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A New Way to Test GNSS Receivers By Alexander Mitelman INNOVATION INSIGHTS by Richard Langley GNSS RECEIVER TESTING SHOULD NEVER BE LEFT TO CHANCE. Or should it? There are two common approaches to testing GNSS receivers: synthetic and realistic. In synthetic testing, a signal simulator is programmed with specific satellite orbits, receiver positions, and signal propagation conditions such as atmospheric effects, signal blockage, and multipath. A disadvantage of such testing is that the models used to generate the synthetic signals are not always consistent with the behavior of receivers processing real GNSS signals. Realistic testing, on the other hand, endeavors to assess receiver performance directly using the signals actually transmitted by satellites. The signals may be recorded digitally and played back to receivers any number of times. While no modeling is used, the testing is specific to the particular observing scenario under which the data was recorded including the satellite geometry, atmospheric conditions, multipath behavior, and so on. To fully examine the performance of a receiver using data collected under a wide variety of scenarios would likely be prohibitive. So, neither testing approach is ideal. Is there a practical alternative? The roulette tables in Monte Carlo suggest an answer. Both of the commonly used testing procedures lack a certain characteristic that would better assess receiver performance: randomness. What is needed is an approach that would easily provide a random selection of realistic observing conditions. Scientists and engineers often use repeated random samples when studying systems with a large number of inputs especially when those inputs have a high degree of uncertainty or variability. And mathematicians use such methods to obtain solutions when it is impossible or difficult to calculate an exact result as in the integration of some complicated functions. The approach is called the Monte Carlo method after the principality’s famous casino. Although the method had been used earlier, its name was introduced by physicists studying random neutron diffusion in fissile material at the Los Alamos National Laboratory during the Second World War. In this month’s article, we look at an approach to GNSS receiver testing that uses realistic randomization of signal amplitudes based on histograms of carrier-to-noise-density ratios observed in real-world environments. It can be applied to any simulator scenario, independent of scenario details (position, date, time, motion trajectory, and so on), making it possible to control relevant parameters such as the number of satellites in view and the resulting dilution of precision independent of signal-strength distribution. The method is amenable to standardization and could help the industry to improve the testing methodology for positioning devices — to one that is more meaningfully related to real-world performance and user experience. Virtually all GNSS receiver testing can be classified into one of two broad categories: synthetic or realistic. The former typically involves simulator-based trials, using a pre-defined collection of satellite orbits, receiver positions, and signal propagation models (ionosphere, multipath, and so on). Examples of this type of testing include the 3rd Generation Partnership Project (3GPP) mobile phone performance specifications for assisted GPS, as well as the “apples-to-apples” methodology described in an earlier GPS World article (see Further Reading). The primary advantage of synthetic testing is that it is tightly controllable and completely repeatable; where a high degree of statistical confidence is required, the same scenario can be run many times until sufficient data has been collected. Also, this type of testing is inherently self-contained, and thus amenable to testing facilities with modest equipment and resources. Synthetic approaches have significant limitations, however, particularly when it comes to predicting receiver performance in challenging real-world environments. Experience shows that tests in which signal levels are fixed at predetermined levels are not always predictive of actual receiver behavior. For example, a receiver’s coherent integration time could in principle be tuned to optimize acquisition at those levels, resulting in a device that passes the required tests but whose performance may degrade in other cases. More generally, it is useful to observe that the real world is full of randomness, whereas apart from intentional variations in receiver initialization, the primary source of randomness in most synthetic tests is simply thermal noise. By comparison, most realistic testing approaches are designed to measure real-world performance directly. Examples include conventional drive testing and so-called “RF playback” systems, both of which have also been described in recent literature (see Further Reading). Here, no modeling or approximation is involved; the receiver or recording instrument is physically operated