2021/03/10
Mitigation Through Adaptive Filtering for Machine Automation Applications
By Luis Serrano, Don Kim, and Richard B. Langley
Multipath is real and omnipresent, a detriment when GPS is used for positioning, navigation, and timing. The authors look at a technique to reduce multipath by using a pair of antennas on a moving vehicle together with a sophisticated mathematical model. This reduces the level of multipath on carrier-phase observations and thereby improves the accuracy of the vehicle’s position.
INNOVATION INSIGHTS by Richard Langley
“OUT, DAMNED MULTIPATH! OUT, I SAY!” Many a GPS user has wished for their positioning results to be free of the effect of multipath. And unlike Lady Macbeth’s imaginary blood spot, multipath is real and omnipresent. Although it may be considered beneficial when GPS is used as a remote sensing tool, it is a detriment when GPS is used for positioning, navigation, and timing — reducing the achievable accuracy of results.
Clearly, the best way to reduce the effects of multipath is to try avoiding it in the first place by siting the receiver’s antenna as low as possible and far away from potential reflectors. But that’s not always feasible. The next best approach is to reduce the level of the multipath signal entering the receiver by attenuating it with a suitably designed antenna. A large metallic ground plane placed beneath an antenna will modify the shape of the antenna’s reception pattern giving it reduced sensitivity to signals arriving at low elevation angles and from below the antenna’s horizon. So-called choke-ring antennas also significantly attenuate multipath signals. And microwave-absorbing materials appropriately placed in an antenna’s vicinity can also be beneficial.
Multipath can also be mitigated by special receiver correlator designs. These designs target the effect of multipath on code-phase measurements and the resulting pseudorange observations. Several different proprietary implementations in commercial receivers significantly reduce the level of multipath in the pseudoranges and hence in pseudorange-based position and time estimates. Some degree of multipath attenuation can be had by using the low-noise carrier-phase measurements to smooth the pseudoranges before they are processed. The effect of multipath on carrier phases is much smaller than that on pseudoranges. In fact, it is limited to only one-quarter of the carrier wavelength when the reflected signal’s amplitude is less than that of the direct signal. This means that at the GPS L1 frequency, the multipath contamination in a carrier-phase measurement is at most about 5 centimeters. Nevertheless, this is still unacceptably large for some high-accuracy applications.
At a static site, with an unchanging multipath environment, the signal reflection geometry repeats day to day and the effect of multipath can be reduced by sidereal filtering or “stacking” of coordinate or carrier-phase-residual time series. However, this approach is not viable for scenarios where the receiver and antenna are moving such as in machine control applications. Here an alternative approach is needed.
In this month’s column, I am joined by two of my UNB colleagues as we look at a technique that uses a pair of antennas on a moving vehicle together with a sophisticated mathematical model, to reduce the level of multipath on carrier-phase observations and thereby improve the accuracy of the vehicle’s position.
Real-time-kinematic (RTK) GNSS-based machine automation systems are starting to appear in the construction and mining industries for the guidance of dozers, motor graders, excavators, and scrapers and in precision agriculture for the guidance of tractors and harvesters. Not only is the precise and accurate position of the vehicle needed but its attitude is frequently required as well.
Previous work in GNSS-based attitude systems, using short baselines (less than a couple of meters) between three or four antennas, has provided results with high accuracies, most of the time to the sub-degree level in the attitude angles. If the relative position of these multiple antennas can be determined with real-time centimeter-level accuracy using the carrier-phase observables (thus in RTK-mode), the three attitude parameters (the heading, pitch, and roll angles) of the platform can be estimated.
However, with only two GNSS antennas it is still possible to determine yaw and pitch angles, which is sufficient for some applications in precision agriculture and construction. Depending on the placement of the antennas on the platform body, the determination of these two angles can be quite robust and efficient.
Nevertheless, even a small separation between the antennas results in different and decorrelated phase-multipath errors, which are not removed by simply differencing measurements between the antennas.
The mitigation of carrier-phase multipath in real time remains, to a large extent, very limited (unlike the mitigation of code multipath through receiver improvements) and it is commonly considered the major source of error in GNSS-RTK applications. This is due to the very nature of multipath spectra, which depends mainly on the location of the antenna and the characteristics of the reflector(s) in its vicinity. Any change in this binomial (antenna/reflectors), regardless of how small it is, will cause an unknown multipath effect.
Using typical choke-ring antennas to reduce multipath is typically not practical (not to mention cost prohibitive) when employing multiple antennas on dynamic platforms. Extended flat ground planes are also impractical. Furthermore, such antenna configurations typically only reduce the effects of low angle reflections and those coming from below the antenna horizon.
One promising approach to mitigating the effects of carrier-phase multipath is to filter the raw measurements provided by the receiver. But, unlike the scenario at a fixed site, the multipath and its effects are not repeatable. In machine automation applications, the machinery is expected to perform complex and unpredictable maneuvers; therefore the removal of carrier-phase multipath should rely on smart digital filtering techniques that adapt not only to the background multipath (coming mostly from the machine’s reflecting surfaces), but also to the changing multipath environment along the machine’s path.
