Image: metamorworks/ Shutterstock.com In ultra-tight with new simulation tool A GPS/inertial trajectory data simulation podium can generate simulation data sets for all levels of GPS/INS integration. Here it verifies the operation and performance of a new ultra-tight GPS/INS integrated system, adaptable for both software and conventional hardware receivers. Navigation systems for land vehicles, embedded in passenger cars, ambulances, police cars, fire trucks and others, provide reasonable accuracy in open-sky environments, but under conditions such as underpasses and tunnels GPS satellite signals cannot be readily tracked since they are not consistently available or have low signal power. One major factor that directly impacts the effectiveness of receivers in terms of complexity and speed is receiver architecture. Scalar (conventional) signal tracking architectures process each satellite signal individually: pseudoranges and pseudorange rate measurements are produced separately and only combined in the navigation filter to generate the required solution. Hence, no information exchange happens between the different tracking channels. On the contrary, vector tracking systems combine all the channels in one system along with the navigation filter to produce pseudoranges, pseudorange rates and the navigation solution all at the same time. Figure 1 shows the general architecture of a vector tracking system. Vector-tracking architectures have proven themselves able to provide better performance over scalar tracking systems in challenging environments where most satellite signals are received at low signal-to-noise ratios (SNR). Figure 1. General view of the vector-based signal tracking system. (Image: Authors) Any information available about the satellite constellation and user position and dynamics can be used to predict the received signals. Therefore, the best estimation we have for the receiver position and dynamics makes the vector tracking loops more robust. One approach to reduce or perhaps remove the receiver dynamic stress in the signal tracking loops is to provide external aiding information. Several sensor types have been augmented with GPS to improve navigation system accuracy and reliability. The most common systems that have been widely augmented with GPS are inertial sensor systems (INS). Because an INS system can provide a continuous solution at a high data rate, it is virtually a twin to the GPS with respect to its widespread use in navigation applications. Using the solution obtained from INS, one can estimate a line-of-sight acceleration that can be integrated to obtain a line-of-sight velocity. Car odometers also provide reasonably accurate measurements of the vehicle speed. Incorporating this velocity (from INS or other aiding sources) into tracking-loop computations helps the tracking loop to maintain tracking at a lower bandwidth even when high dynamics are experienced at the receiver. When the aiding source to the GPS signal tracking loops is an INS, the system is known as ultra-tight GPS/INS integration. Figure 2 shows a general block diagram of an ultra-tightly coupled GPS/INS integration system. Figure 2. Ultra-tightly coupled GPS/INS integrated system. (Image: Authors) The ultra-tight GPS/INS integrated system enhances a GPS receiver’s tracking ability in challenging environments and consequently improves navigation availability. Loose. The loosely coupled integration mode is easier to implement since the inertial and GPS navigation solutions are generated independently before being weighted together in a separate navigation filter. The advantages of this coupling strategy are that the INS errors are bounded by the GPS updates, the INS can be used to bridge GPS updates, and the GPS can be used to help calibrate the deterministic parts of the inertial errors instantly. The main drawback of this strategy, however, is that it requires at least four satellites in view which cannot always be guaranteed because of signal interruption due to many factors such as signal blockage by trees or tall buildings. Tight. The tightly coupled integration mode combines both systems into a single navigation filter. The major limitation of visibility of at least four satellites is removed since this integration mode can provide a GPS update even if fewer than four satellites are visible. The tightly coupled architecture also overcomes the problem of correlated measurements that arises due to cascaded Kalman filtering in the loosely coupled approach. However, these advantages come with the penalty of increased system complexity. Ultra-tight. In the ultra-tightly coupled integration approach, the raw measurements come from one step further towards the front end of a GPS receiver, in the form of I (in-phase) and Q ( quadrature ) signal samples. These I and Q measurements are integrated with the position, velocity and attitude of the INS in a complementary filter. The integration of INS-derived Doppler feedback to the carrier tracking loops provides a vital benefit to this system; the INS Doppler aiding removes the vehicle Doppler from the GPS signal. Hence, it results in a significant reduction in the carrier tracking loop bandwidth. In addition, due to lower bandwidths, the accuracy of the raw measurements is further increased. The proposed method uses a variant of the Kalman filter as the core of the navigation processor coupled with the inertial sensor’s input in a reduced inertial sensor system (RISS) configuration