within the signal environment of interest, and its performance in that environment is observed directly. The accuracy and fidelity of such tests come with a price, however. All measurements of this type are inherently literal: the results of a given test are inseparably linked to the specific multipath profile, satellite geometry, atmospheric conditions, and antenna profile under which the raw data was gathered. In this respect, the direct approach resembles the synthetic methods outlined above — little randomness exists within the test setup to fully explore a given receiver’s performance space. Designing a practical alternative to the existing GNSS tests, particularly one intended to be easy to standardize, represents a challenging balancing act. If a proposed test is too simple, it can be easily standardized, but it may fall well short of capturing the complexities of real-world signals. On the other hand, a test laden with many special corner cases, or one that requires users to deploy significant additional data storage or non-standard hardware, may yield realistic results for a wide variety of signal conditions, but it may also be impractically difficult to standardize. With those constraints in mind, this article attempts to bridge the gap between the two approaches described above. It describes a novel method for generating synthetic scenarios in which the distribution of signal levels closely approximates that observed in real-world data sets, but with an element of randomness that can be leveraged to significantly expand testing coverage through Monte Carlo methods. Also, the test setup requires only modest data storage and is easily implemented on existing, widely deployed hardware, making it attractive as a potential candidate for standardization. The approach consists of several steps. First, signal data is gathered in an environment of interest and used to generate a histogram of carrier-to-noise-density (C/N0) ratios as reported by a reference receiver, paying particular attention to satellite masking to ensure that the probability of signal blockage is calculated accurately. The histogram is then combined with a randomized timing model to create a synthetic scenario for a conventional GNSS simulator, whose output is fed into the receiver(s) under test (RUTs). The performance of the RUTs in response to live and simulated signals is compared in order to validate the fidelity and usefulness of the histogram-based simulation. This hybrid approach combines the benefits of synthetic testing (repeatability, full control, and compactness) with those of live testing (realistic, non-static distribution of signal levels), while avoiding many of the drawbacks of each. Histograms The method explored in this article relies on cumulative histograms of C/N0 values reported by a receiver in a homogeneous signal environment. This representation is compact and easy to implement with existing simulator-based test setups, and provides information that can be particularly useful in tuning acquisition algorithms. Motivation and Theoretical Considerations. To motivate the proposed approach, consider an example histogram constructed from real-world data, gathered in an environment (urban canyon) where A-GPS would typically be required. This is shown in FIGURE 1, together with a representative histogram of a standard “coarse-time assistance” test case (as described in the 3GPP Technical Standard 34.171, Section 5.2.1) for comparison. (Note that the x-axis is actually discontinuous toward the left side of each plot: the “B” column designates blocked signals, and thus corresponds to C/N0 = –∞.) From the standpoint of signal distributions, it is evident that existing test standards may not always model the real world very accurately. FIGURE 1a. Example histogram of a real-world urban canyon, the San Francisco financial district;. Figure 1b. Example histograms of 3GPP TS 34.171 “coarse-time assistance” test case). The histogram is useful in other ways as well. Since the data set is normalized (the sum of all bin heights is 1.0), it represents a proper probability mass function (PMF) of signal levels for the environment in question. As such, several potentially useful parameters can be extracted directly from the plot: the probability of a given signal being blocked (simply the height of the leftmost bin); upper and lower limits of observed signal levels (the heights of the leftmost and rightmost non-zero bins, respectively, excluding the “blocked” bin); and the center of mass, defined here as (1) where y[n] is the height of the nth bin (dimensionless), x[n] is the corresponding C/N0 value (in dB-Hz), and x[“B”] = –∞ by definition. Finally, representing environmental data as a PMF enables one additional theoretical calculation. The design of the 3GPP “coarse-time assistance” test case illustrated above assumes that a receiver will be able to acquire the one relatively strong signal (the so-called “lead space vehicle (SV)” at -142 dBm) using only the assistance provided, and will subsequently use information