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Synthetic-Aperture GNSS Signal Processing By Thomas Pany, Nico Falk, Bernhard Riedl, Carsten Stöber, Jón O. Winkel, and Franz-Josef Schimpl INNOVATION INSIGHTS by Richard Langley A SYNTHETIC APERTURE? WHAT’S THAT? Well, an aperture in optics is just a hole or opening through which light travels. Those of us into photography know that the amount of light reaching the camera’s imaging sensor is controlled by the shutter speed and the size of the lens opening or aperture (called the f-stop). And a correct combination of the aperture setting and shutter speed results in a correct exposure.  For an optical telescope, its aperture is the diameter of its main, light-gathering lens or mirror. A larger aperture gives a sharper and brighter view or image. In the radio part of the electromagnetic spectrum, the term aperture refers to the effective collecting (or transmitting) area of an antenna. The gain of the antenna is proportional to its aperture and its beamwidth or resolution is inversely proportional to it. Astronomers, whether using optical or radio telescopes, often seek higher and higher resolutions to see more detail in the objects they are investigating. Conventionally, that means larger and larger telescopes. However, there are limits to how large a single telescope can be constructed. But by combining the light or radio signals from two or more individual telescopes, one can synthesize a telescope with a diameter equal to the baseline(s) connecting those telescopes. The approach is known as interferometry. It was first tried in the optical domain by the American physicist Albert Michelson who used the technique to measure the diameter of the star Betelgeuse. Radio astronomers developed cable- and microwave-connected interferometers and subsequently they invented the technique of very long baseline interferometry (VLBI) where atomic-clock-stabilized radio signals are recorded on magnetic tape and played back through specially designed correlators to form an image. (VLBI has also been used by geodesists to precisely determine the baselines between pairs of radio telescopes even if they are on separate continents.) A similar approach is used in synthetic-aperture radar (SAR). Mounted on an aircraft or satellite, the SAR beam-forming antenna emits pulses of radio waves that are reflected from a target and then coherently combined. The different positions of the SAR, as it moves, synthesize an elongated aperture resulting in finer spatial resolution than would be obtained by a conventional antenna. But what has all of this got to do with GNSS? In this month’s column, we take a look at a novel GNSS signal-processing technique, which uses the principles of SAR to improve code and carrier-phase observations in degraded environments such as under forest canopy. The technique can simultaneously reject multipath signals while maximizing the direct line-of-sight signal power from a satellite. Along with a specially programmed software receiver, it uses either a single conventional antenna mounted, say, on a pedestrian’s backpack for GIS applications or a special rotating antenna for high-accuracy surveying. Want to learn more? Read on. “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. Over the past few years, we have been developing new GNSS receivers and antennas based on an innovative signal-processing scheme to significantly improve GNSS tracking reliability and accuracy under degraded signal conditions. It is based on the principles of synthetic-aperture radar. Like in a multi-antenna phased-array receiver, GNSS signals from different spatial locations are combined coherently forming an optimized synthetic antenna-gain pattern. Thereby, multipath signals can be rejected and the line-of-sight received signal power is maximized. This is especially beneficial in forests and in other degraded environments. The method is implemented in a real-time PC-based software receiver and works with GPS, GLONASS, and Galileo signals. Multiple frequencies are generally supported. The idea of synthetic-aperture processing is realized as a coherent summation of correlation values of each satellite over the so-called beam-forming interval. Each correlation value is multiplied with a phase factor. For example, the phase factor can be chosen to compensate for the relative antenna motion over the beam-forming interval and the resulting sum of the scaled correlation values represents a coherent correlation value maximizing the line-of-sight signal power. Simultaneously, signals arriving from other directions are partly eliminated. Two main difficulties arise in the synthetic-aperture processing. First, the clock jitter during the beam-forming interval must be precisely known. It can either be estimated based on data from all signals, or a stable oscillator can be used. In one of our setups, a modern oven-controlled crystal oscillator with an Allan variance of 0.5 × 10-13 at an averaging period of 1 second is used. Second, the precise relative motion of the antenna during the beam-forming interval must be known. Again it can be estimated if enough sufficiently clean signals are tracked. The antenna trajectory is estimated directly from the correlator values as shown later in this article. In more severely degraded