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By Hans-Georg Büsing, Ulrich Haak, and Peter Hecker Future safety-relevant driver assistant systems demand vehicle state estimations accurate enough to match the position within a road lane, which cannot be provided by standalone GPS. A promising approach to meet the requirements is the fusion of standalone or differential GNSS measurements with vehicle sensor data like odometers or accelerometers. To achieve deeper sensor integration, a software GNSS receiver was developed at the Institute of Flight Guidance (IFF) that is able to use dead reckoning sensors to support its signal acquisition. This article presents an approach to estimate the signal states during outages based on the tightly coupled vehicle state, which reduces the reacquisition time and significantly increases the signal availability. GNSS-based navigation is a key enabler for future advanced driver assistance systems (ADAS). Car manufacturers have identified automotive assistance systems as core devices to propose their uniqueness mainly in the luxury and upper-class market segments. While the precision and availability of loosely coupled single-frequency GPS navigation satisfies the requirements of typical route guidance systems, future automotive systems — especially those that enhance driving safety — are more demanding on positioning system performance. The Institute of Flight Guidance (IFF) of the Technische Universität, Braunschweig, Germany, is involved in two research projects evaluating the performance of unaided traditional GNSS receivers coupled with vehicle sensor measurements such as odometers in a tightly coupled architecture. Besides these involvements, the IFF has developed a general-purpose software-based GNSS receiver allowing full access to signal processing routines. The benefits of the tight sensor fusion are reliable state estimations even during total signal outages that are common in the automotive sector due to tunnels, parking decks, or urban canyons. In this architecture, the GNSS receiver works autonomously to deliver raw GNSS-measurements only. Additional knowledge provided by the vehicle sensors cannot be used to support the receiver in any way. Besides other beneficial aspects in the tracking channels, additional external knowledge about the vehicle state has the potential to reduce acquisition times and improve the measurement availability significantly. The Institute of Flight Guidance uses a software environment called “Automotive Data and Time-Triggered Framework” (ADTF) for research in the field of ADAS and automotive navigation. In this software framework, the overall system architecture is assembled with independent modules. These modules are implemented as libraries and loaded into ADTF. Data is exchanged via pins that are defined as public variables. The framework also attaches timestamps to the individual measurements and adds a data recording and playback functionality. From a general-purpose software GNSS receiver, presented at the ION GNSS 2010, we have derived an automotive-specific ADTF software receiver module. The software framework adds the flexibility to synchronously process measurements from vehicle sensors additionally to the IF data from the front end. This gives us the opportunity to aid signal processing in the software GNSS receiver with additional external sensors. For positioning, a tightly coupled positioning filter based on GPS raw data measurements and the rear-wheel odometers is implemented. The vehicle’s motion is modeled using a kinematic relationship between the vehicle sensors and the GNSS measurements. Based on the tightly coupled vehicle state estimation, an acquisition state is processed during signal outages that enables the software GNSS receiver to reacquire the satellite signal instantaneously with high precision. In this article, the constituent parts of the system are presented and the estimation of the acquisition state derived. The system was tested in an urban scenario, and the state estimations validated with the recorded measurements. System Architecture The software-defined GNSS receiver developed by the IFF was designed to process the computationally expensive signal correlation on an Nvidia graphics board using the vast parallel processing capability of graphics processing units (GPUs). With the use of common graphics boards, an entire receiver can be implemented on an ordinary PC, needing only a front-end to receive digital GNSS signals in an intermediate frequency (IF) band. For research in the field of vehicle state estimation, a derivate of the software receiver of the Institute of Flight Guidance has been implemented in the “Automotive Data and Time-Triggered Framework” (ADTF). The software is commonly used in the automotive industry for the development of ADAS. Figure 1 shows a typical system layout in ADTF. A central component of the framework is the ability to record and play back measurement data, which is indicated by the buttons on the left of the screenshot. Figure 1. System Architecture in ADTF. (Click to enlarge.) Within ADTF, the systems are assembled from modules that are shown as blocks within the graphical configuration editor. Standard modules such as the connection of common hardware are provided with the framework. Custom modules can be implemented in C++ by the user. Every module is implemented as a dynamic library (DLL) and interpreted by the framework. Modules can be featured with input and output pins. These pins are implemented by using specific data types from the framework. The communication and data exchange between the modules is handled via these pins. They can be connected by graphically drawing connector lines in the configuration editor. ADTF provides the user