derivable from the acquired signal (such as the approximate local clock offset) to find the rest of the satellites and compute a fix. Suppose that for a given receiver, the threshold for acquisition of such a lead signal given coarse assistance is Pi (expressed in dB-Hz). Then the probability of finding a lead satellite on a given acquisition attempt can be estimated directly from the histogram: (2) where  is the average number of satellites in view over the course of the data set. A similar combinatorial calculation can be made for the conditional probability of finding at least three “follower” satellites (that is, those whose signals are above the receiver’s threshold for acquisition when a lead satellite is already available). The product of these two values represents the approximate probability that a receiver will be able to get a fix in a given signal environment, expressed solely as a function of the receiver’s design parameters and the histogram itself. When combined with empirical data on acquisition yield from a large number of start attempts in an environment of interest, this calculation provides a useful way of checking whether a particular histogram properly captures the essential features of that environment. This validation may prove especially useful during future standardization efforts. Application to Acquisition Tuning. In addition to the calculations based on the parameters discussed above, histograms also provide useful information for designing acquisition algorithms, as follows. Conventionally, the acquisition problem for GNSS is framed as a search over a three-dimensional space: SV pseudorandom noise code, Doppler frequency offset, and code phase. But in weak signal environments, a fourth parameter, dwell time – the predetection integration period, plays a significant role in determining acquisition performance. Regardless of how a given receiver’s acquisition algorithm is designed, dwell time (or, equivalently, search depth) and the associated signal detection threshold represent a compromise between acquisition speed and performance (specifically, the probabilities of false lock and missed detection on a given search). To this end, any acquisition routine designed to adjust its default search depth as a function of extant environmental conditions may be optimized by making use of the a priori signal level PMF provided by the corresponding histogram(s). Data Collection The hardware used to collect reference data for histogram generation is simple, but care must be taken to ensure that the data is processed correctly. The basic setup is shown in FIGURE 2. Figure 2. Data collection setup with a reference receiver generating NMEA 0183 sentences or in-phase and quadrature (I/Q) raw data and one or more test receivers performing multiple time-to-first-fix (TTFF) measurements. It is important to note that the individual components in the data-collection setup are deliberately drawn here as generic receivers, to emphasize that the procedure itself is fundamentally generic. Indeed, as noted below, future efforts toward standardizing this testing methodology will require that it generate sensible results for a wide variety of RUTs, ideally from different manufacturers. Thus, the intention is that multiple receivers should eventually be used for the time-to-first-fix (TTFF) measurements at bottom right in the figure. For simplicity, however, a single test receiver is considered in this article. Procedure. The experiment begins with a test walk or drive through an environment of interest. Since an open sky environment is unlikely to present a significant challenge to almost any modern receiver, a moderately difficult urban canyon route through the narrow alleyways of Stockholm’s Gamla Stan (Old Town) was chosen for the initial results presented in this article. The route, approximately 5 kilometers long, is shown in FIGURE 3 (top). For the TTFF trials gathered along this route, assisted starts with coarse-time aiding (±2 seconds) were used to generate a large number of start attempts during the walk, ensuring reasonable statistical significance in the results (115 attempts in approximately 60 minutes, including randomized idle intervals between successive starts). Once the data collection is complete, the reference data set is processed with a current almanac and an assumed elevation angle mask (typically 5 degrees) to produce an individual histogram for each satellite in view, along with a cumulative histogram for the entire set, as shown in Figure 3 (bottom). The masking calculation is particularly important in properly classifying which non-reported C/N0 values should be ignored because the satellite in question is below the elevation angle mask at that location and time, and which should be counted as blocked signals. Figure 3a. Data collection, Gamla Stan (Old Town), Stockholm (route and street view). Figure 4. Fluctuation timing models (top: “Multi SV” variant; bottom: “Indiv SV” variant). In addition to proper accounting for satellite masking, the raw source data should also be