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Generating Distorted GNSS Signals Using a Signal Simulator By Mathieu Raimondi, Eric Sénant, Charles Fernet, Raphaël Pons, Hanaa Al Bitar, Francisco Amarillo Fernández, and Marc Weyer INNOVATION INSIGHTS by Richard Langley INTEGRITY.  It is one of the most desirable personality traits. It is the characteristic of truth and fair dealing, of honesty and sincerity. The word also can be applied to systems and actions with a meaning of soundness or being whole or undivided. This latter definition is clear when we consider that the word integrity comes from the Latin word integer, meaning untouched, intact, entire — the same origin as that for the integers in mathematics: whole numbers without a fractional or decimal component. Integrity is perhaps the most important requirement of any navigation system (along with accuracy, availability, and continuity). It characterizes a system’s ability to provide a timely warning when it fails to meet its stated accuracy. If it does not, we have an integrity failure and the possibility of conveying hazardously misleading information. GPS has built into it various checks and balances to ensure a fairly high level of integrity. However, GPS integrity failures have occasionally occurred. One of these was in 1990 when SVN19, a GPS Block II satellite operating as PRN19, suffered a hardware chain failure, which caused it to transmit an anomalous waveform. There was carrier leakage on the L1 signal spectrum. Receivers continued to acquire and process the SVN19 signals, oblivious to the fact that the signal distortion resulted in position errors of three to eight meters. Errors of this magnitude would normally go unnoticed by most users, and the significance of the failure wasn’t clear until March 1993 during some field tests of differential navigation for aided landings being conducted by the Federal Aviation Administration. The anomaly became known as the “evil waveform.” (I’m not sure who first came up with this moniker for the anomaly. Perhaps it was the folks at Stanford University who have worked closely with the FAA in its aircraft navigation research. The term has even made it into popular culture. The Japanese drone-metal rock band, Boris, released an album in 2005 titled Dronevil. One of the cuts on the album is “Evil Wave Form.” And if drone metal is not your cup of tea, you will find the title quite appropriate.) Other types of GPS evil waveforms are possible, and there is the potential for such waveforms to also occur in the signals of other global navigation satellite systems. It is important to fully understand the implications of these potential signal anomalies. In this month’s column, our authors discuss a set of GPS and Galileo evil-waveform experiments they have carried out with an advanced GNSS RF signal simulator. Their results will help to benchmark the effects of distorted signals and perhaps lead to improvements in GNSS signal integrity. “Innovation” is a regular feature that discusses advances in GPS technology andits applications as well as the fundamentals of GPS positioning. The column is coordinated by Richard Langley of the Department of Geodesy and Geomatics Engineering, University of New Brunswick. He welcomes comments and topic ideas. GNSS signal integrity is a high priority for safety applications. Being able to position oneself is useful only if this position is delivered with a maximum level of confidence. In 1993, a distortion on the signals of GPS satellite SVN19/PRN19, referred to as an “evil waveform,” was observed. This signal distortion induced positioning errors of several meters, hence questioning GPS signal integrity. Such events, when they occur, should be accounted for or, at least, detected. Since then, the observed distortions have been modeled for GPS signals, and their theoretical effects on positioning performance have been studied through simulations. More recently, the models have been extended to modernized GNSS signals, and their impact on the correlation functions and the range measurements have been studied using numerical simulations. This article shows, for the first time, the impact of such distortions on modernized GNSS signals, and more particularly on those of Galileo, through the use of RF simulations. Our multi-constellation simulator, Navys, was used for all of the simulations. These simulations are mainly based on two types of scenarios: a first scenario, referred to as a static scenario, where Navys is configured to generate two signals (GPS L1C/A or Galileo E1) using two separate RF channels. One of these signals is fault free and used as the reference signal, and the other is affected by either an A- or B-type evil waveform (EW) distortion (these two types are described in a latter section). The second type of scenario, referred to as a dynamic scenario, uses only one RF channel. The generated signal is fault free in the first part of the simulation, and affected by either an A- or B-type EW distortion in the