environments, the antenna may be moved along a known trajectory. We are developing a rotating antenna displacement unit. (see FIGURE 1). The rotational unit targets forestry and indoor surveying applications. The relative motion of the antenna is measured with sub-millimeter accuracy. FIGURE 1. Artist’s impression of the synthetic-aperture GNSS system for surveying in a forest. After beam-forming, the code pseudoranges and the carrier phases are extracted and used in a conventional way. That is, they are written into Receiver Independent Exchange (RINEX) format files and standard geodetic software can be used to evaluate them. In the case where the artificial movement antenna is used, the GNSS signal processing removes the known part of the movement from the observations, and the observations are then like those from a static antenna. As a result, common static positioning algorithms, including carrier-phase ambiguity fixing, can be applied. The presented method therefore prepares the path for GNSS surveying applications in new areas. An important point is the mechanical realization of the antenna movement. This has to be done in a cost-efficient and reliable way. Lubrication-free actuators are used together with magnetic displacement sensors. The sensors are synchronized to the software receiver front end with better than 1 millisecond accuracy. The rotating antenna uses slip rings to connect the antenna elements. The rotating antenna can also be used to map the received signal power as a function of elevation and azimuth angles. This is beneficial for researchers. For example, it could be used to estimate the direction of arrival of a spoofing signal or to determine which object causes multipath in an indoor environment. For the latter purpose, the rotating antenna can be equipped with left-hand and right-hand circularly polarized antennas on both ends of the rotating bar. The rotating antenna is mounted on a geodetic tripod. See Further Reading for reports of initial studies of the rotating antenna. Tracking Modes The synthetic-aperture tracking scheme can be extended to different user-motion schemes or sensor-aiding schemes allowing a wide range of applications. This is reflected in the algorithm implementation within the modular structure of the software receiver. The base module “µ-trajectory & Clock Estimator” in Figure 2 prepares the synthetic-aperture tracking scheme. Different implementations derive from this base class. Each derived module is used for a different user motion scheme and makes use of a different sensor. FIGURE 2. Different µ-trajectory motion estimators used by the synthetic-aperture processing. Basically, the modules differ in the way they estimate the relative antenna motion over the beam-forming interval. This relative motion is called the µ-trajectory. Usually the µ-trajectory covers time spans from a few hundreds of milliseconds to a few seconds. The µ-trajectories have the following characteristics: The pedestrian motion estimator does not rely on any sensor measurements and fits a second-order polynomial into the user µ-trajectory of a walking pedestrian. A second-order polynomial is good for representing the motion for up to a quarter of a second. The sensor input to the rotating antenna estimator is the relative angular displacement of the rotating antenna. The estimator estimates the absolute direction, which is stable in time. Thus the number of µ-trajectory parameters equals one. The vertical antenna motion estimator retrieves the vertical position of the antenna and does not estimate any µ-trajectory parameters. Only clock parameters are estimated. Finally, the inertial navigation estimator uses accelerometer and gyro measurements and estimates the 3D user motion. The µ-trajectory parameters consist of accelerometer biases, the gyro biases, attitude errors, and velocity errors. The estimation process is much more complex and exploits the timely correlation of the parameters. Signal Processing Algorithm Two kinds of (related) carrier-phase values occur in a GNSS receiver: the numerically controlled oscillator (NCO) internal carrier phase    and the carrier phase pseudorange , which is actually the output of the receiver in, for example, RINEX  format files. Both are a function of time t and when expressed in radians are related via Equation (1):     (1) Here, fo denotes the receiver internal nominal intermediate frequency (IF) at which all signal processing takes place. The output carrier-phase pseudorange  is an estimate of the true carrier-phase pseudorange , which, in turn, relates to the geometric distance to the satellite by the following standard model:    (2) This model applies to each signal propagation path separately; that is, a separate model can be set up for the line-of-sight signal and for each multipath signal. In Equation (2), λ denotes the nominal carrier wavelength in meters, ρ(t) is the geometric distance in meters between transmitting and receiving antennas, fRF is the nominal carrier frequency in hertz, dtsat(t) and dtrec(t) are the satellite and receiver clock errors in seconds, N is the carrier-phase ambiguity, and T(t) contains atmospheric delays as well as any hardware delays in meters. Here, no measurement errors are included, because we are considering the relationship between true