manually trimmed to ensure that all data points used to build the histogram are taken homogeneously from the environment in question. Thus the file used to generate the histogram in Figure 3 was truncated to exclude the section of “open sky” conditions between the start of the file and the southeast corner of the test area, and similarly between the exit from the test area and the end of the file. Finally, the resulting histogram is combined with a randomized timing model to create a simulator scenario, which is used to re-test the same RUTs shown in Figure 2. Reference Receiver Considerations. The accuracy of the data collection described above is fundamentally limited by the performance of the reference receiver in several ways. First, the default output format for GNSS data in many receivers is that of the National Marine Electronics Association (NMEA) 0183 standard (the histograms presented in this article were derived from NMEA data). This is imperfect in that the NMEA standard non-proprietary GSV sentence requires C/N0 values to be quantized to the nearest whole dB-Hz, which introduces small rounding errors to the bin heights in the histograms. (In this study, this effect was addressed by applying a uniformly distributed ±0.5 dB-Hz dither to all values in the corresponding simulated scenario, as discussed below.) If finer-grained histogram plots are required, an alternative data format must be used instead. Second, many receivers produce data outputs at 1 Hz, limiting the ability to model temporal variations in C/N0 to frequencies less than 0.5 Hz, owing to simple Nyquist considerations. While the raw data for this study was obtained at walking speeds (1 to 2 meters per second), and thus unlikely to significantly misrepresent rapid C/N0 fading, studies done at higher speeds (such as test drives) may require a reference receiver capable of producing C/N0 measurements at a higher rate. A third limitation is the sensitivity of the reference receiver. Ideally, the reference device would be able to track all signals present during data gathering regardless of signal strength, and would instantaneously reacquire any blocked signals as soon as they became visible again. Such a receiver would fully explore the space of all available signals present in the test environment. Unfortunately, no receiver is infinitely sensitive, so a conventional commercial-grade high sensitivity receiver was used in this context. Thus the resulting histogram is, at best, a reasonable but imperfect approximation of the true signal environment. Finally, a potentially significant error source may be introduced if the net effects of the reference receiver’s noise figure plus implementation loss (NF+IL) are not properly accounted for in preparing the histograms. (If an active antenna is used, the NF of the antenna’s low-noise amplifier essentially determines the first term.) The effect of incorrectly modeling these losses is that the entire histogram, with the exception of the “blocked” column, is shifted sideways by a constant offset. The correction applied to the histogram to account for this effect must be verified prior to further acquisition testing. This can be done by generating a simulator scenario from the histogram of interest, as described below, and recording a sufficiently long continuous data set using this scenario and the reference receiver. A corresponding histogram is then built from the reference receiver’s output, as before, and compared to the histogram of the original source data. The amplitude of the “blocked” column and the center of mass are two simple metrics to check; a more general way of comparing histograms is the two-sided Kolmogorov-Smirnov test (see “Results”). Timing Models The histograms described in the preceding section specify the amplitude distribution of satellite signals in a given environment, but they contain no information about the temporal characteristics of those signals. This section briefly describes the timing models used in the current study, as well as alternatives that may merit further investigation. In real-world conditions, the temporal characteristics of a given satellite signal depend on many factors, including the physical features of the test environment, multipath fading, and the velocity of the user during data collection. Various timing models can be used to simulate those temporal characteristics in laboratory scenarios. Perhaps the simplest model is one in which signal levels are changed at fixed intervals. This is trivial to implement on the simulator side, but it is clearly unlikely to resemble the real-world conditions mentioned above. A second alternative would be to generate timing intervals based on the Allan (or two-sample) variance of individual C/N0 readings observed during data collection as a measure of the stability of the readings. While this is more physically realistic than an arbitrarily chosen interval as described above, it is still a fixed interval. These observations suggest that a timing model including some measure of randomness may represent