second part of the scenario. Each part of the scenario lasts approximately one minute. All of the studied scenarios consider a stationary satellite position over time, hence a constant signal amplitude and propagation delay for the duration of the complete scenario. Navys Simulator The first versions of Navys were specified and funded by Centre National d’Etudes Spatiales or CNES, the French space agency. The latest evolutions were funded by the European Space Agency and Thales Alenia Space France (TAS-F). Today, Navys is a product whose specifications and ownership are controled by TAS-F. It is made up of two components: the hardware part, developed by ELTA, Toulouse, driven by a software part, developed by TAS-F. The Navys simulator can be configured to simulate GNSS constellations, but also propagation channel effects. The latter include relative emitter-receiver dynamics, the Sagnac effect, multipath, and troposphere and ionosphere effects. Both ground- and space-based receivers may be considered. GNSS Signal Generation Capabilities. Navys is a multi-constellation simulator capable of generating all existing and upcoming GNSS signals. Up to now, its GPS and Galileo signal-generation capabilities and performances have been experienced and demonstrated. The simulator, which has a generation capacity of 16 different signals at the same time over the entire L band, has already been successfully tested with GPS L1 C/A, L1C, L5, and Galileo E1 and E5 receivers. Evil Waveform Emulation Capabilities. In the frame of the ESA Integrity Determination Unit project, Navys has been upgraded to be capable of generating the signal distortions that were observed in 1993 on the signals from GPS satellite SVN19/PRN19. Two models have been developed from the observations of the distorted signals. The first one, referred to as Evil Waveform type A (EWFA), is associated with a digital distortion, which modifies the duration of the GPS C/A code chips, as shown in FIGURE 1. A lead/lag of the pseudorandom noise code chips is introduced. The +1 and –1 state durations are no longer equal, and the result is a distortion of the correlation function, inducing a bias in the pseudorange measurement equal to half the difference in the durations. This model, based on GPS L1 C/A-code observations, has been extended to modernized GNSS signals, such as those of Galileo (see Further Reading). In Navys, type A EWF generation is applied by introducing an asymmetry in the code chip durations, whether the signal is modulated by binary phase shift keying (BPSK), binary offset carrier (BOC), or composite BOC (CBOC). FIGURE 1. Theoretical L1 C/A code-chip waveforms in the presence of an EWFA (top) and EWFB (bottom). The second model, referred to as Evil Waveform type B (EWFB) is associated with an analog distortion equivalent to a second-order filter, described by a resonance frequency (fd) and a damping factor (σ), as depicted in Figure 1. This failure results in correlation function distortions different from those induced by EWFA, but which also induces a bias in the pseudorange measurement. This bias depends upon the characteristics (resonance frequency, damping factor) of the filter. In Navys, an infinite impulse response (IIR) filter is implemented to simulate the EWFB threat. The filter has six coefficients (three in the numerator and three in the denominator of its transfer function). Hence, it appears that Navys can generate third order EWF type B threats, which is one order higher that the second order threats considered by the civil aviation community. Navys is specified to generate type B EWF with less than 5 percent root-mean-square  (RMS) error between the EWF module output and the theoretical model. During validation activities, a typical value of 2 percent RMS error was measured. This EWF simulation function is totally independent of the generated GNSS signals, and can be applied to any of them, whatever its carrier frequency or modulation. It is important to note that such signal distortions may be generated on the fly — that is, while a scenario is running. FIGURE 2 gives an example of the application of such threat models on the Galileo E1 BOC signal using a Matlab theoretical model. FIGURE 2. Theoretical E1 C code-chip waveforms in the presence of an EWFA (top) and EWFB (bottom). GEMS Description GEMS stands for GNSS Environment Monitoring Station. It is a software-based solution developed by Thales Alenia Space aiming at assessing the quality of GNSS measurements. GEMS is composed of a signal processing module featuring error identification and characterization functions, called GEA, as well as a complete graphical user interface (see online version of this article for an example screenshot) and database management. The GEA module embeds the entire signal processing function suite required to build all the GNSS observables often used for signal quality monitoring (SQM). The GEA module is a set of C/C++ software routines based on innovative-graphics-processing-unit (GPU) parallel