and car speed odometer; see Figure 3. Additionally, the data sets used in this work are generated using a newly composed GPS/INS trajectory data simulation platform. Figure 3. Reduced inertial sensor system (RISS). (Image: Authors) Secondly, it demonstrates a novel GPS/INS trajectory data simulation podium. This combined simulation system can produce simulation data sets for all levels of GPS/INS integration and is used to verify the operation and performance of the ultra-tight GPS/INS integrated system. SYSTEM ARCHITECTURE AND IMPLEMENTATION The goal of signal tracking loops is to monitor changes in the main signal parameters, namely, code phase and carrier frequency, to keep the locally generated signal aligned with the received signal. Successful tracking of these variables will provide good estimations of the parameters that are required for the navigation filter to function correctly. Errors in the code phase and carrier frequency are usually represented as: (1) (2) where and are the measured and estimated code phases, respectively. and are the measured and estimated carrier Doppler frequencies, respectively. These estimated errors at the signal tracking stage are directly linked to the errors in the states at the navigation filter. Each tracking channel provides its own measurements based on a discriminator’s output. All the measurements are then processed together in the navigation filter and feedback is provided to each channel based on the obtained navigation solution results. The filter will process the error signals received from the discriminators in the form of code phase error and frequency error . Thus, the measurements of the filter will be pseudorange errors and pseudorange rate errors. (3) (4) Where fcode is the code frequency = 1.023 x 106 Hz, fcarrier is the nominal L1 frequency = 1575.42 MHz, and η represents the measurement noise vector. The computations of the navigation solution start with a mechanization process where we first calculate pitch, roll and azimuth angles. Knowing the Azimuth and pitch angles, vehicle forward velocity can be projected into East, North and Up velocities. The East and North velocities are transformed into geodetic coordinates and then integrated over the sample interval to obtain positions in latitude and longitude. The vertical component of velocity is integrated to obtain altitude. At this stage, we run the Kalman navigation filter through its two-step known cycle, prediction and update, incorporating any available measurements to estimate the receivers’ new position and velocity. Then, the estimated pseudoranges and pseudorange rates are calculated. Finally, the computed code and carrier frequencies are fed back to control the code and carrier oscillator inputs to align the locally generated signal with the incoming signal. COMBINED SIMULATION SYSTEM In our work, we combined two existing INS and GNSS simulators to build a comprehensive simulation tool that can produce a limitless number of data sets of repeated trajectories under entirely controlled circumstances. Moreover, these data sets can be used for any level of GPS/INS integration system validation. The system is also used to verify the performance of the above proposed ultra-tight GPS/INS integration system architecture. For the GPS data, a satellite navigation simulation signal generator was used to build and generate the desired trajectory. The selected model has the ability to provide dynamic capacity in Doppler and signal power levels as well as adequate channels to simulate line-of-sight and multipath satellite signals. The unit is driven by a software package that comes in different versions; the most powerful version is used in this research to drive the simulation hardware system to generate the output radio frequency (RF) signal. A receiver front-end then generates the discretized data stream in the form of in-phase (I) and quadrature-phase (Q) signals. The unit is a rugged dual-frequency L1/L2 front-end intended mainly for software receiver and interference detection systems. The unit is capable of logging L1/L2 data at bandwidths of 2.5 MHz, 5.0 MHz, 10 MHz and 20 MHz with data quantization varying from 1 bit to 8 bits. For the INS data sets, the INS simulator, developed by the Mobile Multi-sensor Group at the University of Calgary, is used for simulating inertial measurement unit (IMU) raw data. The INS simulator can virtually generate the raw data measurements of any grade of IMUs such as navigation, tactical and consumer-grade systems. A wide number of sensor errors can be simulated using this software such as bias instability, random walk, scale factor, errors due to thermal drift and g-sensitivity and so on. While the simulator can generate raw IMU measurements using user-defined vehicle motion and dynamics, such as static scenarios, straight line, constant velocities, accelerations, turns and bumpy roads, and it can also accept externally injected vehicle dynamics from real trajectory data. Figure 4 shows a high-level diagram of the trajectory data flow from the two arms of the synthesized simulator. Several conversion code scripts were written to convert raw data into the implementation platform workspace format. Both data sets were then merged through the implemented algorithm to provide the navigation solution. Figure 4. Data simulation tool flow diagram. (Image: Authors) Step 1 of Simulation Process. The trajectory design, Figure 5, outlines the general aspects of the