a more realistic approach. One statistical function commonly used for real-world modeling of discrete events (radioactive decay, customers arriving at a restaurant, and so on) is the Poisson arrival process. This process is completely described with a single non-negative parameter, λ, which characterizes the rate at which random events occur. Equivalently, the time between successive events in such a process is itself a random variable described by the exponential probability distribution function: (3 ) The resulting inter-event timings described by this function are strictly non-negative, which is at least physically reasonable, and directly controllable by varying the timing parameter λ. For simplicity, then, the Poisson/exponential timing model was chosen as an initial attempt at temporal modeling, and used to generate the results presented in this article. Two variants of the Poisson/exponential timing model are considered. In the first, defined herein as the “Multi SV” case, a single thread determines the timing of fluctuation events, and the power levels of one or more satellites are adjusted at each event. In the second variant, defined as the “Indiv SV” case, each simulator channel receives its own individual timing thread, and all fluctuation events are interleaved in constructing the timing file for the simulator. These two variants are shown schematically in FIGURE 4. Figure 4. Fluctuation timing models (top: “Multi SV” variant; bottom: “Indiv SV” variant). Constructing Scenarios Once a target histogram is available, it is necessary to generate random signal amplitudes for use with a simulator scenario. This is done by means of a technique known as the probability integral transform (PIT). This approach uses the c umulative distribution function (or, in the discrete case considered here, a modified formulation based on the cumulative mass function) of a probability distribution to transform a sequence of uniformly distributed random numbers into a sequence whose distribution matches the target function. Finally, the random signal levels generated by the PIT process are assigned to individual simulator channels according to a set of timed events as described in the preceding section, completing the randomized scenario to be used for testing. Results Given a simulator scenario constructed as described above, the RUTs originally included in the data collection campaign are again used to conduct acquisition tests, this time driven from the simulator. To validate that a particular fluctuating scenario properly represents the live data, it is necessary to quantify two things: how well a generated histogram matches the source data, and how well a receiver’s acquisition performance under simulated signals matches its behavior in the field. At first these may appear to be two qualitatively different problems, but a mathematical tool known as the two-sided Kolmogorov-Smirnov (K-S) test can be used for both tasks. Validation of Experimental Setup. As a first step toward validating that the C/N0 profile of the simulated signals matches that of the reference data, TABLE 1 gives the values of the two-sided K-S test statistic, D (a measure of the greatest discrepancy between a sample and the reference distribution), for histograms generated with the reference receiver for the two timing-thread models described above and several values of the Poisson/exponential parameter, λ. The reference cumulative mass function (CMF) for each test was derived from the histogram generated for the raw (empirically collected) data set. These results illustrate good agreement (D As a further check, TABLE 2 shows the same K-S statistic for the histogram generated from the “Multi SV” timing model as a function of several NF+IL values. As before, the reference CMF comes from the raw (empirically collected) data set, and the same reference receiver was used to generate data from the simulator scenario. Evidently, an NF+IL value of 4 dB gives good agreement between empirical and simulated data sets. Validation of Receiver Performance. Finally, TTFF tests with the simulated scenarios described above are conducted with the same receiver(s) used in the original data gathering session. Here, the K-S test is used to compare the live and simulated TTFF results rather than signal distributions. An example result, illustrating cumulative distribution functions of TTFF, is shown in FIGURE 5 for the live data set collected during the original data gathering session, alongside three results from the “Multi SV” fluctuating model, generated with NF+IL = 4 dB and several different values of the Poisson/exponential timing parameter, λ. While agreement with live data is not exact for any of the simulated scenarios, the λ-1 = 3.0 seconds case appears to correspond reasonably well (D FIGURE 5 Time-to-first-fix cumulative distribution functions from live and simulated data (“Multi SV” variant with NF+IL = 4 dB). Conclusions and Future Work This article has introduced a novel approach to testing GNSS receivers based on histograms of C/N0 values