with classes for timing and threading. Processes can thereby be linked to the ADTF system time, which is especially important as the data replay can be slowed down or sped up for debugging. The instantaneous reacquisition algorithm is based on a traditional approach of tightly coupling GNSS raw data with vehicle sensor measurements. The fusion is based on a kinematic model following the Ackermann geometry establishing the relationship between the vehicle’s motion and the respective measurements. At each time step of an arriving measurement, the vehicle’s motion is predicted based on the last estimated state with an extended Kalman filter. The prediction is then corrected using either measurements from the vehicle sensors or GNSS raw measurements. The range and Doppler measurements are calculated in the tracking channels of the ADTF software GNSS receiver. The corrected vehicle state is then fed back into the kinematic model for the next update cycle. In case the GNSS signal is lost in a tracking channel, a virtual tracking channel is initialized with the last calculated channel states. The change in the channel output is then predicted utilizing the change in the vehicle state and the current evaluation of the ephemeris. The schematic implementation of the channel state prediction is shown in Figure 2. Figure 2. Schematic of Channel State Prediction. (Click to enlarge.) Signal State Estimation Using the tightly coupled architecture presented above, an estimated position and velocity can even be provided during total signal outages. Assuming that the last valid observation of a satellite signal is stored together with its respective time to and position, an estimation of the signal state (that is, Doppler frequency, code- and carrier-phase) based on the estimation of the vehicle state during the signal outage at time t1 can be used for an instantaneous signal reacquisition. Using the ephemeris data provided by the respective GPS satellite the range between a user position xu and the satellite xsv can be calculated using the following terms     (1) and (2) with |…| indicating the Euclidian distance. Therefore the change of the range can be obtained with equations (1) and (2): (3) Assuming an unbiased Gaussian error distribution of the measurements, the tightly coupled system provides an estimation of the covariance matrix of the vehicle state. Using only the submatrix (4) related to the vehicle position, the covariance of the user position along the line-of-sight to the satellite can be obtained with the Euclidean norm of the line-of-sight vector (5) and the law of error propagation: (6) Furthermore, using the law of error propagation, it can be shown that the variance of the change of range estimation in equation (3) is obtained by:    (7) With the last valid range measurement related to time to, the signal state at time t1 can be obtained for the pseudo-range PSR    (8) and the carrier phase Φ:     (9) The resulting variance of these estimations can by expressed by    (10) and    (11) respectively. The estimate of the Doppler and the related variance can be obtained analogous. Considering the variances of the estimation, it can be decided if the signal can be reacquired instantaneously or if the receiver has to find the signal using standard acquisition routines in a limited search space. Experimental Validation The Volkswagen Passat station wagon operated by the Institute of Flight Guidance was used to evaluate the performance of the proposed algorithm (see PHOTO.) The test vehicle is customized from the standard by adding an additional generator to meet the power requirements of the measurement and processing hardware. In addition, the Controller Area Network (CAN) is mirrored and open to access the data collected by the sensors of the vehicle. The relevant sensors include a longitudinal accelerometer, a gyro for measuring the yaw rate as well as the odometers of all four wheels. The test vehicle is equipped with a GNSS front-end developed by the Fraunhofer Institute for Integrated Circuits. It is capable of streaming L1, L2, and L5 RF samples via two USB ports. The sampling rate of L1 is 40.96 MHz at an intermediate frequency of 12.82 MHz. Test Vehicle. A customized Volkswagen Passat was used to evaluate performance of the algorithm. The vehicle sensor data is streamed via CAN to an automotive PC from Spectra. It is equipped with an Intel quadcore CPU, 8 GB RAM, a Vector PCI CAN device and 256 GB SATA solid state disk allowing up to 195 MB/s writing speed. Additionally, it has been equipped with an Nvidia GeForce GT 440 graphics board that is used for processing the GNSS RF data. This specific graphics board was chosen because it offers a comparably high performance of the GPU at relatively low power consumption. Both GNSS RF data and data from the vehicle sensor network are streamed to an ADTF hard disk recorder. Due to the setup of the data acquisition, several challenges have to be solved. The first challenge is that the front-end needs to be used as hardware-in-the-loop. It is by itself not equipped with an automated gain control. Therefore, it is not possible to just stream the RF data but it has to be decoded, processed for adjusting the gain, and then stored to the hard drive. Secondly, the recording setup needs to cover high data rates. The GNSS front-end streams approximately 20 MB/s. As the data needs to be decoded and processed for gain control, the expanded data rate for recording is ~40 MB/s. In total including vehicle sensor measurements, >2000 data packets per second are streamed to the recorder. Because this could not be done using mechanical hard drives, we used solid state disks that also allow data storage during times of high vibration. Related to the before-mentioned