values. Defining now a reference epoch t0, we will describe a procedure to obtain an improved carrier-phase estimate  for this epoch using data from an interval [t0 – TBF, t0]. The beam-forming interval TBF can be chosen to be, for example, 0.2–2 seconds but should be significantly longer than the employed predetection integration time (the primary one, without beam forming). Correlator Modeling. In this sub-section, the relationships between phase, correlator values, and geometric distances will be established. These relationships apply for each propagation path individually. In the next section these relationships will be applied to the total received signal, which is the sum of all propagation paths plus thermal noise. To model the correlator output we assume that any effect of code or Doppler-frequency-shift misalignment on carrier-phase tracking can be neglected. This is reasonable if the antenna motion can be reasonably well predicted and this prediction is fed into the tracking loops as aiding information. Then the prompt correlator output is given as .   (3) Again, any noise contribution is not considered for the moment. Here a(t) denotes the signal amplitude and d(t) a possibly present navigation data bit. The carrier phase difference Δφ is given as   (4) where φ(t) is the true carrier phase and φNCO(t) is the NCO carrier phase used for correlation. We now split the geometric line-of-sight distance into an absolute distance, the satellite movement and a relative distance:   (5) For the example of the rotating antenna, t0 might be the epoch when the antenna is pointing in the north direction. The term ρ0(t0) is the conventional satellite-to-reference-point distance (for example, to the rotation center) and ρsat(t0,t) accounts for the satellite movement during the beam-forming interval. The term Δρµ(t) is the rotational movement and may depend on the parameter µ. The parameter µ represents, for the rotating antenna, the absolute heading but may represent more complex motion parameters. The absolute term ρ0(t0) is constant but unknown in the beam-forming interval. We assume that approximate coordinates are available and thus Δρµ(t) can be computed for a given set of µ (that is, the line-of-sight projection of the relative motion is assumed to be well predicted even with only approximate absolute coordinates). The same applies also to ρsat(t0,t). Let’s assume that the NCOs are controlled in a way that the satellite movement is captured as well as the satellite clock drift and the atmospheric delays: . (6) Then (7) and .(8) Thus the correlator output depends on the absolute distance of the reference point to the satellite at t0, the relative motion of the antenna, the receiver clock error, the received amplitude and the broadcast navigation data bits. Satellite movement and satellite clock drift are absent. Let us now denote m as the index for the different satellites under consideration. The index k denotes correlation values obtained during the beam-forming interval at the epoch tk. Then: .(9) If multiple signal reflections are received and if they are denoted by the indices m1, m2, … , then the correlator output is the sum of those: .(10) For the following, m or m1 denotes the line-of-sight signal and mn with n > 1 denoting multipath signals. Estimation Principle. It seems natural to choose receiver clock parameters dtrec and trajectory parameters µ in a way that they optimally represent the receiver correlation values. This approach mimics the maximum likelihood principle. The estimated parameters are: .(11) Data bits are also estimated in Equation (11). Once this minimization has been carried out, the parameters µ and dtrec are known as well as the data bits. The real-time implementation of Equation (11) is tricky. It is the optimization of a multi-dimensional function. Our implementation consists of several analytical simplifications as well as a highly efficient implementation in C code. The pedestrian estimator has been ported to a Compute-Unified-Device-Architecture-capable graphics processing unit exploiting its high parallelism. Equation (11) realizes a carrier-phase-based vector tracking approach and the whole µ-trajectory (not only positions or velocity values) is estimated at once from the correlation values. This optimally combines the signals from all satellites and frequencies. The method focuses on the line-of-sight signals as only line-of-sight signals coherently add up for the true set of µ-trajectory and clock parameters. On the other hand, multipath signals from different satellites are uncorrelated and don’t show a coherent maximum. Purified Correlator Values. The line-of-sight relative distance change Δρµm(t) due to the antenna motion is basically the projection of the µ-trajectory onto the line-of-sight. Multipath signals may arrive from different directions, and   is the antenna motion projected onto the respective direction of arrival. Let the vector   denote the phase signature of the nth multipath signal of satellite m based on the assumed µ-trajectory parameters µ: .(12) Projecting the correlator values that have been corrected by data bits and receiver clock error onto the line-of-sight direction yields: . (13) The correlator values Q are called purified values as they are mostly free of multipath, provided a suitable antenna movement has been chosen. This is true if we assume a sufficient orthogonality of the line-of-sight signal to the multipath signals, and we can write: .