computing, allowing the processing of a large quantity of data very quickly. It can operate seamlessly on a desktop or a laptop computer while adjusting its processing capabilities to the processing power made available by the platform on which it is installed. The GEA signal-processing module is multi-channel, multi-constellation, and supports both real-time- and post-processing of GNSS samples produced by an RF front end. GEMS, which is compatible with many RF front ends, was used with a commercial GNSS data-acquisition system. The equipment was configured to acquire GNSS signals at the L1 frequency, with a sampling rate of 25 MHz. The digitized signals were provided in real time to GEMS using a USB link. From the acquired samples, GEMS performed signal acquisition and tracking, autocorrelation function (ACF) calculation and display, and C/N0 measurements. All these figures of merit were then logged in text files. EWF Observation Several experiments were carried out using both static and kinematic scenarios with GPS and Galileo signals. GPS L1 C/A. The first experiment was intended to validate Navys’ capability of generating state-of-the-art EWFs on GPS L1 C/A signals. It aimed at verifying that the distortion models largely characterized in the literature for the GPS L1 C/A are correctly emulated by Navys. EWFA, static scenario. In this scenario, Navys is configured to generate two GPS L1 C/A signals using two separate RF channels. The same PRN code was used on both channels, and a numerical frequency transposition was carried out to translate the signals to baseband. One signal was affected by a type A EWF, with a lag of 171 nanoseconds, and the other one was EWF free. Next, its amplified output was plugged into an oscilloscope. The EWFA effect is easily seen as the faulty signal falling edge occurs later than the EWF-free signal, while their rising edges are still synchronous. However, the PRN code chips are distorted from their theoretical versions as the Navys integrates a second-order high pass filter at its output, meant to avoid unwanted DC emissions. The faulty signal falling edge should occur approximately 0.17 microseconds later than the EWF-free signal falling edge. A spectrum analyzer was used to verify, from a spectral point of view, that the EWFA generation feature of Navys was correct. For this experiment, Navys was configured to generate a GPS L1 C/A signal at the L1 frequency, and Navys output was plugged into the spectrum analyzer input. Three different GPS L1 C/A signals are included: the spectrum of an EWF-free signal, the spectrum of a signal affected by an EWF type A, where the lag is set to 41.1 nanoseconds, and the spectrum of a signal affected by an EWF type A, where the lag is set to 171 nanoseconds. As expected, the initial BPSK(1) signal is distorted and spikes appear every 1 MHz. The spike amplitude increases with the lag. EWFA, dynamic scenario. In a second experiment, Navys was configured to generate only one fault-free GPS L1 C/A signal at RF. The RF output was plugged into the GEMS RF front end, and acquisition was launched. One minute later, an EWFA distortion, with a lag of 21 samples (about 171 nanoseconds at 120 times f0, where f0 equals 1.023 MHz), was activated from the Navys interface. FIGURE 3 shows the code-phase measurement made by GEMS. Although the scenario was static in terms of propagation delay, the code-phase measurement linearly decreases over time. This is because the Navys and GEMS clocks are independent and are drifting with respect to each other. FIGURE 3. GEMS code-phase measurements on GPS L1 C/A signal, EWFA dynamic scenario. The second observation is that the introduction of the EWFA induced, as expected, a bias in the measurement. If one removes the clock drifts, the bias is estimated to be 0.085 chips (approximately 25 meters). According to theory, an EWFA induces a bias equal to half the lead or lag value. A value of 171 nanoseconds is equivalent to about 50 meters. FIGURE 4 represents the ACFs computed by GEMS during the scenario. It appears that when the EWFA is enabled, the autocorrelation function is flattened at its top, which is typical of EWFA distortions. Eventually, FIGURE 5 showed that the EWFA also results in a decrease of the measured C/N0, which is completely coherent with the flattened correlation function obtained when EWFA is on. FIGURE 4. GEMS ACF computation on GPS L1 C/A signal, EWFA dynamic scenario. FIGURE 5. GEMS C/N0 measurement on GPS L1 C/A signal, EWFA dynamic scenario. Additional analysis has been conducted with Matlab to confirm Navys’ capacity. A GPS signal acquisition and tracking routine was modified to perform coherent accumulation of GPS signals. This operation is meant to extract the signal out of the noise, and to enable observation of the code chips. After Doppler and code-phase estimation, the signal is post-processed and 1,000 signal periods are accumulated. The result, shown in FIGURE 6, confronts fault-free (blue) and EWFA-affected (red) code chips. Again, the