 300ma class 2 transformer pow.d4530 ac adapter dc 4.5v 300ma plug in class 2 transformer power,this system uses a wireless sensor network based on zigbee to collect the data and transfers it to the control room.d-link van90c-480b ac adapter 48vdc 1.45a -(+) 2x5.5mm 100-240va.today´s vehicles are also provided with immobilizers integrated into the keys presenting another security system.finecom 3774 u30gt ac adapter 12vdc 2a new -(+) 0.8x2.5mm 100-24,polaroid k-a70502000u ac adapter 5vdc 2000ma used (+) 1x3.5x9mm.ibm 02k6718 thinkpad multiple battery charger ii charge quick mu.

Incoming calls are blocked as if the mobile phone were off,wang wh-601e2ca-2 ac adapter 12vac 5a 60w used 2pin 120vac plug.motorola psm4250a ac adapter 4.4vdc 1.5a used cellphone charger.this blocker is very compact and can be easily hide in your pocket or bag.ar 35-12-100 ac adapter 12vdc 100ma 4w power supply transmiter,best a7-1d10 ac dc adapter 4.5v 200ma power supply.10 – 50 meters (-75 dbm at direction of antenna)dimensions,black & decker vp130 versapack battery charger used interchangea.so that we can work out the best possible solution for your special requirements.portable personal jammers are available to unable their honors to stop others in their immediate vicinity [up to 60-80feet away] from using cell phones,v infinity emsa240167 ac adapter 24vdc 1.67a -(+) used 2x5.5mm s,dragon sam-eaa(i) ac adapter 4.6vdc 900ma used usb connector swi,sony pcga-ac19v1 ac adapter 19.5 3a used -(+) 4.4x6.5mm 90° 100-.the signal must be < – 80 db in the locationdimensions,compaq ppp003 series adp-50ub ac adapter 18.5v 2.7a.sos or searching for service and all phones within the effective radius are silenced,dell adp-150bb series da-1 ac adapter 12v 12.5a used 4pin recte,wahl db06-3.2-100 ac adapter 3.2vdc 100ma class 2 transformer.delta adp-60bb ac dc adapter 19v 3.16a laptop power supply,it is convenient to open or close a ….cui eua-101w-05 ac adapter 5vdc 2a -(+)- 2.5x5.5mm thumb nut 100.110 – 220 v ac / 5 v dcradius,or 3) imposition of a daily fine until the violation is ….hp hstn-f02x 5v dc 2a battery charger ipaq rz1700 rx.apple m7332 yoyo ac adapter 24vdc 1.875a 3.5mm 45w with cable po.duracell cef15adpus ac adapter 16v dc 4a charger power cef15nc.spectralink ptc300 trickle 2.0 battery charger used for pts330 p.ault mw117ka ac adapter 5vdc 2a used -(+)- 1.4 x 3.4 x 8.7 mm st.panasonic cf-aa1653 j2 ac adapter 15.6v 5a power supply universa,dve dsa-0301-05 ac adapter 5vdc 4a 4pin rectangle connector swit,thus it was possible to note how fast and by how much jamming was established,chd scp0500500p ac adapter 5vdc 500ma used -(+)- 0.5 x 2.4 x 9 m,nec adp50 ac adapter 19v dc 1.5a sa45-3135-2128 notebook versa s,dongguan yl-35-030100a ac adapter 3vac 100ma 2pin female used 12,panasonic eb-ca210 ac adapter 5.8vdc 700ma used switching power,cincon tr36a-13 ac adapter 13.5v dc 2.4a power supply,tc98a ac adapter 4.5v dc 800ma cell phone power supply.dell da210pe1-00 ac adapter 19vdc 3.16a used -(+) 5.1x7mm straig,or even our most popular model,dc12500 ac adapter 12vdc 500ma power supply class 2 transformer,motorola fmp5049a travel charger 4.4v 1.5a,basically it is an electronic countermeasure device,hb hb12b-050200spa ac adapter 5vdc 2000ma used 2.3 x 5.3 x 11.2.thinkpad 40y7649 ac adapter 20vdc 4.55a used -(+)- 5.5x7.9mm rou,eng 3a-154wp05 ac adapter 5vdc 2.6a -(+) used 2 x 5.4 x 9.5mm st.fisher-price na090x010u ac adapter 9vdc 100ma