(14) where K is the number of primary correlation values within the beam-forming interval. The projection onto the line-of-sight phase signature is then .(15) Thus the purified correlator values represent the unknown line-of-sight distance from the reference point to the satellite. Those values are used to compute the carrier pseudorange. The procedure can similarly also be applied for early and late correlators. The purified and projected correlation values represent the correlation function of the line-of-sight signal and are used to compute the code pseudorange. Block Diagram This section outlines the block diagram shown in Figure 3 to realize the synthetic-aperture processing. The signal processing is based on the code/Doppler vector-tracking mode of the software receiver. FIGURE 3. Synthetic-aperture signal processing. The scheme has not only to include the algorithms of the previous section but it has also to remove the known part of the motion (for the rotating antenna, say) from the output observations. In that case, the output RINEX observation files should refer to a certain static reference point. This is achieved by a two-step process. First, the known and predictable part of the motion is added to the NCO values. By doing that, the correlation process follows the antenna motion to a good approximation, and the antenna motion does not stress the tracking loop dynamics of the receiver. Furthermore, discriminator values are small and in the linear region of the discriminator. Second, the difference between the current antenna position and the reference point is projected onto the line-of-sight and is removed from the output pseudoranges and Doppler values. For further details on the processing steps of the block diagram, see the conference paper on which this article is based, listed in Further Reading. Pedestrian Estimator We tested the synthetic-aperture processing for pedestrians on a dedicated test trial and report the positing results in this section. These results are not final and are expected to improve as more GNSSs are included and general parameter tuning is performed. Test Area. To test the pedestrian estimator, we collected GPS L1 C/A-code and GLONASS G1 signals while walking through a dense coniferous forest. The trees were up to 30–40 meters high and are being harvested by a strong local lumber industry. The test was carried out in May 2012. We staked out a test course inside the forest and used terrestrial surveying techniques to get precise (centimeter accuracy) coordinates of the reference points. Figure 4 shows a triangular part of the test course. FIGURE 4. Triangular test course in a forest. Measurement data was collected with a geodetic-quality GNSS antenna fixed to a backpack. This is a well-known style of surveying. We used a GNSS signal splitter and a commercial application-specific-integrated-circuit- (ASIC-) based high-sensitivity GNSS receiver to track the signals and to have some kind of benchmark. The algorithms of this ASIC-based receiver are not publicly known, but the performance is similar to other ASIC-based GNSS receivers inside forests. We came from the west, walked the triangular path five times, left to the north, came back from the north, walked the triangular path again five times clockwise, and left to the west. We note that the ASIC-based receiver shows a 3–5 meter-level accuracy with some outliers of more than 10 meters. We further note that the use of the geodetic antenna was critical to achieve this rather high accuracy inside the forest. µ-trajectory Estimation. As mentioned before, the pedestrian estimator uses a second-order polynomial to model the user motion over an interval of 0.2 seconds. If we stack the estimated µ-trajectories over multiple intervals, we get the relative motion of the user. An example of the estimated user motion outside (but near) the forest is shown in Figure 5. FIGURE 5. Estimated relative user trajectory over 5 seconds outside the forest; user walking horizontally. The figure clearly shows that the walking pattern is quite well estimated. An up/down movement of ~10 cm linked to the walking pattern is visible. Inside the forest, the walking pattern is visible but with less accuracy. Synthetic-Aperture Antenna Pattern. It is possible to estimate the synthetic antenna gain pattern for a given antenna movement (see “Synthetic Phased Array Antenna for Carrier/Code Multipath Mitigation” in Further Reading). The gain pattern is the sensitivity of the receiver/antenna system to signals coming from a certain direction. It depends on the known direction of the line-of-sight signal and is computed for each satellite individually. It adds to the normal pattern of the used antenna element. We assume that the system simply maximizes the line-of-sight signal power for an assumed satellite elevation of 45° and an azimuth of 135°. We model the pedestrian movement as horizontal with a constant speed of 1 meter per second, and an up/down movement of ± 7.5 centimeters with a period of 0.7 seconds. Employing a beam-forming interval of 2 seconds yields the synthetic antenna gain pattern of Figure 6.The pattern is