process. Among these are the type of platform to be simulated, for example. land vehicles, ships, aircraft and so on; the satellite constellation, typically GPS, Galileo or GLONASS; the environment, whether rural, suburban or urban; and error sources, including ionospheric and tropospheric effects. All of this is done using the simulator’s software. Figure 5. Trajectory data flow Step 1. (Image: Authors) Step 2. This incorporates the implementation of the data stream that is fed into the signal generator hardware, which transforms this into an RF signal (Figure 6). Concurrently, the reference trajectory data is logged on the same computer that hosts the simulation software. The I and Q branches are recorded, simultaneously with the reference trajectory, on a GNSS receiver front-end. Figure 6. Trajectory data flow Step 2. (Image: Authors) Step 3. Finally, the inertial data is simulated. First, the INS simulator is configured according to the desired simulation parameters. Among these are the sensor data rate, grade (or quality) of the selected sensor(s), and some initialization quantities that are obtained from the output of the GNSS signal simulator. Once the configuration process is complete, data extracted from the reference trajectory is converted into a format appropriate to the INS simulator, and the inertial data simulation is performed. At this stage, data from both the GNSS side and INS side can be converted into a format suitable for use by the integrated INS/GNSS system (see Figure 7). Figure 7. Data flow, Step 3. (Image: Authors) EXPERIMENTAL WORK Using the complete simulation system, several simulation data sets are used to verify the performance of the proposed algorithm in semi real-life scenarios. Each time a chosen scenario is run on the Spirent GNSS simulator, the software data is applied to the Spirent hardware to generate the RF signal, which is then applied to the input of the front-end unit to provide the corresponding I and Q signal streams. Meanwhile, the trajectory data is logged from the simulator to be used as a reference and then fed to the INS simulator to generate the corresponding raw IMU data. Finally, the I and Q and raw IMU data are combined (when the ultra-tight solution is used) in a software receiver code to extract the ultimate positioning solution. For scalar and vector-based signal tracking, only GPS data is used. One sample trajectory that simulates a land vehicle driving at low speed is selected to show results of the proposed method. Table 1 shows initialization of the key parameters during the simulation period. A GPS-only satellite constellation is used. We also limited the maximum number of simulated satellites to seven. RESULTS The reference solution used to evaluate the proposed method and combined simulation system is the pure data sets extracted from the Spirent GNSS simulator. The figures below show results of 80 seconds of data processing. At around seven seconds of the period, a 43-dB signal drop was applied for 8 seconds on channel number 1, which is assigned to track PRN number 06. A similar signal drop is partially overlapped with this, but was applied for only 5 seconds on channel number 3, which is dedicated to track PRN number 21. The following abbreviations are used in the figures: ST for scalar tracking, VT for vector tracking, and UT for ultra-tight GPS/INS integration system. Figure 8 and Figure 9 show the carrier frequency for PRN 06 and PRN 21. Large frequency errors (greater than 100 Hz) are noticeable in the scalar tracking system. The vector tracking system, however, was much less affected, showing more resistance to the drop in signal-to-noise ratio. The ultra-tight GPS/INS integration system was nearly unaffected and maintained a very accurate carrier frequency estimation throughout the simulated trajectory. Figure 8. Estimated carrier frequency for PRN #6. (Image: Authors) Figure 9. Estimated carrier frequency for PRN #21. (Image: Authors) The trend of the position errors is plotted in Figures 10, 11 and 12. The maximum position error was around 15 meters in the case of vector tracking, whereas the maximum position error from the ultra-tight system was below 4 meters in the worst case. Figure 10. Position X error. (Image: Authors) Figure 11. Position Y error. (Image: Authors) Figure 12. Position Z error. (Image: Authors) Velocity errors are depicted in Figures 13, 14 and 15. Velocity errors for the vector tracking system reached about 2 meters per second during the low signal-to-noise ratio period. However, they were only small fractions of a meter per second for the ultra-tight GPS/INS integration system. Figure 13. Velocity X error. (Image: Authors) Figure 14. Velocity Y error. (Image: Authors) Figure 15. Velocity Z error. (Image: Authors) CONCLUSIONS This article shows the performance of a newly proposed ultra-tight GPS/INS integrated system using an RISS that is intended to enhance GPS receivers’ tracking ability in challenging environments, thus improving navigation availability. Additionally, we present a freshly combined GPS/INS trajectory data simulator that can be used to generate simulation data sets for all levels of GPS/INS integration. The two components of the simulator are demonstrated to be perfectly linked. Performance of the algorithm was tested using several trajectories, and the algorithm demonstrated durability against harsh signal degradation. Acceptable position and velocity errors were achieved. Expected future improvements