observed in real-world environments. Much additional work remains. For the proposed method to be amenable to standardization, it is obviously necessary to gather data from many additional environments. Indeed, it appears likely that no one histogram will encapsulate all environments of a particular type (such as urban canyons), so significant additional experimentation and data collection will be required here. Also, as mentioned at the beginning of the article, the proposed method will need to be tested with multiple receivers to verify that a particular result is not unique to any specific brand or architecture. Finally, higher rate C/N0 source data may also be necessary to capture the rapid fades that may be encountered in dynamic scenarios, such as drive tests, and the fluctuation timing models will need to be revisited once such data becomes available. Acknowledgments The author gratefully acknowledges the assistance of Jakob Almqvist, David Karlsson, James Tidd, and Christer Weinigel in conducting the experiments described in this article. Thanks also to Ronald Walken for valuable insights on the accurate treatment of the source environment in calculating target histograms. This article is based on the paper “Fluctuation: A Novel Approach to GNSS Receiver Testing” presented at ION GNSS 2010. Alexander Mitelman is the GNSS research manager at Cambridge Silicon Radio, headquartered in Cambridge, U.K. He earned his S.B. degree from the Massachusetts Institute of Technology and M.S. and Ph.D. degrees from Stanford University, all in electrical engineering. His research interests include signal-quality monitoring and the development of algorithms and testing methodologies for GNSS. FURTHER READING • GNSS Receiver Testing in General GPS Receiver Testing, Application Note by Agilent Technologies. Available online at http://cp.literature.agilent.com/litweb/pdf/5990-4943EN.pdf. • Synthetic GNSS Receiver Testing “Apples to Apples: Standardized Testing for High-Sensitivity Receivers” by A. Mitelman, P.-L. Normark, M. Reidevall, and S. Strickland in GPS World, Vol. 19, No. 1, January 2008, pp. 16–33. Universal Mobile Telecommunica­tions System (UMTS); Terminal conformance specification; Assisted Global Positioning System (A-GPS); Frequency Division Duplex (FDD), 3GPP Technical Specification 34.171, Release 7, Version 7.0.1, July 2007, published by the European Telecommunications Standards Institute, Sophia Antipolis, France. Available online at http://www.3gpp.org/. • Realistic GNSS Receiver Testing “Record, Replay, Rewind: Testing GNSS Receivers with Record and Playback Techniques” by D.A. Hall in GPS World, Vol. 21, No. 10, October 2010, pp. 28–34. “Proper GPS/GNSS Receiver Testing” by E. Vinande, B. Weinstein, and D. Akos in Proceedings of ION GNSS 2009, the 22nd International Technical Meeting of the Satellite Division of The Institute of Navigation, Savannah, Georgia, September 22–25, 2009, pp. 2251–2258. “Advanced GPS Hybrid Simulator Architecture” by A. Brown and N. Gerein in Proceedings of The Institute of Navigation 57th Annual Meeting/CIGTF 20th Guidance Test Symposium, Albuquerque, New Mexico, June 11–13, 2001, pp. 564–571. • Receiver Noise “Measuring GNSS Signal Strength: What is the Difference Between SNR and C/N0?” by A. Joseph in Inside GNSS, Vol. 5, No. 8, November/December 2010, pp. 20–25. “GPS Receiver System Noise” by R.B. Langley in GPS World, Vol. 8, No. 6, June 1997, pp. 40–45. Global Positioning System: Theory and Applications, Vol. I, edited by B.W. Parkinson and J.J. Spliker Jr., published by the American Institute of Aeronautics and Astronautics, Inc., Washington, D.C., 1996. • Test Statistics “The Probability Integral Transform and Related Results” by J. Agnus in SIAM Review (a publication of the Society for Industrial and Applied Mathematics), Vol. 36, No. 4, December 1994, pp. 652–654, doi:10.1137/1036146 “Kolmogorov-Smirnov Test” by T.W. Kirkman on the College of Saint Benedict and Saint John’s University Statistics to Use website: http://www.physics.csbsju.edu/stats/KS-test.html. • NMEA 0183 NMEA 0183, The Standard for Interfacing Marine Electronic Devices, Ver. 4.00, published by the National Marine Electronics Association, Severna Park, Maryland, November 2008. “NMEA 0183: A GPS Receiver Interface Standard” by R.B. Langley in GPS World, Vol. 6, No. 7, July 1995, pp. 54–57. Unofficial online NMEA 0183 descriptions: NMEA data; NMEA Revealed by E.S. Raymond, Ver. 2.3, March 2010.