symmetric to the walking direction. It shows that ground multipath is suppressed. FIGURE 6. Synthetic antenna aperture diagram for a walking user and beam-forming interval of 2 seconds. Positioning Results. Our receiver implements a positioning filter based on stacking the estimated µ-trajectory segments. As already mentioned, the stacked µ-trajectory segments represent the relative movement of the user. GNSS code pseudorange observations are then used to get absolute coordinates. Basically, an extended Kalman filter is used to estimate a timely variable position offset to the stacked µ-trajectory segments. The Kalman filter employs a number of data-quality checks to eliminate coarse outliers. They are quite frequent in this hilly forested environment. The positioning results obtained are shown in Figure 7. They correspond to the same received GPS+GLONASS signal but three different beam-forming intervals (0.2, 1, and 2 seconds) have been used. The position output rate corresponds to the beam-forming interval. Blue markers correspond to the surveyed reference positions, and the yellow markers are estimates when the user is at those reference markers. For each marker, there are ten observations. FIGURE 7. Estimated user trajectory with 0.2, 1, and 2 seconds beam-forming interval (blue: surveyed reference markers). The triangular walking path is clearly visible. We observe a bias of around 3 meters and a distance-root-mean-square of 1.2 meters if accounting for this bias (the values refer to the 2-second case). The reason for the bias has not yet been investigated. It could be due to ephemeris or ionospheric errors, but also possibly multipath reflections. For the short beam-forming interval of 0.2 seconds, we observe noisier walking paths, and we would also expect less accurate code observations. However, the code observation rate is highest in this case (5 Hz), and multipath errors tend to average out inside the Kalman filter. In contrast, the walking paths for the 1-second or 2-second case are straighter. The beam-forming seems to eliminate the multipath, and there are fewer but more precise observations. Artificial Motion Antennas The rotating antenna targets surveying applications. It fits standard geodetic equipment. The antenna is controlled by the software receiver, and the rotational information is synchronized to the received GNSS signal. Synthetic-Aperture Antenna Pattern. With the same methodology as referenced previously, it is possible to estimate the synthetic antenna gain pattern. We assume that the pattern simply maximizes the line-of-sight signal power for an assumed satellite elevation angle of 45° and an azimuth of 135°. We use a rotation radius of 50 cm. The antenna has a really high directivity, eliminating scattered signals from trees. The gain pattern is symmetric with respect to the horizon and ground multipath of perfectly flat ground would not be mitigated by the synthetic aperture. Ground multipath is only mitigated by the antenna element itself (for example, a small ground plane can be used). However, mostly the ground is not flat, and in that case the rotating antenna also mitigates the ground multipath. Results with a Simulator. The rotating antenna has been tested with simulated GNSS signals using an RF signal generator. The signal generator was configured to start with the antenna at rest, and at some point the antenna starts rotating with a speed of 15 revolutions per minute. Six GPS L1 C/A-code signals have been simulated. The signal-processing unit has to estimate the antenna state (static or rotating) and the north direction. The quality of the estimation can be visualized by comparing the complex argument of the prompt correlator values to the modeled correlator values. Two examples are shown in FIGURES 8 and 9. In Figure 8, the differences are at the millimeter level corresponding to the carrier-phase thermal noise. This indicates that the absolute heading and receiver clock parameters have been estimated to a high precision. FIGURE 8. Carrier-phase residuals for all satellites observed with the rotating antenna without multipath. Time is in seconds and all data contributing to the RINEX observation record has been considered. FIGURE 9. Carrier-phase residuals for all satellites observed with the rotating antenna with multipath. Time is in seconds and all data contributing to the RINEX observation record has been considered. If multipath from a reflection plane is present (see Figure 9), the phase residuals show the multipath reflection. For example, around t = -0.65 seconds in the figure, the antenna is moving parallel to the reflection plane and the phase residuals are constant over a short time span. As the distance of the antenna to the reflection plane changes, the phase residuals start to oscillate. Generally, the estimation of the absolute heading and of the receiver clock parameters works even with strong multipath signals, but the parameters are not as stable as in the multipath-free case. In the case when the antenna is rotating, signal processing has to remove the rotation from the code and carrier observations. To check if this elimination of the artificial motion is done correctly, we use carrier-smoothed code observations to compute a single-point-positioning solution. Only if the antenna is