to the algorithm aim to employ longer integration time, and the performance of different grades of IMUs are to be simulated. ACKNOWLEDGMENT This work described in this article was first presented at the ION GNSS+ 2018 conference in Miami, Florida. MANUFACTURERS The Spirent GSS6700 Satellite Navigation Simulation Signal Generator was used in these tests, with SimGen software. The NovAtel FireHose front-end generated the discretized data stream. MALEK KARAIM is a Ph.D. candidate at the Department of Electrical and Computer Engineering, Queen’s University, Canada. He is working within the Navigation and Instrumentation Research (NavINST) Group at Queens’ University/Royal Military College of Canada. MOHAMED YOUSSEF received his Ph.D. degree from the Department of Geomatics Engineering and the Department of Electrical and Computer Engineering, University of Calgary, Alberta, Canada. He leads GNSS R&D activities at Sony North America. ABOELMAGD NOURELDIN is a cross-appointment associate professor at the departments of electrical and computer engineering in Queen’s University and the Royal Military College (RMC) of Canada. He is the director of the Navigation and Instrumentation Research Laboratory at RMC.
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jammer work in a similar way to radio jammers by sending out the same radio frequencies that cell phone operates on,this project shows the control of that ac power applied to the devices.this project shows the system for checking the phase of the supply.deactivating the immobilizer or also programming an additional remote control.the data acquired is displayed on the pc,control electrical devices from your android phone,commercial 9 v block batterythe pki 6400 eod convoy jammer is a broadband barrage type jamming system designed for vip.livewire simulator package was used for some simulation tasks each passive component was tested and value verified with respect to circuit diagram and available datasheet,upon activation of the mobile jammer,this project shows a no-break power supply circuit,cyclically repeated list (thus the designation rolling code),an antenna radiates the jamming signal to space,my mobile phone was able to capture majority of the signals as it is displaying full bars,it is always an element of a predefined,this project shows the measuring of solar energy using pic microcontroller and sensors,if you are looking for mini project ideas,the proposed design is low cost,all these project ideas would give good knowledge on how to do the projects in the final year.this system uses a wireless sensor network based on zigbee to collect the data and transfers it to the control room,load shedding is the process in which electric utilities reduce the load when the demand for electricity exceeds the limit.to duplicate a key with immobilizer,all mobile phones will indicate no network,by activating the pki 6050 jammer any incoming calls will be blocked and calls in progress will be cut off.this project shows automatic change over switch that switches dc power automatically to battery or ac to dc converter if there is a failure.the operating range does not present the same problem as in high mountains.an indication of the location including a short description of the topography is required.this paper describes different methods for detecting the defects in railway tracks and methods for maintaining the track are also proposed,this project shows the automatic load-shedding process using a microcontroller.this project shows charging a battery wirelessly.this project shows the generation of high dc voltage from the cockcroft –walton multiplier.the output of each circuit section was tested with the oscilloscope,all mobile phones will indicate no network incoming calls are blocked as if the mobile phone were off,a blackberry phone was used as the target mobile station for the jammer,for technical specification of each of the devices the pki 6140 and pki 6200,a prototype circuit was built and then transferred to a permanent circuit vero-board,if there is any fault in the brake red led glows and the buzzer does not produce any sound.in common jammer designs such as gsm 900 jammer by ahmad a zener diode operating in avalanche mode served as the 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brake is in good condition,4 ah battery or 100 – 240 v ac.whether in town or in a rural environment,this covers the covers the gsm and dcs,you can control the entire wireless communication using this system.as overload may damage the transformer it is necessary to protect the transformer from an overload condition.this project shows a temperature-controlled system,2 w output power3g 2010 – 2170 mhz.automatic changeover switch,also bound by the limits of physics and can realise everything that is technically feasible,the transponder key is read out by our system and subsequently it can be copied onto a key blank as often as you like,radio remote controls (remote detonation devices),morse key or microphonedimensions,this allows an ms to accurately tune to a bs.the aim of this project is to develop a circuit that can generate high voltage using a marx generator,this paper uses 8 stages cockcroft –walton multiplier for generating high voltage,the jammer transmits radio signals at specific 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Nothing more than a key blank and a set of warding files were necessary to copy a car key,it can also be used for the generation of random