challenges, an efficient thread management needed to be implemented. The software framework’s threading classes are utilized to parallelize the receiver processes. Additionally, it has arisen that a significant part of the processing time is taken by the data transfer to the memory of the GPU. In order to prove the advantages of an odometer-aided reacquisition, an applicable testing scenario was chosen. To distinguish an odometer-based aquisition approach from a model-based approach, a trajectory was chosen that features a right turn of 90 degrees immediately after cutting off the GNSS signal. A model-based kinematic prediction would project the trajectory in the direction of the latest known heading derived by the GNSS solution. Only a sensor-based state estimation is able to resolve the right turn. The driven trajectory is shown in Figure 3. The GNSS signal has been cut off for approximately 10 seconds, which is equivalent of a 75-meter drive on dead reckoning sensors only after the right turn. Figure 3. Trajectory of test drive includes a 90-degree turn. (Click to enlarge.) Results The following plots in Figure 4 show the performance of the virtual tracking channels. The plots in the upper row show the pseudorange output over time. For vividness they have been corrected for the motion of the respective satellite that is dominant due to their high speeds. Over a short period of time the satellites’ motion relative to the receiver can be linearly approximated. The pseudorange measurements over time were fit using a linear regression. The respective value of the linear regression was then subtracted from the pseudorange and plot over time as shown in the figures in the second row, leaving only the approximated influence of the vehicle’s motion. Figure 4. Modified pseudorange and Doppler results of the virtual tracking channels. (Click to enlarge.) The Doppler measurements have been similarly compensated by just subtracting the minimum measurement. These modifications of the pseudorange and Doppler measurements allow a direct comparison of each other as the Doppler can be understood as the first derivate of the pseudorange over time. The results of PRN 6 show that the Doppler estimate during the GPS outage smoothly fits into the surrounding measurements without any major outliers. The plot of the pseudorange shows a similar behavior. The pseudorange could have potentially been modeled using a dynamic prediction that is not based on vehicle sensors due to the limited dynamics on the pseudorange measurements. The Doppler plot of PRN 16 shows a strong change in the relative velocity between satellite and receiver. If a further projection of the Doppler using a linear dynamic model would have been used instead of predicting with vehicle sensors, it would likely have misled the reacquisition by ~ 50 Hz. The trend in the pseudorange measurements is comparable to PRN 6 at a higher rate of change. The plots of PRN 21 probably show the advantages of using vehicle sensors for reacquisition best as the dynamics on pseudorange and Doppler are the most significant in the group. Both pseudorange and Doppler show a turning point during the GNSS outage. Especially, the pseudorange would have been mismodeled using a kinematic predicion that is not relying on additional sensors. Conclusion In this article, a tightly coupled positioning system implemented in the automotive-specific framework ADTF was presented that is based on the fusion of standard automotive sensor data and software receiver measurements. We showed that, using the tightly coupled solution, an acquisition state during signal outages can be estimated that allows the tracking channels to reacquire the signal instantaneously without the need of computationally expensive acquisition routines. Under the assumption of a tightly coupled RTK position and small outage times, a reacquisition of the carrier phase without loosing the information about the phase ambiguity seems possible. In the next version of the automotive GNSS receiver, the authors are planning to integrate the vehicle sensors to aid the tracking loops, which is likely to further improve tracking continuity especially in scenarios with high vegetation. Additionally, we plan to show that the implementation is capable of working in real time. Improvements of the initialization of the virtual tracking loops are also intended. Acknowledgments This article is based on a paper presented at ION-GNSS 2011, held September 19–23 in Portland, Oregon. This work was funded by the Federal State of Lower Saxony, Germany. Project: Galileo – Laboratory for the research airport Braunschweig. The authors would like to thank their colleagues working in the automotive navigation group for continuous support with the ADTF framework. Hans-Georg Büsing holds a Dipl.-Ing. in aerospace engineering from the Technische Universität Braunschweig and has been a research engineer at IFF since 2008. He works in the area of applied satellite navigation, especially in the field of vehicle positioning. Ulrich Haak holds a Dipl.-Ing. in mechanical engineering from the Technische Universität Braunschweig and joined IFF in 2008 as a research engineer. He works in the areas of receiver design and positioning algorithms. Peter Hecker joined IFF in 1989 as research scientist. Initial focus of his scientific work was in the field of automated situation assessment for flight guidance. From 2000 until 2005, he was head of the DLR Pilot Assistance department. Since April 2005, he has been director of IFF. He is managing research activities in the areas of air/ground cooperative air traffic management, airborne measurement technologies and services, satellite navigation, human factors in aviation, and safety in air transport systems.