In this article, we describe how a typical GPS-based machine automation application using a dual-antenna system is used to calibrate, in a first step, and then remove carrier-phase multipath afterwards. The intricate dynamical relationship between the platform’s two “rover” antennas and the changing multipath from nearby reflectors is explored and modeled through several stochastic and dynamical models. These models have been implemented in an extended Kalman filter (EKF).
MIMICS Strategy
Any change in the relative position between a pair of GNSS antennas most likely will affect, at a small scale, the amplitude and polarization of the reflected signals sensed by the antennas (depending on their spacing). However, the phase will definitely change significantly along the ray trajectories of the plane waves passing through each of the antennas.
This can be seen in the equation that describes the single-difference multipath between two close-by antennas (one called the “master” and the other the “slave”):
(1)
where the angle is the relative multipath phase delay between the antennas and a nearby effective reflector (α0 is the multipath signal amplitude in the master and slave antennas, and is dependent on the reflector characteristics, reflection coefficient, and receiver tracking loop).
As our study has the objective to mimic as much as possible the multipath effect from effective reflectors in kinematic scenarios with variable dynamics, we decided to name the strategy MIMICS, a slightly contrived abbreviation for “Multipath profile from between receIvers dynaMICS.”
The MIMICS algorithm for a dual-antenna system is based on a specular reflector ray-tracing multipath model (see Figure 1).
Figure 1. 3D ray-tracing modeling of phase multipath for a GNSS dual-antenna system. 0 designates the “master” antenna; 1, the “slave” antenna; Elev and Az, the elevation angle and the azimuth of the satellite, respectively. The other symbols are explained in the text.
After a first step of data synchronization and data-snooping on the data provided by the two receiver antennas, the second step requires the calculation of an approximate position for both antennas, relaxed to a few meters using a standard code solution.
A precise estimation of both antennas’ velocity and acceleration (in real time) is carried out using the carrier-phase observable. Not only should the antenna velocity and acceleration estimates be precisely determined (on the order of a few millimeters per second and a few millimeters per second squared, respectively) but they should also be immune to low-frequency multipath signatures. This is important in our approach, as we use the antennas’ multipath-free dynamic information to separate the multipath in the raw data.
We will start from the basic equations used to derive the single-difference multipath observables.
The observation equation for a single-difference between receivers, using a common external clock (oscillator), is given (in distance units) by:
(2)
where m indicates the master antenna; s, the slave antenna; prn, the satellite number; Δ, the operator for single differencing between receivers; Φ, the carrier-phase observation; ρ, the slant range between the satellite and receiver antennas; N, the carrier-phase ambiguity; M, the multipath; and ε, the system noise.
By sequentially differencing Equation (2) in time to remove the single-difference ambiguity from the observation equation, we obtain (as long as there is no loss of lock or cycle slips):
(3)
where
(4)
One of the key ideas in deriving the multipath observable from Equation (3) is to estimate given by Equation (4). We will outline our approach in a later section.
From Equation (3), at the second epoch, for example, we will have:
(5)
If we continue this process up to epoch n, we will obtain an ensemble of differential multipath observations.
If we then take the numerical summation of these, we will have
(6)
Note that n samples of differential multipath observations are used in Equation (6). Therefore, we need n + 1 observations.
Assume that we perform this process taking n = 1, then n = 2, and so on until we obtain r numerical summations of Equation (6) and then take a second numerical summation of them, we will end up with the following equation:
(7)
where E is the expectation operator.
Another key idea in our approach is associated with Equation (7). To isolate the initial epoch multipath, , from the differential multipath observations, the first term on the right-hand side of Equation (7), , should be removed.
This can be accomplished by mechanical calibration and/or numerical randomization. To summarize the idea, we have to create random multipath physically (or numerically) at the initialization step. When the isolation of the initial multipath epoch is completed, we can recover multipath at every epoch using Equation (5).
Digital Differentiators. We introduce digital differentiators in our approach to derive higher order range dynamics (that is, range rate, range-rate change, and so on) using the single-difference (between receivers connected to a common external oscillator) carrier-phase observations. These higher order range dynamics are used in Equation (4).
There are important classes of finite-impulse-response differentiators, which are highly accurate at low to medium frequencies. In central-difference approximations, both the backward and the forward values of the function are used to approximate the current value of the derivative.
Researchers have demonstrated that the coefficients of the maximally linear digital differentiator of order 2N + 1 are the same as the coefficients of the easily computed central-difference approximation of order N.
Another advantage of this class is that within a certain maximum allowable ripple on the amplitude response of the resultant differentiator, its pass band can be dramatically increased. In our approach, this is something fundamental as the multipath in kinematic scenarios is conceptually treated as high-frequency correlated multipath, depending on the platform dynamics and the distance to the reflector(s).