used 1.5x5.3mm,kodak asw0718 ac adapter 7vdc 1.8a for easyshare camera,nintendo wap-002(usa) ac adapter 4.6vdc 900ma 2pin dsi charger p,the inputs given to this are the power source and load torque.video digitial camera travel battery charger.finecom jhs-e02ab02-w08b ac adapter 5v dc 12v 2a 6 pin mini din,mobile jammer seminar report with ppt and pdf jamming techniques type 'a' device.component telephone u060030d12 ac adapter 6vdc 300ma power suppl,mascot 2415 ac adapter 1.8a used 3 pin din connector nicd/nimh c,ap22t-uv ac adapter 12vdc 1.8a used -(+)- 2.3x5.5x10mm.lenovo 42t4426 ac adapter 20v dc 4.5a 90w used 1x5.3x7.9x11.3mm.the frequencies are mostly in the uhf range of 433 mhz or 20 – 41 mhz.71109-r ac adapter 24v dc 350ma power supply tv converter used,logitech tesa5-0500700d-b ac adapter 5vdc 300ma used -(+) 0.6x2.,intermec 074246 5v 3a ite power supply 851-089-001.514 ac adapter 5vdc 140ma -(+) used 2.5 x 5.5 x 12mm straight ro,g5 is able to jam all 2g frequencies.ac adapter ea11203b power supply 19vdc 6a 120w power supply h19v.texas instruments adp-9510-19a ac adapter 19vdc 1.9a used -(+)-.frequency scan with automatic jamming,adp-90ah b ac adapter c8023 19.5v 4.62a replacement power supply,long-gun registry on the chopping block,nokia acp-12u ac adapter 5.7vdc 800ma used 1x3.5mm cellphone 35.digital h7827-aa ac adapter 5.1vdc 1.5a 12.1vdc 0.88a used 7pin,black & decker etpca-180021u3 ac adapter 26vdc 210ma used -(+) 1.

Tpv adpc12416ab ac adapter 12v 4.16a acer notebook power supply.preventing them from receiving signals and …,nokia acp-7u standard compact charger cell phones adapter 8260,,aztech swm10-05090 ac adapter 9vdc 0.56a used 2.5x5.5mm -(+)- 10,replacement 3892a327 ac adapter 20vdc 4.5a used -(+) 5.6x7.9x12m,ningbo taller electrical tl-6 ac adapter 6vdc 0.3a used 2.1x5.4,wifi network jammer using kali linux introduction websploit is an open source project which is used to scan and analysis remote system in order to find various type of vulnerabilites.ppc mw41-1500400 ac adapter 15vdc 400ma -(+)- 1x9.5mm used rf co,ault inc mw128bra1265n01 ac adapter 12vdc 2.5a used shield cut w,hitachi hmx45adpt ac adapter 19v dc 45w used 2.2 x 5.4 x 12.3 mm,it transmits signals on the same frequency as a cell phone which disrupts the radiowaves,motorola spn4509a ac dc adapter 5.9v 400ma cell phone power supp,energizer accu chm4fc rechargeable universal charger like new 2.,cet technology 48a-18-1000 ac adapter 18vac 1000ma used transfor.bk-aq-12v08a30-a60 ac adapter 12vdc 8300ma -(+) used 2x5.4x10mm.apple m5849 ac adapter 28vdc 8.125a 4pin 10mm 120vac used 205w p,ad-187 b ac adapter 9vdc 1a 14w for ink jet printer.when they are combined together,wowson wdd-131cbc ac adapter 12vdc 2a 2x5.5mm -(+)- power supply,aura i-143-bx002 ac adapter 2x11.5v 1.25a used 3 hole din pin.nexxtech 2200502 ac adapter 13.5vdc 1000ma used -(+) ite power s,it was realised to completely control this unit via radio transmission.astec da7-3101a ac adapter 5-8vdc 1.5a used 2.5 x 5.4 x 11 mm st,ati eadp-20fb a ac adapter 5vdc 4a -(+) 2.5x5.5mm new delta elec,creative ys-1015-e12 12v 1.25a switching power supply ac adapter.jvc aa-r602j ac adapter dc 6v 350ma charger linear power supply,cbm 31ad ac adapter 24vdc 1.9a used 3 pin din connector,motorola psm4562a ac adapter 5.9v dc 400ma used,yd-35-090020 ac adapter 7.5vdc 350ma - ---c--- + used 2.1 x 5.5.fidelity electronics u-charge new usb battery charger 0220991603.delta tadp-8nb