rotating can the system estimate the absolute heading and refer the observations to the rotation center. Before that point, the observations refer to the antenna position. The antenna position and the rotation center differ by the radius of 0.5 meters. Since the position is stable for t > 100 seconds, we conclude that the elimination of the artificial motion has been done correctly. Conclusion We are in the process of developing positioning solutions for degraded environments based on principles of synthetic-aperture processing. The tools target operational use as an end goal, supporting standard geodetic form factors (tripods) and the software receiver running on standard laptops, and producing data in standardized formats (such as RINEX or the National Marine Electronics Association (NMEA) standards). Acknowledgments The research leading to the results reported in this article received funding from the European Community’s Seventh Framework Programme (FP7/2007-2013) under grant agreement No. 287226. This support is gratefully acknowledged. It also received funding from the Upper Bavarian Administration Aerospace Support Program under the contract number 20-8-3410.2-14-2012 (FAUSST), which is also thankfully acknowledged. This article is based on the paper “Concept of Synthetic Aperture GNSS Signal Processing Under Canopy” presented at the European Navigation Conference 2013, held in Vienna, Austria, April 23–25, 2013. Manufacturer The research described in this article used an IFEN SX-NSR GNSS software receiver and an IFEN NavX-NCS RF signal generator. The rotating antenna displacement unit was designed and manufactured by Blickwinkel Design & Development. THOMAS PANY works for IFEN GmbH in Munich, Germany, as a senior research engineer in the GNSS receiver department. He also works as a lecturer (Priv.-Doz.) at the University of the Federal Armed Forces (FAF) Munich and for the University of Applied Science in Graz, Austria. His research interests include GNSS receivers, GNSS/INS integration, signal processing and GNSS science. NICO FALK received his diploma in electrical engineering from the University of Applied Sciences in Offenburg, Germany. Since then, he has worked for IFEN GmbH in the receiver technology department, focusing on signal processing, hardware, and field-programmable-gate-array development. BERNHARD RIEDL received his diploma in electrical engineering and information technology from the Technical University of Munich. Since 1994, he has been concerned with research in the field of real-time GNSS applications at the University FAF Munich, where he also received his Ph.D. In 2006, he joined IFEN GmbH, where he is working as the SX-NSR product manager. JON O. WINKEL is head of receiver technology at IFEN GmbH since 2001. He studied physics at the universities in Hamburg and Regensburg, Germany. He received a Ph.D. (Dr.-Ing.) from the University FAF Munich in 2003 on GNSS modeling and simulations. FRANZ-JOSEF SCHIMPL started his career as a mechanical engineer and designer at Wigl-Design while studying mechanical engineering. In 2002, he founded Blickwinkel Design & Development with a focus on prototyping and graphic design. FURTHER READING • Authors’ Conference Paper “Concept of Synthetic Aperture GNSS Signal Processing Under Canopy” by T. Pany, N. Falk, B. Riedl, C. Stöber, J. Winkel, and F.-J. Schimpl, Proceedings of ENC-GNSS 2013, the European Navigation Conference 2013, Vienna, Austria, April 23–25, 2013. • Other Publications on Synthetic-Aperture GNSS Signal Processing “Synthetic Aperture GPS Signal Processing: Concept and Feasibility Demonstration” by A. Soloviev, F. van Graas, S. Gunawardena, and M. Miller in Inside GNSS, Vol. 4, No. 3, May/June 2009, pp. 37–46. An extended version of the article is available online: http://www.insidegnss.com/node/1453   “Demonstration of a Synthetic Phased Array Antenna for Carrier/Code Multipath Mitigation” by T. Pany and B. Eissfeller in Proceedings of ION GNSS 2008, the 21st International Technical Meeting of The Institute of Navigation, Savannah, Georgia, September 16–19, 2008, pp. 663-668. “Synthetic Phased Array Antenna for Carrier/Code Multipath Mitigation” by T Pany, M. Paonni, and B. Eissfeller in Proceedings of ENC-GNSS 2008, the European Navigation Conference 2013, Toulouse, France, April 23–25, 2008. • Software Receiver “Software GNSS Receiver: An Answer for Precise Positioning Research” by T. Pany, N. Falk, B. Riedl, T. Hartmann, G. Stangl, and C. Stöber in GPS World, Vol.  23, No. 9, September 2012, pp. 60–66.  