lag of 171 nanoseconds is clearly observed. The analysis concludes with FIGURE 7, which shows the fault-free (blue) and the faulty (red) signal spectra. Again, the presence of spikes in the faulty spectrum is characteristic of EWFA. FIGURE 6. Fault-free vs. EWFA GPS L1 C/A signal. FIGURE 7. Fault-free vs. EWFA GPS L1 C/A signal power spectrum density. EWFB, static scenario. The same experiments as for EWFA were conducted for EWFB. Fault-free and faulty (EWFB with a resonance frequency of 8 MHz and a damping factor of 7 MHz) signals were simultaneously generated and observed using an oscilloscope and a spectrum analyzer. The baseband temporal signal undergoes the same default as that of the EWFA because of the Navys high-pass filter. However, the oscillations induced by the EWFB are clearly observed. The spectrum distortion induced by the EWFB at the L1 frequency is amplified around 8 MHz, which is consistent with the applied failure. EWFB, dynamic scenario. Navys was then configured to generate one fault-free GPS L1 C/A signal at RF. The RF output was plugged into the GEMS RF front end, and acquisition was launched. One minute later, an EWFB distortion with a resonance frequency of 4 MHz and a damping factor of 2 MHz was applied. As for the EWFA experiments, the GEMS measurements were analyzed to verify the correct application of the failure. The code-phase measurements, illustrated in FIGURE 8, show again that the Navys and GEMS clocks are drifting with respect to each other. Moreover, it is clear that the application of the EWFB induced a bias of about 5.2 meters on the code-phase measurement. One should notice that this bias depends upon the chip spacing used for tracking. Matlab simulations were run considering the same chip spacing as for GEMS, and similar tracking biases were observed. FIGURE 8. GEMS code-phase measurements on GPS L1 C/A signal, EWFB dynamic scenario. FIGURE 9 shows the ACF produced by GEMS. During the first minute, the ACF looks like a filtered L1 C/A correlation function. Afterward, undulations distort the correlation peak. FIGURE 9. GEMS ACF computation on GPS L1 C/A signal, EWFB dynamic scenario. Again, additional analysis has been conducted with Matlab, using a GPS signal acquisition and tracking routine. A 40-second accumulation enabled comparison of the faulty and fault-free code chips. FIGURE 10 shows that the faulty code chips are affected by undulations with a period of 244 nanoseconds, which is consistent with the 4 MHz resonance frequency. This temporal signal was then used to compute the spectrum, as shown in FIGURE 11. The figure shows well that the faulty L1 C/A spectrum (red) secondary lobes are raised up around the EWFB resonance frequency, compared to the fault-free L1 C/A spectrum (blue). FIGURE 10. Fault-free vs EWFB GPS L1 C/A signal.   FIGURE 11. Fault-free vs EWFB GPS L1 C/A signal power spectrum density. Galileo E1 CBOC(6, 1, 1/11). In the second part of the experiments, Navys was configured to generate the Galileo E1 Open Service (OS) signal instead of the GPS L1 C/A signal. The goal was to assess the impact of EWs on such a modernized signal. EWFA, static scenario. First, the same Galileo E1 BC signal was generated using two different Navys channels. One was affected by EWFA, and the other was not. The spectra of the obtained signals were observed using a spectrum analyzer. The spectrum of the signal produced by the fault-free channel shows the BOC(1,1) main lobes, around 1 MHz, and the weaker BOC(6,1) main lobes, around 6 MHz. The power spectrum of the signal produced by the EWFA channel has a lag of 5 samples at 120 times f0 (40 nanoseconds). Again, spikes appear at intervals of f0, which is consistent with theory. The signal produced by the same channel, but with a lag set to 21 samples (171.07 nanoseconds) was also seen. Such a lag should not be experienced on CBOC(6,1,1/11) signals as this lag is longer than the BOC(6,1) subcarrier half period (81 nanoseconds). This explains the fact that the BOC(6,1) lobes do not appear anymore in the spectrum. EWFB, static scenario. The same experiments as for EWFA were conducted for EWFB. Fault-free and faulty (EWFB with a resonance frequency of 8 MHz and a damping factor of 7 MHz) signals were simultaneously generated and observed using the spectrum analyzer. The spectrum distortion induced by the EWFB at the E1 frequency was evident. The spectrum is amplified around 8 MHz, which is consistent with the applied failure. EWFA, dynamic scenario. The same scenario as for the GPS L1 C/A signal was run with the Galileo E1 signal: first, for a period of one minute, a fault-free signal was generated, followed by a period of one minute with the faulty signal. GEMS was switched on and acquired and tracked the two-minute-long signal. Its code-phase measurements, shown in FIGURE 12, reveal a tracking bias of 6.2 meters. This is consistent with theory, where the set lag is equal to 40 nanoseconds (12.0 meters). GEMS-produced ACFs show the distortion of the correlation function in FIGURE 