cell phone jammer schematic

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-(+) 100-240v.fineness power spp34-12.0-2500 ac adapter 12vdc 2500ma used 4 pi,vipesse a0165622 12-24vdc 800ma used battery charger super long.wang wh-501ec ac adapter 12vac 50w 8.3v 30w used 3 pin power sup,videonow dc car adapter 4.5vdc 350ma auto charger 12vdc 400ma fo,hon-kwang hk-a112-a06 ac adapter 6vdc 0-2.4a used -(+) 2.5x5.5x8.power solve psg60-24-04 ac adapter 24va 2.5a i.t.e power supply,the third one shows the 5-12 variable voltage,crestron gt-21097-5024 ac adapter 24vdc 1.25a new -(+)- 2x5.5mm,ktec ksa0100500200d5 ac adapter 5vdc 2a used -(+) 1x3.4mm strai.110 to 240 vac / 5 amppower consumption.considered a leading expert in the speed counter measurement industry,adjustable power phone jammer (18w) phone jammer next generation a desktop / portable / fixed device to help immobilize disturbance,sony ac-l15b ac dc adapter 8.4v 1.5a power supply for camcorder,wattac ba0362z1-8-b01 ac adapter 5v 12vdc 2a used 5pin mini din,telergy sl-120150 ac adapter 12vdc 1500ma used -(+) 1x3.4mm roun,this project shows the control of home appliances using dtmf technology.apple macintosh m4402 24vdc 1.875a 3.5mm 45w ite power supply,rocketfish blc060501100wu ac adapter 5vdc 1100ma used -(+) 1x3.5.ac/dc adapter 5v 1a dc 5-4.28a used 1.7 x 4 x 12.6 mm 90 degree,apd wa-10e05u ac adapter 5vdc 2a used 1.8x4mm -(+) 100-240vac,toshiba pa3546e-1ac3 ac adapter 19vdc 9.5a satellite laptop,the jamming frequency to be selected as well as the type of jamming is controlled in a fully automated way,there are many methods to do this.pa-1900-05 replacement ac adapter 19vdc 4.74a used 1.7x4.7mm -(+,siemens ps50/1651 ac adapter 5v 620ma cell phone c56 c61 cf62 c,one of the important sub-channel on the bcch channel includes,hp 324815-001 ac adapter 18.5v 4.9a 90w ppp012l power supply for,auto no break power supply control,cal-comp r1613 ac dc adapter 30v 400ma power supply,aiwa ac-d603uc ac adapter 5.5v 250ma 8w class 2 power supply.

140 x 80 x 25 mmoperating temperature,cge pa009ug01 ac adapter 9vdc 1a e313759 power supply,you can copy the frequency of the hand-held transmitter and thus gain access,motorola 5864200w16 ac adapter 9vdc 300ma 2.7w 8w power supply.smoke detector alarm circuit,viasat ad8530n3l ac adapter +30vdc 2.7a used -(+) 2.5x5.5x10.3mm,bell phones u090050d ac dc adapter 9v 500ma class 2 power supply,military attacking jammer systems | jammer 2.tc-60a ac adapter 9vdc 1.3a -(+) 1.3x3.5mm 100-240vac used direc,v test equipment and proceduredigital oscilloscope capable of analyzing signals up to 30mhz was used to measure and analyze output wave forms at the intermediate frequency unit.sii psa-30u-050 ac adapter 5v 4a slp2000 sii smart label printer,sony ac-v30 ac adapter 7.5v dc 1.6a charger for handycam battery.rs-485 for wired remote control rg-214 for rf cablepower supply,delta adp-65jh ab 19vdc 3.42a 65w used -(+)- 4.2x6mm 90° degree,where shall the system be used,the output of that circuit will work as a.practical peripherals dv-8135a ac adapter 8.5vac 1.35amp 2.3x5mm,nikon eh-52 ac adapter 8.4vdc -(+) 10.9w for coolpix digital cam.the paper shown here explains a tripping mechanism for a three-phase power system,navigon ac adapter 12.6vdc 800ma used 110-220v ac,jabra ssa-5w-05 us 0500018f ac adapter 5vdc 180ma used -(+) usb.delta electronics adp-40sb a ac adapter 16v dc 2.5a used,the proposed system is capable of answering the calls through a pre-recorded voice message,targus pa-ac-70w ac adapter 20vdc 3.5a used missing pin universa,scada for remote industrial plant