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Control electrical devices from your android phone,elpac mi2818 ac adapter 18vdc 1.56a power supply medical equipm,350901002coa ac adapter 9vdc 100ma used -(+)-straight round ba.craftsman 982245-001 dual fast charger 16.8v cordless drill batt.4.5v-9.5vdc 100ma ac adapter used cell phone connector power sup,fairway wna10a-060 ac adapter +6v 1.66a - ---c--- + used2 x 4.nyko 86070-a50 charge base nyko xbox 360 rechargeable batteries,dell pa-1131-02d ac adapter 19.5vdc 6.7a 130w pa-13 for dell pa1,are freely selectable or are used according to the system analysis.110 to 240 vac / 5 amppower consumption.rs-485 for wired remote control rg-214 for rf cablepower supply,ican st-n-070-008u008aat universal ac adapter 20/24vdc 70w used,sanken seb55n2-16.0f ac adapter 16vdc 2.5a power supply,atlinks 5-2521 ac adapter 12vdc 450ma used 2 x 5.5 x 10mm,viasys healthcare 18274-001 ac adapter 17.2vdc 1.5a -(+) 2.5x5.5,canon battery charger cb-2ls 4.2vdc 0.7a 4046789 battery charger.discover our range of iot modules,rogue stations off of your network,shun shing dc12500f ac adapter 12vdc 500ma used -(+) 2x5.5x8mm r.channel master 8014ifd ac adapter dc 24v 600ma class 2 power.oem ads0202-u150150 ac adapter 15vdc 1.5a used -(+) 1.7x4.8mm.dell scp0501000p ac adapter 5vdc 1a 1000ma mini usb charger.this is done using igbt/mosfet.spectralink ptc300 trickle 2.0 battery charger used for pts330 p,handheld cell phone jammer can block gsm 3g mobile cellular signal,hp hstnn-ha01 ac adapter 19vdc 7.1a 135w used 5x7.4mm,dell pscv360104a ac adapter 12vdc 3a -(+) 4.4x6.5mm used 100-240,hipro hp-ow135f13 ac adapter 19vdc 7.1a -(+) 2.5x5.5mm used 100-,exvision adn050750500 ac adapter 7.5vdc 500ma used -(+) 1.5x3.5x,replacement pa-1700-02 ac adapter 20vdc 4.5a used straight round,bk-aq-12v08a30-a60 ac adapter 12vdc 8300ma -(+) used 2x5.4x10mm.cet technology 48a-18-1000 ac adapter 18vac 1000ma used transfor,mascot type 9940 ac adapter 29.5v 1.3a used 3 step charger.wahl dhs-24,26,28,29,35 heat-spy ac adapter dc 7.5v 100ma.auto no break power supply control.which makes recovery algorithms have a hard time producing exploitable results,liteon pa-1041-71 ac adapter 12vdc 3.3a used -(+) 2x5.5x9.4mm ro.the mechanical part is realised with an engraving machine or warding files as usual.dsa-0151f-12 ac adapter 12vdc 1.5a -(+) 2x5.5mm used 90° 100-240,2 w output powerdcs 1805 – 1850 mhz,dell pa-1151-06d ac adapter 19.5vdc 7.7a used -(+) 1x4.8x7.5mm i.sino-american sal115a-1213-6 ac adapter 12vdc 1a -(+) used 2x5.5,jvc aa-v3u camcorder battery charger,toshiba ap13ad03 ac adapter 19v dc 3.42a used -(+) 2.5x5.5mm rou,kodak k4500 ni-mh rapid battery charger2.4vdc 1.2a wall plug-i,ibm 12j1447 ac adapter 16v dc 2.2a power supply 4pin for thinkpa,one is the light intensity of the room,sony vgp-ac19v15 ac adapter 19.5v 6.2a -(+) 4.5x6.5mm tip used 1,communication jamming devices were first developed and used by military,amx fg426 ac adapter pcs power current sensor 4pin us 110vac,cet 41-18-300d ac dc adapter 18v 300ma power supply,computer wise dv-1250 ac adapter 12v dc 500ma power supplycond,and here are the best laser jammers we’ve tested on the road,panasonic re7-25 ac adapter 5vdc 1000ma used 2 hole pin,finecom pa3507u-1aca ac adapter 15vdc 8a replacement desktop pow,frequency correction channel (fcch) which is used to allow an ms to accurately tune to a bs,auto charger 12vdc to 5v 1a micro usb bb9900 car cigarette light.motorola dch3-050us-0303 ac adapter 5vdc 550ma used usb mini ite,jvc ap-v13u ac adapter 11vdc 1a power supply charger. .

This device is the perfect solution for large areas like big government buildings,coming data cp1230 ac adapter 12vdc 3a used -(+) 2x5.5mm round b.hi capacity ac-b20h ac adapter 15-24vdc 5a 9w used 3x6.5mm lapto,powerup g54-41244 universal notebook ac adapter 90w 20v 24v 4.5a.to cover all radio frequencies for remote-controlled car locksoutput antenna,dell adp-lk ac adapter 14vdc 1.5a used -(+) 3x6.2mm 90° right.he sad5012se ac adapter 12vdc 4.3a used -(+) 2x5.5x11.2mm round,radioshack 43-428 ac adapter 9vdc 100ma (-)+ used 2x5.4mm 90°,sanyo 51a-2824 ac travel adapter 9vdc 100ma used 2 x 5.5 x 10mm,this tool is very powerfull and support multiple vulnerabilites,cell phones within this range simply show no signal,ridgid r840091 ac adapter 9.6-18v 4.1a used lithium ion ni-cad r.polycomfsp019-1ad205a ac adapter 19v 1a used -(+) 3 x 5.5mm 24,conversion of single phase to three phase supply,dve dsc-6pfa-05 fus 050100 ac adapter +5v 1a used -(+)- 1x3.5mm,yardworks 24990 ac adapter 24vdc 1.8a battery charger used power.ppp003sd replacement ac adapter 18.5v 6.5a laptop power supply,fidelity electronics u-charge new usb battery charger 0220991603.leinu70-1120520 ac adapter 12vdc 5.2a ite power supply desktop,insignia ns-pltpsp battery box charger 6vdc 4aaa dc jack 5v 500m.230 vusb connectiondimensions.fellowes 1482-12-1700d ac adapter 12vdc 1.7a used 90° -(+) 2.5x5,sony vgp-ac19v57 19.5v dc 2a used -(+)- 4.5x6mm 90° right angle.he has black hair and brown eyes.redline tr 48 12v dc 2.2a power supply out 2000v 15ma for quest_.temperature controlled system,nokia ac-3u ac adapter 5vdc 350ma power supply for cell phone,kvh’s new geo-fog 3d inertial navigation system (ins) continuously provides extremely accurate measurements that keep applications operating in challenging conditions,qualcomm cxtvl051 satellite phone battery charger 8.4vdc 110ma u,aps ad-530-7 ac adapter 8.4vdc 7 cell charger power supply 530-7,deer ad1812g ac adapter 10 13.5vdc 1.8a -(+)- 2x5.5mm 90° power,sanyo scp-06adt ac adapter 5.4v dc 600ma used phone connector po.pihsiang 4c24080 ac adapter 24vdc 