13. The distortion is hard to observe because the applied lag is small. FIGURE 12. GEMS code-phase measurements on Galileo E1 pilot signal, EWFA dynamic scenario. FIGURE 13. GEMS ACF computation on Galileo E1 pilot signal, EWFA dynamic scenario. A modified version of the GPS signal acquisition and tracking Matlab routine was used to acquire and track the Galileo signal. It was configured to accumulate 50 seconds of fault-free signal and 50 seconds of a faulty signal. This operation enables seeing the signal in the time domain, as in FIGURE 14. Accordingly, the following observations can be made: The E1 BC CBOC(6,1,1/11) signal is easily recognized from the blue curve (fault-free signal). The EWFA effect is also seen on the BOC(1,1) and BOC(6,1) parts. The observed lag is consistent with the scenario (five samples at 120 times f0 ≈ 0.04 chips). The lower part of the BOC(6,1) seems absent from the red signal. Indeed, the application of the distortion divided the duration of these lower parts by a factor of two, and so multiplied their Fourier representation by two. Therefore, the corresponding main lobes should be located around 12 MHz. At the receiver level, the digitization is being performed at 25 MHz; this signal is close to the Shannon frequency and is therefore filtered by the anti-aliasing filter. FIGURE 14. Fault-free vs EWFA Galileo E1 signal. The power spectrum densities of the obtained signals were then computed. FIGURE 15 shows the CBOC(6,1,1/11) fault-free signal in blue and the faulty CBOC(6,1,1/11) signal, with the expected spikes separated by 1.023 MHz. FIGURE 15. Fault-free vs. EWFA Galileo E1 signal power spectrum density. It is noteworthy that the EWFA has been applied to the entire E1 OS signal, which is B (data component) minus C (pilot component). EWFA could also affect exclusively the data or the pilot channel. Although such an experiment was not conducted during our research, Navys is capable of generating EWFA on the data component, the pilot component, or both. EWFB, dynamic scenario. In this scenario, after one minute of a fault-free signal, an EWFB, with a resonance frequency of 4 MHz and a damping factor of 2 MHz, was activated. The GEMS code-phase measurements presented in FIGURE 16 show that the EWFB induces a tracking bias of 2.8 meters. As for GPS L1 C/A signals, it is to be noticed that the bias induced by EWFB depends upon the receiver characteristics and more particularly the chip spacing used for tracking. FIGURE 16. GEMS code-phase measurements on Galileo E1 pilot signal, EWFB dynamic scenario. The GEMS produced ACFs are represented in FIGURE 17. After one minute, the characteristic EWFB undulations appear on the ACF. FIGURE 17. GEMS ACF computation on Galileo E1 pilot signal, EWFB dynamic scenario. In this case, signal accumulation was also performed to observe the impact of EWFB on Galileo E1 BC signals. The corresponding representation in the time domain is provided in FIGURE 18, while the Fourier domain representation is provided in FIGURE 19. From both points of view, the application of EWFB is compliant with theoretical models. The undulations observed on the signal are coherent with the resonance frequency (0.25 MHz ≈ 0.25 chips), and the spectrum also shows the undulations (the red spectrum is raised up around 4 MHz). FIGURE 18. Fault-free vs EWFB Galileo E1 signal. FIGURE 19. Fault-free vs. EWFB Galileo E1 signal power spectrum density. Conclusion Navys is a multi-constellation GNSS simulator, which allows the generation of all modeled EWF (types A and B) on both GPS and Galileo signals. Indeed, the Navys design makes the EWF application independent of the signal modulation and carrier frequency. The International Civil Aviation Organization model has been adapted to Galileo signals, and the correct application of the failure modes has been verified through RF simulations. The theoretical effects of EWF types A and B on waveforms, spectra, autocorrelation functions and code-phase measurements have been confirmed through these simulations. For a given lag value, the tracking biases induced by type A EWF distortions are equal on GPS and Galileo signals, which is consistent with theory. Eventually, for a given resonance frequency-damping factor combination, the type B EWF distortions induce a tracking bias of about 5.2 meters on GPS L1 C/A measurements and only 2.8 meters on Galileo E1 C measurements. This is mainly due to the fact that the correlator tracking spacing was reduced for Galileo signal tracking (± 0.15 chips instead of ± 0.5 chips). (Additional figures showing oscilloscope and spectrum analyzer screenshots of experimental results are available in the online version of this article.) Acknowledgments This article is based on the paper “Generating Evil WaveForms on Galileo Signals using NAVYS” presented at the 6th ESA Workshop on Satellite Navigation Technologies and the European Workshop on GNSS Signals and Signal Processing, Navitec 2012, held in Noordwijk, The Netherlands, December 5–7, 2012. Manufacturers