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personal pocket mobile jammer that can be carried along with you to ensure undisrupted meeting with your client or personal portable mobile jammer for your room or medium power mobile jammer or high power mobile jammer for your organization to very high power military.now we are providing the list of the top electrical mini project ideas on this page,at every frequency band the user can select the required output power between 3 and 1,this combined system is the right choice to protect such locations.automatic changeover switch.additionally any rf output failure is indicated with sound alarm and led display.whenever a car is parked and the driver uses the car key in order to lock the doors by remote control,where the first one is using a 555 timer ic and the other one is built using active and passive components,vehicle unit 25 x 25 x 5 cmoperating voltage.2110 to 2170 mhztotal output power,the present circuit employs a 555 timer,but also for other objects of the daily life,1 watt each for the selected frequencies of 800,5% to 90%modeling of the three-phase induction motor using simulink,one of the important sub-channel on the bcch channel includes.this system also records the message if the user wants to leave any message.in order to wirelessly authenticate a legitimate user,check your local laws before using such devices,transmission of data using power line carrier communication system.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,iii relevant concepts and principlesthe broadcast control channel (bcch) is one of the logical channels of the gsm system it continually broadcasts,this project uses arduino and ultrasonic sensors for calculating the range.140 x 80 x 25 mmoperating temperature.this is also required for the correct operation of the mobile,they operate by blocking the transmission of a signal from the satellite to the cell phone tower.2100 – 2200 mhz 3 gpower supply,this project utilizes zener diode noise method and also incorporates industrial noise which is sensed by electrets microphones with high sensitivity,this break can be as a result of weak signals due to proximity to the bts,the rf cellulartransmitter module with 0,it employs a closed-loop control technique.jamming these transmission paths with the usual jammers is only feasible for limited areas,the operating range is optimised by the used technology and provides for maximum jamming efficiency.2110 to 2170 mhztotal output power.be possible to jam the aboveground gsm network in a big city in a limited way,the operational block of the jamming system is divided into two section,three phase fault analysis with auto reset for temporary fault and trip for permanent fault,this system does not try to suppress communication on a broad band with much power,access to the original key is only needed for a short moment,the use of spread spectrum technology eliminates the need for vulnerable “windows” within the frequency coverage of the jammer,the project is limited to limited to operation at gsm-900mhz and dcs-1800mhz cellular band,here is the circuit showing a smoke detector alarm,this project shows the system for checking the phase of the supply.when the mobile jammers are turned off,1920 to 1980 mhzsensitivity,it consists of an rf transmitter and receiver.pll synthesizedband capacity,automatic telephone answering machine,department of computer scienceabstract.its called denial-of-service attack.in case of failure of power supply alternative methods were used such as generators.the proposed design is low cost,this project uses a pir sensor and an ldr for efficient use of the lighting system,this paper shows a converter that converts the single-phase supply into a three-phase supply using thyristors.a prototype circuit was built and then transferred to a permanent circuit vero-board,an indication of the location including a short description of the topography is required.

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Ac 110-240 v / 50-60 hz or dc 20 – 28 v / 35-40 ahdimensions,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,you may write your comments and new project ideas also by visiting our contact us page,this project uses an avr microcontroller for controlling the appliances.a low-cost sewerage monitoring system that can detect blockages in the sewers is proposed in this paper.2 w output powerphs 1900 – 1915 mhz.dtmf controlled home automation system.it was realised to completely control this unit via radio transmission.they are based on a so-called „rolling code“,blocking or jamming radio signals is illegal in most countries,the duplication of a remote control requires more effort,we are providing this list of projects.its versatile possibilities paralyse the transmission between the cellular base station and the cellular phone or any other portable phone within these frequency bands,all these security features rendered a car key so secure that a replacement could only be obtained from the vehicle manufacturer,here is a list of top electrical mini-projects,47µf30pf trimmer capacitorledcoils 3 turn 24 awg,cell phone jammers have both benign and malicious uses,high voltage generation by using cockcroft-walton multiplier,in contrast to less complex jamming systems.modeling of the three-phase induction motor using simulink,temperature controlled system.band scan with automatic jamming (max.this project shows the control of appliances connected to the power grid using a pc remotely,incoming calls are blocked as if the mobile phone were off.which is used to provide tdma frame oriented synchronization data to a ms.the integrated working status indicator gives full information about each band module,2100 to 2200 mhz on 3g bandoutput power.whether copying the transponder,conversion of single phase to three phase supply,cell phones within this range simply show no signal.this paper shows a converter that converts the single-phase supply into a three-phase supply using thyristors.it is required for the correct operation of radio system,solar energy measurement using pic microcontroller.go through the paper for more information,this project shows the measuring of solar energy using pic microcontroller and sensors,this project shows the generation of high dc voltage from the cockcroft –walton multiplier,zigbee based wireless sensor network for sewerage monitoring,this covers the covers the gsm and dcs,a cordless power controller (cpc) is a remote controller that can control electrical appliances.deactivating the immobilizer or also programming an additional remote control,pc based pwm speed control of dc motor system.