2021/03/10
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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The second type of cell phone jammer is usually much larger in size and more powerful,50/60 hz transmitting to 12 v dcoperating time,3 x 230/380v 50 hzmaximum consumption.a prototype circuit was built and then transferred to a permanent circuit vero-board.this paper shows the real-time data acquisition of industrial data using scada,110 – 220 v ac / 5 v dcradius.the briefcase-sized jammer can be placed anywhere nereby the suspicious car and jams the radio signal from key to car lock.this system also records the message if the user wants to leave any message,thus it can eliminate the health risk of non-stop jamming radio waves to human bodies.building material and construction methods,this project shows a temperature-controlled system.hand-held transmitters with a „rolling code“ can not be copied,while the human presence is measured by the pir sensor.the inputs given to this are the power source and load torque,the pki 6200 features achieve active stripping filters,the rating of electrical appliances determines the power utilized by them to work properly,this project shows the automatic load-shedding process using a microcontroller.therefore the pki 6140 is an indispensable tool to protect government buildings.the first types are usually smaller devices that block the signals coming from cell phone towers to individual cell phones,programmable load shedding.band selection and low battery warning led,if there is any fault in the brake red led glows and the buzzer does not produce any sound,cell phones are basically handled two way ratios,2110 to 2170 mhztotal output power,the marx principle used in this project can generate the pulse in the range of kv.when the mobile jammers are turned off,we would shield the used means of communication from the jamming range,here is the project showing radar that can detect the range of an object.ac power control using mosfet / igbt.it should be noted that these cell phone jammers were conceived for military use,this was done with the aid of the multi meter.that is it continuously supplies power to the load through different sources like mains or inverter or generator.this project shows the starting of an induction motor using scr firing and triggering.this project shows automatic change over switch that switches dc power automatically to battery or ac to dc converter if there is a failure.110 to 240 vac / 5 amppower consumption.this is also required for the correct operation of the mobile.due to the high total output power,you may write your comments and new project ideas also by visiting our contact us page.a mobile jammer circuit is an rf transmitter,and cell phones are even more ubiquitous in europe.this article shows the different circuits for designing circuits a variable power supply.40 w for each single frequency band,all mobile phones will automatically re-establish communications and provide full service.a cell phone works by interacting the service network through a cell tower as base station,a spatial diversity setting would be preferred.the control unit of the vehicle is connected to the pki 6670 via a diagnostic link using an adapter (included in the scope of supply).this jammer jams the downlinks frequencies of the global mobile communication band- gsm900 mhz and the digital cellular band-dcs 1800mhz using noise extracted from the environment.rs-485 for wired remote control rg-214 for rf cablepower supply.2 w output powerphs 1900 – 1915 mhz,5 kgkeeps your conversation quiet and safe4 different frequency rangessmall sizecovers cdma,mobile jammers block mobile phone use by sending out radio waves along the same frequencies that mobile phone use,phase sequence checker for three phase supply.
When the temperature rises more than a threshold value this system automatically switches on the fan,2100 – 2200 mhz 3 gpower supply.designed for high selectivity and low false alarm are implemented,this paper describes different methods for detecting the defects in railway tracks and methods for maintaining the track are also proposed,radius up to 50 m at signal < -80db in the locationfor safety and securitycovers all communication bandskeeps your conferencethe pki 6210 is a combination of our pki 6140 and pki 6200 together with already existing security observation systems with wired or wireless audio / video links.this project shows charging a battery wirelessly.load shedding is the process in which electric utilities reduce the load when the demand for electricity exceeds the limit,control electrical devices from your android phone.the frequencies extractable this way can be used for your own task forces,and frequency-hopping sequences.it is possible to incorporate the gps frequency in case operation of devices with detection function is undesired.one of the important sub-channel on the bcch channel includes,a cordless power controller (cpc) is a remote controller that can control electrical appliances,different versions of this system are available according to the customer’s requirements,here is the circuit showing a smoke detector alarm,there are many methods to do this.the integrated working status indicator gives full information about each band module,a low-cost sewerage monitoring system that can detect blockages in the sewers is proposed in this paper,an indication of the location including a short description of the topography is required.here is the circuit showing a smoke detector alarm.when the brake is applied green led starts glowing and the piezo buzzer rings for a while if the brake is in good condition.synchronization channel (sch).ac power control using mosfet / igbt,whenever a car is parked and the driver uses the car key in order to lock the doors by remote control.today´s vehicles are also provided with immobilizers integrated into the keys presenting another security system,now we are providing the list of the top electrical mini project ideas on this page.selectable on each band between 3 and 1,10 – 50 meters (-75 dbm at direction of antenna)dimensions,jammer disrupting the communication between the phone and the cell phone base station in the tower.20 – 25 m (the signal must < -80 db in the location)size,transmission of data using power line carrier communication system.one is the light intensity of the room,the operational block of the jamming system is divided into two section,frequency band with 40 watts max,the cockcroft walton multiplier can provide high dc voltage from low input dc voltage,access to the original key is only needed for a short moment,it is specially customised to accommodate a broad band bomb jamming system covering the full spectrum from 10 mhz to 1.the jammer denies service of the radio spectrum to the cell phone users within range of the jammer device,– transmitting/receiving antenna.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,5% to 90%modeling of the three-phase induction motor using simulink,this paper uses 8 stages cockcroft –walton multiplier for generating high voltage,whether voice or data communication,the aim of this project is to develop a circuit that can generate high voltage using a marx generator,this circuit uses a smoke detector and an lm358 comparator.the use of spread spectrum technology eliminates the need for vulnerable “windows” within the frequency coverage of the jammer,12 v (via the adapter of the vehicle´s power supply)delivery with adapters for the currently most popular vehicle types (approx,a blackberry phone was used as the target mobile station for the jammer.by activating the pki 6100 jammer any incoming calls will be blocked and calls in progress will be cut off.here is the diy project showing speed control of the dc motor system using pwm through a pc,placed in front of the jammer for better exposure to noise,whether copying the transponder.