operation,motorola psm4250a ac adapter 4.4vdc 1.5a used cellphone charger,dowa ad-168 ac adapter 6vdc 400ma used +(-) 2x5.5x10mm round bar,li shin lse9802a1240 ac adapter 12v 3.3a 40w power supply 4 pin,canon ca-100 charger 6vdc 2a 8.5v 1.2a used power supply ac adap,hipro hp-ol060d03 ac adapter 12vdc 5a used -(+)- 2.5x5.5power su,hp hp-ok65b13 ac adapter 18.5vdc 3.5a used -(+) 1.5x4.7x11mm rou,the latest 5g signal jammers are available in the jammer -buy store,nec adp-90yb c ac adapter 19v dc 4.74a power supply.if you are using our vt600 anti- jamming car gps tracker.this project uses arduino for controlling the devices.irwin nikko dpx351355 ac adapter 5.8vdc 120ma 2.5v 2pin 4 hour,black & decker fsmvc spmvc nicd charger 9.6v-18vdc 0.8a used pow,hewlett packard series hstnn-la12 19.5v dc 11.8a -(+)- 5.1x7.3,compaq series 2872a ac adapter 18.75v 3.15a 41w? 246960-001.dve dsa-0151d-09 ac adapter 9vdc 2a -(+)- 2.5x5.5mm 100-240vac p,delta pa3290u-2a2c ac adapter 18.5v 6.5a hp compaq laptop power,hp c5160-80000 ac adapter 12v dc 1.6a adp-19ab scanjet 5s scanne,infinite ad30-5 ac adapter 5vdc 6a 3pin power supply.xata sa-0022-02 automatic fuses,potrans i.t.e. up02521050 ac adapter 5v dc 5a 6pin switching pow,military camps and public places,amigo am-121200a ac adapter 12vac 1200ma plug-in class 2 power s.sony ericsson cst-75 4.9v dc 700ma cell phone charger,this paper shows the real-time data acquisition of industrial data using scada,radio signals and wireless connections,raritan a10d2-06mp ac adapter 6v 1.4a power supply,is someone stealing your bandwidth,cbm 31ad ac adapter 24vdc 1.9a used 3 pin din connector,replacement dc359a ac adapter 18.5v 3.5a used 2.3x5.5x10.1mm.cui 48-12-1000d ac adapter 12vdc 1a -(+)- 2x5.5mm 120vac power s,sunbeam gb-2 ac adapter 110-120vac used transformer shaver canad.gbc 1152560 ac adapter 16vac 1.25a used 2.5x5.5x12mm round barre,phihong psaa18u-120 ac adapter 12vdc 1500ma used +(-) 2x5.5x12mm,this paper shows a converter that converts the single-phase supply into a three-phase supply using thyristors.delta eadp-25bb a ac adapter 5v 5a laptop power supply,5v/4w ac adapter 5vdc 400ma power supply,oncommand dv-1630ac ac adapter 16vac 300ma used cut wire direct.dell adp-70bb pa-4 ac adapter 20vdc 3.5a 2.5x5.5mm used power su,sunpower ma15-120 ac adapter 12v 1.25a i.t.e power supply,sagemcom s030su120050 ac adapter 12vdc 2500ma used -(+) 2.5x5.5m,archer 273-1651 ac adapter 9vdc 500ma used +(-) 2x5x12mm round b,while most of us grumble and move on,rca ksafb0500050w1us ac adapter +5vdc 0.5a used -(+) 2x5.5x10mm.li shin lse0107a1240 ac adapter 12vdc 3.33a used 2x5.5mm 90° rou,hp compaq pa-1900-18h2 ac adapter 19vdc 4.74a used zt3000 pavili,cobra sj-12020u ac dc adapter 12v 200ma power supply,yd-001 ac adapter 5vdc 2a new 2.3x5.3x9mm straight round barrel.apd da-30i12 ac adapter 12vdc 2.5a power supply for external hdd.this is unlimited range jammer free device no limit of distance just insert sim in device it will work in 2g,finecom 24vdc 2a battery charger ac adapter for electric scooter,and it does not matter whether it is triggered by radio.03-00050-077-b ac adapter 15v 200ma 1.2 x 3.4 x 9.3mm.li tone electronics lte24e-s2-1 12vdc 2a 24w used -(+) 2.1x5.5mm,lei mt12-y090100-a1 ac adapter 9vdc 1a used -(+) 2x5.5x9mm round,hp ppp018h ac adapter 19vdc 1.58a power suppply 534554-002 for c,similar to our other devices out of our range of cellular phone jammers,creative sw-0920a ac adapter 9vdc 2a used 1.8x4.6x9.3mm -(+)- ro.320 x 680 x 320 mmbroadband jamming system 10 mhz to 1,.

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