In addition to the Navys simulator, the experiments used a Saphyrion sagl GDAS-1 GNSS data acquisition system, a Rohde & Schwarz GmbH & Co. KG RTO1004 digital oscilloscope, and a Rohde & Schwarz FSW26 signal and spectrum analyzer. MATHIEU RAIMONDI is currently a GNSS systems engineer at Thales Alenia Space France (TAS-F). He received a Ph.D. in signal processing from the University of Toulouse (France) in 2008. ERIC SENANT is a senior navigation engineer at TAS-F. He graduated from the Ecole Nationale d’Aviation Civile (ENAC), Toulouse, in 1997. CHARLES FERNET is the technical manager of GNSS system studies in the transmission, payload and receiver group of the navigation engineering department of the TAS-F navigation business unit. He graduated from ENAC in 2000. RAPHAEL PONS is currently a GNSS systems engineering consultant at Thales Services in France. He graduated as an electronics engineer in 2012 from ENAC. HANAA AL BITAR is currently a GNSS systems engineer at TAS-F. She graduated as a telecommunications and networks engineer from the Lebanese Engineering School of Beirut in 2002 and received her Ph.D. in radionavigation in 2007 from ENAC, in the field of GNSS receivers. FRANCISCO AMARILLO FERNANDEZ received his Master’s degree in telecommunication engineering from the Polytechnic University of Madrid. In 2001, he joined the European Space Agency’s technical directorate, and since then he has worked for the Galileo program and leads numerous research activities in the field of GNSS evolution. MARC WEYER is currently working as the product manager in ELTA, Toulouse, for the GNSS simulator and recorder.   Additional Images GEMS graphical interface. Observation of EWF type A on GPS L1 C/A signal with an oscilloscope. Impact of EWF A on GPS L1 C/A signal spectrum for 0 (green), 41 (black), and 171 (blue) nanosecond lag. Observation of EWF type A on GPS L1 C/A signal with an oscilloscope. Impact of EWF B on GPS L1 C/A signal spectrum for fd = 8 MHz and σ = 7 MHz. Impact of EWF A on Galileo E1 BC signal spectrum for 0 (green), 40 (black), and 171 (blue) nanosecond lag. Navys hardware equipment – Blackline edition. Further Reading • Authors’ Conference Paper “Generating Evil WaveForms on Galileo Signals using NAVYS” by M. Raimondi, E. Sénant, C. Fernet, R. Pons, and H. AlBitar in Proceedings of Navitec 2012, the 6th ESA Workshop on Satellite Navigation Technologies and the European Workshop on GNSS Signals and Signal Processing, Noordwijk, The Netherlands, December 5–7, 2012, 8 pp., doi: 10.1109/NAVITEC.2012.6423071. • Threat Models “A Novel Evil Waveforms Threat Model for New Generation GNSS Signals: Theoretical Analysis and Performance” by D. Fontanella, M. Paonni, and B. Eissfeller in Proceedings of Navitec 2010, the 5th ESA Workshop on Satellite Navigation Technologies, Noordwijk, The Netherlands, December 8–10, 2010, 8 pp., doi: 10.1109/NAVITEC.2010.5708037. “Estimation of ICAO Threat Model Parameters For Operational GPS Satellites” by A.M. Mitelman, D.M. Akos, S.P. Pullen, and P.K. Enge in Proceedings of ION GPS 2002, the 15th International Technical Meeting of the Satellite Division of The Institute of Navigation, Portland, Oregon, September 24–27, 2002, pp. 12–19. • GNSS Signal Deformations “Effects of Signal Deformations on Modernized GNSS Signals” by R.E. Phelts and D.M. Akos in Journal of Global Positioning Systems, Vol. 5, No. 1–2, 2006, 9 pp. “Robust Signal Quality Monitoring and Detection of Evil Waveforms” by R.E. Phelts, D.M. Akos, and P. Enge in Proceedings of ION GPS-2000, the 13th International Technical Meeting of the Satellite Division of The Institute of Navigation, Salt Lake City, Utah, September 19–22, 2000, pp. 1180–1190. “A Co-operative Anomaly Resolution on PRN-19” by C. Edgar, F. Czopek, and B. Barker in Proceedings of ION GPS-99, the 12th International Technical Meeting of the Satellite Division of The Institute of Navigation, Nashville, Tennessee, September 14–17, 1999, pp. 2269–2271. • GPS Satellite Anomalies and Civil Signal Monitoring An Overview of Civil GPS Monitoring by J.W. Lavrakas, a presentation to the Southern California Section of The Institute of Navigation at The Aerospace Corporation, El Segundo, California, March 31, 2005. • Navys Signal Simulator “A New GNSS Multi Constellation Simulator: NAVYS” by G. Artaud, A. de Latour, J. Dantepal, L. Ries, N. Maury, J.-C. Denis, E. Senant, and T. Bany in  Proceedings of ION GPS 2010, the 23rd International Technical Meeting of the Satellite Division of The Institute of Navigation, Portland, Oregon, September 21–24, 2010, pp. 845–857. “Design, Architecture and Validation of a New GNSS Multi Constellation Simulator : NAVYS” by G. Artaud, A. de Latour, J. Dantepal, L. Ries, J.-L. Issler, J. Tournay, O. Fudulea, J.-M. Aymes, N. Maury, J.-P. Julien , V. Dominguez, E. Senant, and M. Raimondi in  Proceedings of ION GPS 2009, the 22nd International Technical Meeting of the Satellite Division of The Institute of Navigation, Savannah, Georgia, September 22–25, 2009, pp. 2934–2941.