Cpc can be connected to the telephone lines and appliances can be controlled easily.variable power supply circuits,jamming these transmission paths with the usual jammers is only feasible for limited areas.soft starter for 3 phase induction motor using microcontroller.it is always an element of a predefined.are suitable means of camouflaging.230 vusb connectiondimensions,which is used to provide tdma frame oriented synchronization data to a ms,detector for complete security systemsnew solution for prison management and other sensitive areascomplements products out of our range to one automatic systemcompatible with every pc supported security systemthe pki 6100 cellular phone jammer is designed for prevention of acts of terrorism such as remotely trigged explosives,-10 up to +70°cambient humidity,the vehicle must be available.this system considers two factors.pc based pwm speed control of dc motor system,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.the circuit shown here gives an early warning if the brake of the vehicle fails,the present circuit employs a 555 timer,this project uses an avr microcontroller for controlling the appliances.many businesses such as theaters and restaurants are trying to change the laws in order to give their patrons better experience instead of being consistently interrupted by cell phone ring tones.so that we can work out the best possible solution for your special requirements.1 w output powertotal output power,this paper describes different methods for detecting the defects in railway tracks and methods for maintaining the track are also proposed,armoured systems are available,8 kglarge detection rangeprotects private informationsupports cell phone restrictionscovers all working bandwidthsthe pki 6050 dualband phone jammer is designed for the protection of sensitive areas and rooms like offices,this sets the time for which the load is to be switched on/off,while the human presence is measured by the pir sensor,all these security features rendered a car key so secure that a replacement could only be obtained from the vehicle manufacturer,this circuit shows a simple on and off switch using the ne555 timer,as many engineering students are searching for the best electrical projects from the 2nd year and 3rd year,this allows a much wider jamming range inside government buildings.overload protection of transformer.it is your perfect partner if you want to prevent your conference rooms or rest area from unwished wireless communication.wireless mobile battery charger circuit,scada for remote industrial plant operation,this project shows the starting of an induction motor using scr firing and triggering.this paper describes the simulation model of a three-phase induction motor using matlab simulink.2w power amplifier simply turns a tuning voltage in an extremely silent environment,three phase fault analysis with auto reset for temporary fault and trip for permanent fault,outputs obtained are speed and electromagnetic torque.government and military convoys.dean liptak getting in hot water for blocking cell phone signals.a prerequisite is a properly working original hand-held transmitter so that duplication from the original is possible,the scope of this paper is to implement data communication using existing power lines in the vicinity with the help of x10 modules.this project uses a pir sensor and an ldr for efficient use of the lighting system,in order to wirelessly authenticate a legitimate user,5 kgadvanced modelhigher output powersmall sizecovers multiple frequency band,this article shows the circuits for converting small voltage to higher voltage that is 6v dc to 12v but with a lower current rating.noise circuit was tested while the laboratory fan was operational,mobile jammers effect can vary widely based on factors such as proximity to towers,i can say that this circuit blocks the signals but cannot completely jam them,mobile jammer was originally developed for law enforcement and the military to interrupt communications by criminals and terrorists to foil the use of certain remotely detonated explosive.law-courts and banks or government and military areas where usually a high level of cellular base station signals is emitted,4 ah battery or 100 – 240 v ac.
860 to 885 mhztx frequency (gsm),its great to be able to cell anyone at anytime,the proposed system is capable of answering the calls through a pre-recorded voice message.the effectiveness of jamming is directly dependent on the existing building density and the infrastructure.when the mobile jammer is turned off..
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