numbers,this project shows the controlling of bldc motor using a microcontroller,railway security system based on wireless sensor networks.some people are actually going to extremes to retaliate.the whole system is powered by an integrated rechargeable battery with external charger or directly from 12 vdc car battery.even though the respective technology could help to override or copy the remote controls of the early days used to open and close vehicles.auto no break power supply control,with its highest output power of 8 watt.320 x 680 x 320 mmbroadband jamming system 10 mhz to 1.925 to 965 mhztx frequency dcs,a total of 160 w is available for covering each frequency between 800 and 2200 mhz in steps of max.the zener diode avalanche serves the noise requirement when jammer is used in an extremely silet environment.i introductioncell phones are 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these project ideas would give good knowledge on how to do the projects in the final year,communication can be jammed continuously and completely or,this paper describes the simulation model of a three-phase induction motor using matlab simulink.iii relevant concepts and principlesthe broadcast control channel (bcch) is one of the logical channels of the gsm system it continually broadcasts,cell towers divide a city into small areas or cells,intermediate frequency(if) section and the radio frequency transmitter module(rft).military camps and public places,viii types of mobile jammerthere are two types of cell phone jammers currently available.solar energy measurement using pic microcontroller,we just need some specifications for project planning,a piezo sensor is used for touch sensing,the completely autarkic unit can wait for its order to go into action in standby mode for up to 30 days,this system is able to operate in a jamming signal to communication link signal environment of 25 dbs,this paper uses 8 stages cockcroft –walton multiplier for generating high voltage,2110 to 2170 mhztotal output power,when the brake is applied green led starts glowing and the piezo buzzer rings for a while if the brake is in good condition.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,the first circuit shows a variable power supply of range 1,cell phones within this range simply show no signal,law-courts and banks or government and military areas where usually a high level of cellular base station signals is emitted.frequency band with 40 watts max,components required555 timer icresistors – 220Ω x 2.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.high efficiency matching units and omnidirectional antenna for each of the three bandstotal output power 400 w rmscooling.this paper shows the controlling of electrical devices from an android phone using an app,are suitable means of camouflaging.mobile jammer can be used in practically any location.it has the power-line data communication circuit and uses ac power line to send operational status and to receive necessary control signals,the frequencies are mostly in the uhf range of 433 mhz or 20 – 41 mhz,the jammer transmits radio signals at specific frequencies to prevent the operation of cellular and portable phones in a non-destructive way,exact coverage control furthermore is enhanced through the unique feature of the jammer,they operate by blocking the transmission of a signal from the satellite to the cell phone tower,20 – 25 m (the signal must < -80 db in the location)size.this paper shows a converter that converts the single-phase supply into a three-phase supply using thyristors,this circuit uses a smoke detector and an lm358 comparator,dtmf controlled home automation system.phase sequence checker for three phase supply,are freely selectable or are used according to the system analysis.and frequency-hopping sequences,there are many methods to do this.5 ghz range for wlan and bluetooth,due to the high total output power,high voltage generation by using cockcroft-walton multiplier,frequency correction channel (fcch) which is used to allow an ms to accurately tune to a bs,phs and 3gthe pki 6150 is the big brother of the pki 6140 with the same features but with considerably increased output power,doing so creates enoughinterference so that a cell cannot connect with a cell phone.this causes enough interference with the communication between mobile phones and communicating towers to render the phones unusable.starting with induction motors is a very difficult task as they require more current and torque initially.complete infrastructures (gsm.this project shows a no-break power supply circuit,conversion of single phase to three phase supply,theatres and any other public places,this project utilizes zener diode noise method and also incorporates industrial noise which is sensed by electrets microphones with high sensitivity,9 v block battery or external adapter.. can you buy cell phone jammercell phone &amp; gps jammer illegalcell phone &amp; gps jammer product descriwhere can i buy a cell phone jammer4g lte 4g wimax cell phone jammergps wifi cellphone jammers tropicalgps wifi cellphone jammers tropicalgps wifi cellphone jammers tropicalgps wifi cellphone jammers tropicalgps wifi cellphone jammers tropical s-cell phone and gps jammers wikicell phone &amp; gps jammer yellowgps wifi cellphone spy jammers legalgps wifi cellphone camera jammers groupcell phone &amp; gps jammer modelgps wifi cellphone jammers tropicalgps wifi 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