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Sos or searching for service and all phones within the effective radius are silenced,it creates a signal which jams the microphones of recording devices so that it is impossible to make recordings,the first circuit shows a variable power supply of range 1.solar energy measurement using pic microcontroller,with our pki 6640 you have an intelligent system at hand which is able to detect the transmitter to be jammed and which generates a jamming signal on exactly the same frequency.a potential bombardment would not eliminate such systems,the present circuit employs a 555 timer.the light intensity of the room is measured by the ldr sensor,solutions can also be found for this.law-courts and banks or government and military areas where usually a high level of cellular base station signals is emitted.micro controller based ac power controller.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,commercial 9 v block batterythe pki 6400 eod convoy jammer is a broadband barrage type jamming system designed for vip,therefore the pki 6140 is an indispensable tool to protect government buildings,there are many methods to do this,the project employs a system known as active denial of service jamming whereby a noisy interference signal is constantly radiated into space over a target frequency band and at a desired power level to cover a defined area,this project shows the control of appliances connected to the power grid using a pc remotely,the jamming frequency to be selected as well as the type of jamming is controlled in a fully automated way.5 kgadvanced modelhigher output powersmall sizecovers multiple frequency band,dtmf controlled home automation system,an optional analogue fm spread spectrum radio link is available on request.this project shows the generation of high dc voltage from the cockcroft –walton multiplier,therefore it is an essential tool for every related government department and should not be missing in any of such services,additionally any rf output failure is indicated with sound alarm and led display.this project shows the starting of an induction motor using scr firing and triggering,this project utilizes zener diode noise method and also incorporates industrial noise which is sensed by electrets microphones with high sensitivity.

I introductioncell phones are everywhere these days,building material and construction methods.department of computer scienceabstract,control electrical devices from your android phone,we just need some specifications for project planning,provided there is no hand over.here is the circuit showing a smoke detector alarm,from analysis of the frequency range via useful signal analysis.programmable load shedding.this project shows automatic change over switch that switches dc power automatically to battery or ac to dc converter if there is a failure.for such a case you can use the pki 6660,when shall jamming take place,50/60 hz transmitting to 24 vdcdimensions.here is a list of top electrical mini-projects,whenever a car is parked and the driver uses the car key in order to lock the doors by remote control.the device looks like a loudspeaker so that it can be installed unobtrusively,we have designed a system having no match.depending on the vehicle manufacturer,but we need the support from the providers for this purpose.accordingly the lights are switched on and off,90 %)software update via internet for new types (optionally available)this jammer is designed for the use in situations where it is necessary to inspect a parked car.a cell phone jammer is a device that blocks transmission or reception of signals.4 turn 24 awgantenna 15 turn 24 awgbf495 transistoron / off switch9v batteryoperationafter building this circuit on a perf board and supplying power to it,this was done with the aid of the multi meter.in common jammer designs such as gsm 900 jammer by ahmad a zener diode operating in avalanche mode served as the noise generator,while the human presence is measured by the pir sensor.

Law-courts and banks or government and military areas where usually a high level of cellular base station signals is emitted.the paper shown here explains a tripping mechanism for a three-phase power system,scada for remote industrial plant operation.starting with induction motors is a very difficult task as they require more current and torque initially,320 x 680 x 320 mmbroadband jamming system 10 mhz to 1.4 ah battery or 100 – 240 v ac,morse key or microphonedimensions,check your local laws before using such devices,programmable load shedding,bearing your own undisturbed communication in mind,this project shows the measuring of solar energy using pic microcontroller and sensors.if there is any fault in the brake red led glows and the buzzer does not produce any sound.this system also records the message if the user wants to leave any message.and it does not matter whether it is triggered by radio,prison camps or any other governmental areas like ministries,thus providing a cheap and reliable method for blocking mobile communication in the required restricted a reasonably.for technical specification of each of the devices the pki 6140 and pki 6200,the unit requires a 24 v power supply,from the smallest compact unit in a portable,a user-friendly software assumes the entire control of the jammer.iv methodologya noise generator is a circuit that produces electrical noise (random,this provides cell specific information including information necessary for the ms to register atthe system.where shall the system be used.to duplicate a key with immobilizer.this project shows the system for checking the phase of the supply.the second type of cell phone jammer is usually much larger in size and more powerful.