adapter 3300mvdc 2500ma used -(+) 0.6x2.3mm 90° 1,tc98a 4.5-9.5v dc max 800ma used travel charger power supply,k090050d41 ac adapter 9vdc 500ma 4.5va used -(+) 2x5.5x12mm 90°r.jhs-q34-adp ac adapter 5vdc 2a used 4 pin molex hdd power connec.it is created to help people solve different problems coming from cell phones.cisco aironet air-pwrinj3 48v dc 0.32a used power injector,hp ppp009s ac adapter 18.5v dc 3.5a 65w -(+)- 1.7x4.7mm 100-240v.ault cs240pwrsup ac adapter 7.5vdc 260ma used 9.0vac 250ma,blocking or jamming radio signals is illegal in most countries.finecom ac adpter 9vdc 4a 100-240vac new.aw17-3r3-u ac adapter 3.3vdc 5a used 1.8x5.5x9.7mm straight.the components of this system are extremely accurately calibrated so that it is principally possible to exclude individual channels from jamming,ktec ksafc0500150w1us ac adapter 5vdc 1.5a -(+) 2.1x5.5mm used c,ibm 02k6756 ac adapter 16vdc 4.5a 2.5x5.5mm -(+) 100-240vac powe,it is a device that transmit signal on the same frequency at which the gsm system operates,sony ericsson cst-75 ac adapter 4.9vdc 700ma used cell phone uk,fujitsu sq2n80w19p-01 ac adapter 19v 4.22a used 2.6 x 5.4 x 111.,hk-b518-a24 ac adapter 12vdc 1a -(+)- ite power supply 0-1.0a,noise generator are used to test signals for measuring noise figure.replacement pa-1750-09 ac adapter 19vdc 3.95a used -(+) 2.5x5.5x,thomson 5-2752 telephone recharge cradle with 7.5v 150ma adapter,kingshen mobile network jammer 16 bands highp power 38w adjustable desktop jammer ₹29.9 v block battery or external adapter,canon cb-2ls battery charger 4.2v dc 0.5a used digital camera s1,thus it can eliminate the health risk of non-stop jamming radio waves to human bodies,all these project ideas would give good knowledge on how to do the projects in the final year.in case of failure of power supply alternative methods were used such as generators.this project shows the controlling of bldc motor using a microcontroller.aopen a10p1-05mp ac adapter 22v 745ma i.t.e power supply for gps,ktec ksaa0500080w1eu ac adapter 5vdc 0.8a used -(+)- 1.5 x 3.5 x,dv-1220dc ac adapter 9v 300ma power supply,none reports/minutes 7 - 15 1.targus apa32ca ac adapter 19.5vdc 4.61a used -(+) 1.6x5.5x11.4mm,automatic power switching from 100 to 240 vac 50/60 hz,condor dv-1611a ac adapter 16v 1.1a used 3.5mm mono jack.shun shing dc12500f ac adapter 12vdc 500ma used -(+) 2x5.5x8mm r.dve dv-0920acs ac adapter 9vac 200ma used 1.2x3.6mm plug-in clas,southwestern bell 9a200u-28 ac adapter 9vac 200ma 90° right angl,eng 3a-302da18 ac adapter 20vdc 1.5a new 2.5x5.5mm -(+) 100-240v.viewsonic adp-60wb ac adapter 12vdc 5a used -(+)- 3 x6.5mm power.

Starcom cnr1 ac dc adapter 5v 1a usb charger,intermatic dt 17 ac adapter 15amp 500w used 7-day digital progra,jentec ah3612-y ac adapter 12v 2.1a 1.1x3.5mm power supply,milwaukee 48-59-1808 rapid 18v battery charger used genuine m12,this system considers two factors.preventively placed or rapidly mounted in the operational area.delta ga240pe1-00 ac ddapter 19.5vdc 12.3a used 5x7.4mm dell j21,power amplifier and antenna connectors,dve dvr-0920ac-3508 ac adapter 9vac 200ma used 1.1x3.8x5.9mm rou,sony ac-940 ac adapter 9vdc 600ma used +(-) 2x5.5x9mm round barr,a prerequisite is a properly working original hand-held transmitter so that duplication from the original is possible,the operational block of the jamming system is divided into two section,.

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