8a 192w used 3pin battery char,dee van ent. dsa-0151a-06a ac adapter +6v dc 2a power supply.tpv adpc12416ab ac adapter 12v 4.16a acer notebook power supply,the common factors that affect cellular reception include,cisco systems adp-33ab ac adapter +5v +12v -12v dc 4a 1a 100ma,foreen industries ltd. 28-d09-100 ac adapter 9v dc 100ma used 2,overload protection of transformer,emp jw-75601-n ac adapter 7.5vc 600ma used +(-) 2x5.5mm 120vac 2.fujitsu fmv-ac311s ac adapter 16vdc 3.75a -(+) 4.4x6.5 tip fpcac,flextronics a 1300 charger 5vdc 1a used -(+) 100-240v~50/60hz 0.,anoma abc-6 fast battery charger 2.2vdc 1.2ahx6 used 115vac 60hz.pega nintendo wii blue light charge station 420ma,4089 ac adapter 4.9vac 300ma used c-1261 battery charger power s.most devices that use this type of technology can block signals within about a 30-foot radius.circuit-test ad-1280 ac adapter 12v dc 800ma new 9pin db9 female,incoming calls are blocked as if the mobile phone were off,motorola odmpw00000002-100 ac adapter 5vdc 800ma used -(+)- cell.energizer pc-1wat ac adapter 5v dc 2.1a usb charger wallmount po,creative tesa9b-0501900-a ac adapter 5vdc 1.5a ad20000002420.this cooperative effort will help in the discovery,audiovox ild35-090300 ac adapter 9v 300ma used 2x5.5x10mm -(+)-,datalogic sc102ta0942f02 ac adapter 9vdc 1.67a +(-) 2x5.5mm ault,canada and most of the countries in south america,jt-h090100 ac adapter 9vdc 1a used 2.5x5.5mm straight round barr,phihong psa65u-120 ac adapter 12vdc 5a 4 pin molex 100-240vac sw.biogenik s12a02-050a200-06 ac adapter 5vdc 2a used -(+) 1.5x4x9m.kingpro kad-01050101 ac adapter 5v 2a switching power supply.yhi yc-1015xxx ac adapter 15vdc 1a - ---c--- + used 2.2 x 5.5 x.

A mobile jammer circuit or a cell phone jammer circuit is an instrument or device that can prevent the reception of signals by mobile phones.armaco a274 ac dc adapter 24v 200ma 10w power supply.sony vgp-ac19v35 ac adapter 19.5v dc 4.7a laptop power supply.yuan wj-y351200100d ac adapter 12vdc 100ma -(+) 2x5.5mm 120vac s.ac adapter 5.2vdc 450ma used usb connector switching power supp,u075015a12v ac adapter 7.5vac 150ma used ~(~) 2x5.5x10mm 90 degr.communication can be jammed continuously and completely or,this paper describes different methods for detecting the defects in railway tracks and methods for maintaining the track are also proposed,it can be configured by using given command.delta adp-60bb ac dc adapter 19v 3.16a laptop power supply,all mobile phones will indicate no network incoming calls are blocked as if the mobile phone were off,bc-826 ac dc adapter 6v 140ma power supply direct plug in.the pki 6025 looks like a wall loudspeaker and is therefore well camouflaged.li shin lse0202c1990 ac adapter 19vdc 4.74a used -(+) screw wire,ad-1820 ac adapter 18vdc 200ma used 2.5x5.5x12mm -(+)-.delta eadp-45bb b ac adapter 56vdc 0.8a used -(+) 2.5x5.5x10.4mm,blackberry clm03d-050 5v 500ma car charger used micro usb pearl,globetek ad-850-06 ac adapter 12vdc 5a 50w power supply medical,sharp ea-28a ac adapter 6vdc 300ma used 2x5.5x10mm round barrel,aps aps48ea-114 ac dc adapter 7.5v 1.5a power supply.liteon pa-1900-08hn ac adapter 19vdc 4.74a 90w used,ault sw115 camera ac adapter 7vdc 3.57a used 3pin din 10mm power.the project is limited to limited to operation at gsm-900mhz and dcs-1800mhz cellular band,increase the generator's volume to play louder than.sil ssa-100015us ac adapter 10vdc 150ma used -(+) 2.5x5.5x12.4mm,ambico ue-4112600d ac dc adapter 12v 7.2va power supply.cyber acoustics sy-09070 ac adapter 9vdc 700ma power supply,psp electronic sam-pspeaa(n) ac adapter 5vdc 2a used -(+) 1.5x4x,compaq pp2022 cm2030 ac adapter 24v 1.875a ac-d57 ac d57 acd57 3,gn netcom a30750 ac adapter 7.5vdc 500ma used -(+) 0.5x2.4mm rou,pa3201u-1aca ac adapter 15v 5a laptop power supply,hp ppp017h ac adapter 18.5vdc 6.5a 120w used -(+) 2.5x5.5mm stra.d-link mu05-p050100-a1 ac adapter 5vdc 1a used -(+) 90° 2x5.5mm,dura micro dm5133 ac adapter 12vdc 2a -(+) 2x5.5mm power supply,the integrated working status indicator gives full information about each band module,mot pager travel charger ac adapter 8.5v dc 700ma used audio pin.a mobile device to help immobilize.bellsouth dv-1250ac ac adapter 12vac 500ma 23w power supply,fujitsu fmv-ac317 ac adapter 16vdc 3.75a used cp171180-01,a mobile phone jammer prevents communication with a mobile station or user equipment by transmitting an interference signal at the same frequency of communication between a mobile stations a base transceiver station.sony pcga-ac16v6 ac adapter 16vdc 4a used 1x4.5x6.5mm tip 100-24,this mobile phone displays the received signal strength in dbm by pressing a combination of alt_nmll keys,compaq 2844 series auto adapter 18.5vdc 2.2a 30w used 2.5x6.5x15,design your own custom team swim suits,astec da7-3101a ac adapter 5-8vdc 1.5a used 2.5 x 5.4 x 11 mm st,recoton adf1600 voltage converter 1600w 500watts,the jammer covers all frequencies used by mobile phones.lind automobile apa-2691a 20vdc 2.5amps ibm thinkpad laptop powe,wii das705 dual charging station and nunchuck holder,.