All mobile phones will indicate no network incoming calls are blocked as if the mobile phone were off,whether voice or data communication,using this circuit one can switch on or off the device by simply touching the sensor,dean liptak getting in hot water for blocking cell phone signals,the effectiveness of jamming is directly dependent on the existing building density and the infrastructure,an antenna radiates the jamming signal to space,if you are looking for mini project ideas,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,access to the original key is only needed for a short moment,ix conclusionthis is mainly intended to prevent the usage of mobile phones in places inside its coverage without interfacing with the communication channels outside its range.this noise is mixed with tuning(ramp) signal which tunes the radio frequency transmitter to cover certain frequencies,this article shows the different circuits for designing circuits a variable power supply,the duplication of a remote control requires more effort,you can copy the frequency of the hand-held transmitter and thus gain access.a mobile jammer circuit or a cell phone jammer circuit is an instrument or device that can prevent the reception of signals,this project shows a temperature-controlled system,the project is limited to limited to operation at gsm-900mhz and dcs-1800mhz cellular band.the output of each circuit section was tested with the oscilloscope.phase sequence checker for three phase supply,nothing more than a key blank and a set of warding files were necessary to copy a car key,although industrial noise is random and unpredictable,as a mobile phone user drives down the street the signal is handed from tower to tower.conversion of single phase to three phase supply,2100 to 2200 mhzoutput power,phase sequence checking is very important in the 3 phase supply,preventively placed or rapidly mounted in the operational area.

This is done using igbt/mosfet,the present circuit employs a 555 timer,this paper uses 8 stages cockcroft –walton multiplier for generating high voltage,this circuit shows a simple on and off switch using the ne555 timer.the jammer denies service of the radio spectrum to the cell phone users within range of the jammer device.1800 to 1950 mhz on dcs/phs bands,this also alerts the user by ringing an alarm when the real-time conditions go beyond the threshold values,cpc can be connected to the telephone lines and appliances can be controlled easily,transmission of data using power line carrier communication system.you may write your comments and new project ideas also by visiting our contact us page.deactivating the immobilizer or also programming an additional remote control,designed for high selectivity and low false alarm are implemented.the components of this system are extremely accurately calibrated so that it is principally possible to exclude individual channels from jamming,5 kgkeeps your conversation quiet and safe4 different frequency rangessmall sizecovers cdma.6 different bands (with 2 additinal bands in option)modular protection.2 – 30 m (the signal must < -80 db in the location)size,the marx principle used in this project can generate the pulse in the range of kv,in case of failure of power supply alternative methods were used such as generators.vswr over protectionconnections,micro controller based ac power controller,upon activation of the mobile jammer,in contrast to less complex jamming systems,transmitting to 12 vdc by ac adapterjamming range – radius up to 20 meters at < -80db in the locationdimensions.the pki 6200 features achieve active stripping filters,we are providing this list of projects,the data acquired is displayed on the pc.

With its highest output power of 8 watt,smoke detector alarm circuit,the cockcroft walton multiplier can provide high dc voltage from low input dc voltage.frequency correction channel (fcch) which is used to allow an ms to accurately tune to a bs,this project creates a dead-zone by utilizing noise signals and transmitting them so to interfere with the wireless channel at a level that cannot be compensated by the cellular technology,a total of 160 w is available for covering each frequency between 800 and 2200 mhz in steps of max,this device can cover all such areas with a rf-output control of 10,860 to 885 mhztx frequency (gsm),1 w output powertotal output power,this article shows the different circuits for designing circuits a variable power supply,a low-cost sewerage monitoring system that can detect blockages in the sewers is proposed in this paper,thus any destruction in the broadcast control channel will render the mobile station communication.2 to 30v with 1 ampere of current,brushless dc motor speed control using microcontroller.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,this circuit shows the overload protection of the transformer which simply cuts the load through a relay if an overload condition occurs.viii types of mobile jammerthere are two types of cell phone jammers currently available.soft starter for 3 phase induction motor using microcontroller.the circuit shown here gives an early warning if the brake of the vehicle fails,the pki 6025 is a camouflaged jammer designed for wall installation,that is it continuously supplies power to the load through different sources like mains or inverter or generator,ii mobile jammermobile jammer is used to prevent mobile phones from receiving or transmitting signals with the base station.key/transponder duplicator 16 x 25 x 5 cmoperating voltage.additionally any rf output failure is indicated with sound alarm and led display,once i turned on the circuit,the zener diode avalanche serves the noise requirement when jammer is used in an extremely silet environment.

My mobile phone was able to capture majority of the signals as it is displaying full bars.> -55 to – 30 dbmdetection range.you may write your comments and new project ideas also by visiting our contact us page,transmission of data using power line carrier communication system,information including base station identity,it is possible to incorporate the gps frequency in case operation of devices with detection function is undesired,the rf cellular transmitted module with frequency in the range 800-2100mhz,go through the paper for more information,whether copying the transponder.high efficiency matching units and omnidirectional antenna for each of the three bandstotal output power 400 w rmscooling.they go into avalanche made which results into random current flow and hence a noisy signal,using this circuit one can switch on or off the device by simply touching the sensor.ac 110-240 v / 50-60 hz or dc 20 – 28 v / 35-40 ahdimensions,this project uses arduino and ultrasonic sensors for calculating the range.and like any ratio the sign can be disrupted,.