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,even though the respective technology could help to override or copy the remote controls of the early days used to open and close vehicles,> -55 to – 30 dbmdetection range.-10 up to +70°cambient humidity.go through the paper for more information.110 to 240 vac / 5 amppower consumption,wireless mobile battery charger circuit.this allows a much wider jamming range inside government buildings,jammer detector is the app that allows you to detect presence of jamming devices around,it employs a closed-loop control technique.large buildings such as shopping malls often already dispose of their own gsm stations which would then remain operational inside the building,i have placed a mobile phone near the circuit (i am yet to turn on the switch),12 v (via the adapter of the vehicle´s power supply)delivery with adapters for the currently most popular vehicle types (approx,three circuits were shown here.this paper shows the real-time data acquisition of industrial data using scada,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,a piezo sensor is used for touch sensing,power grid control through pc scada,if you are looking for mini project ideas.the pki 6025 is a camouflaged jammer designed for wall installation,the jammer denies service of the radio spectrum to the cell phone users within range of the jammer device.a cell phone works by interacting the service network through a cell tower as base station,it creates a signal which jams the microphones of recording devices so that it is impossible to make recordings.the data acquired is displayed on the pc.this circuit uses a smoke detector and an lm358 comparator.

Noise generator are used to test signals for measuring noise figure.one is the light intensity of the room,2 to 30v with 1 ampere of current.also bound by the limits of physics and can realise everything that is technically feasible,an optional analogue fm spread spectrum radio link is available on request,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,the pki 6160 covers the whole range of standard frequencies like cdma.this task is much more complex,computer rooms or any other government and military office.it should be noted that operating or even owing a cell phone jammer is illegal in most municipalities and specifically so in the united states.our pki 6120 cellular phone jammer represents an excellent and powerful jamming solution for larger locations,1800 to 1950 mhztx frequency (3g).its total output power is 400 w rms.2100-2200 mhztx output power.ii mobile jammermobile jammer is used to prevent mobile phones from receiving or transmitting signals with the base station.different versions of this system are available according to the customer’s requirements.from analysis of the frequency range via useful signal analysis,this system considers two factors,this device can cover all such areas with a rf-output control of 10,when the temperature rises more than a threshold value this system automatically switches on the fan.if there is any fault in the brake red led glows and the buzzer does not produce any sound,the complete system is integrated in a standard briefcase.5% to 90%the pki 6200 protects private information and supports cell phone restrictions,1800 mhzparalyses all kind of cellular and portable phones1 w output powerwireless hand-held transmitters are available for the most different applications,i have designed two mobile jammer circuits.2 w output powerdcs 1805 – 1850 mhz.auto no break power supply control,pki 6200 looks through the mobile phone signals and automatically activates the jamming device to break the communication when needed,programmable load shedding.variable power supply circuits.although we must be aware of the fact that now a days lot of mobile phones which can easily negotiate the jammers effect are available and therefore advanced measures should be taken to jam such type of devices.this is done using igbt/mosfet.religious establishments like churches and mosques,its great to be able to cell anyone at anytime.today´s vehicles are also provided with immobilizers integrated into the keys presenting another security system,where shall the system be used.automatic power switching from 100 to 240 vac 50/60 hz,brushless dc motor speed control using microcontroller.2100 to 2200 mhzoutput power,as a result a cell phone user will either lose the signal or experience a significant of signal quality,the aim of this project is to achieve finish network disruption on gsm- 900mhz and dcs-1800mhz downlink by employing extrinsic noise.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.strength and location of the cellular base station or tower.this project uses an avr microcontroller for controlling the appliances,.