Adaptive Estimation. To derive single-difference multipath at the initial epoch, , a numerical randomization (or mechanical calibration) of the single-difference multipath observations is performed in our approach. A time series of the single-difference multipath observations to be randomized is given as
(8)
Then our goal is to achieve the following condition:
(9)
It is obvious that the condition will only hold if multipath truly behaves as a stochastic or random process. The estimation of multipath in a kinematic scenario has to be understood as the estimation of time-correlated random errors. However, there is no straightforward way to find the correlation periods and model the errors.
Our idea is to decorrelate the between-antenna relative multipath through the introduction of a pseudorandom motion. As one cannot completely rely only on a decorrelation through the platform calibration motion, one also has to do it through the mathematical “whitening” of the time series.
Nevertheless, the ensemble of data depicted in the above formulation can be modeled as an oscillatory random process, for which second or higher order autoregressive (AR) models can provide more realistic modeling in kinematic scenarios. (An autoregressive process is simply another name for a linear difference equation model where the input or forcing function is white Gaussian noise.) We can estimate the parameters of this model in real time, in a block-by-block analysis using the familiar Yule-Walker equations. A whitening filter can then be formed from the estimation parameters.
We obtain the AR coefficients using the autocorrelation coefficient vector of the random sequences. Since the order of the coefficient estimation depends on the multipath spectra (in turn dependent on the platform dynamics and reflector distance), MIMICS uses a cost function to estimate adaptively, in real time, the appropriate order. An order too low results in a poor whitener of the background colored noise, while an order too large might affect the embedded original signal that we are interested in detecting.
The cost function uses the residual sum of squared error. The order estimate that gives the lowest error is the one chosen, and this task is done iteratively until it reaches a minimum threshold value. Once this stage is fulfilled, the multipath observable can be easily obtained.
Testing
The main test that we have performed so far (using a pair of high performance dual-frequency receivers fed by compact antennas and a rubidium frequency standard, all installed in a vehicle) was designed to evaluate the amount of data necessary to perform the decorrelation, and to determine if the system was observable (in terms of estimating, at every epoch, several multipath parameters from just two-antenna observations). Receiver data was collected and post-processed (so-called RTK-style processing) although, with sufficient computing power, data processing could take place in real, or near real, time.
In a real-life scenario, the platform pseudorandom motions have the advantage that carrier-phase embedded dynamics are typically changing faster and in a three-dimensional manner (antennas sense different pitch and yaw angles). Thus a faster and more robust decorrelation is possible.
One can see from the bottom picture in Figure 2 the façade of the building behaving as the effective reflector. The vehicle performed several motions, depicted in the bottom panel of Figure 3, always in the visible parking lot, hence the building constantly blocked the view to some satellites. We used only the L1 data from the receivers recorded at a rate of 10 Hz.
In the bottom panel of Figure 3, one can also see the kind of motion performed by the platform. Accelerations, jerk, idling, and several stops were performed on purpose to see the resultant multipath spectra differences between the antennas. The reference station (using a receiver with capabilities similar to those in the vehicle) was located on a roof-top no more than 110 meters away from the vehicle antennas during the test. As such, most of the usual biases where removed from the solution in the differencing process and the only remaining bias can be attributed to multipath. The data from the reference receiver was only used to obtain the varying baseline with respect to the vehicle master antenna.
In the top panel of Figure 3, one can see the geometric distance calculated from the integer-ambiguity-fixed solutions of both antenna/receiver combinations. Since the distance between the mounting points on the antenna-support bar was accurately measured before the test (84 centimeters), we had an easy way to evaluate the solution quality. The “outliers” seen in the figure come from code solutions because the building mentioned before blocked most of the satellites towards the southeast. As a result, many times fewer than five satellites were available.
Figure 3. Correlation between vehicle dynamics (heading angle) and the multipath spectra.
Looking at the first nine minutes of results in Figure 4, one can see that when the vehicle is still stationary, the multipath has a very clear quasi-sinusoidal behavior with a period of a few minutes. Also, one can see that it is zero-mean as expected (unlike code multipath). When the vehicle starts moving (at about the four-minute mark), the noise figure is amplified (depending on the platform velocity), but one can still see a mixture of low-frequency components coming from multipath (although with shorter periods).
These results indicate, firstly, that regardless of the distance between two antennas, multipath will not be eliminated after differencing, unlike some other biases. Secondly, when the platform has multiple dynamics, multipath spectra will change accordingly starting from the low-frequency components (due to nearby reflectors) towards the high-frequency ones (including diffraction coming from the building edges and corners). As such, our approach to adaptively model multipath in real time as a quasi-random process makes sense.
Figure 4. Position results from the kinematic test, showing the estimated distance between the two vehicle antennas (upper plot) and the distance between the master antenna and the reference antenna.
Multipath Observables. The multipath observables are obtained through the MIMICS algorithm. It is quite flexible in terms of latency and filter order when it comes to deriving the observables. Basically, it is dependent on the platform dynamics and the amplitude of the residuals of the whitened time series (meaning that if they exceed a certain threshold, then the filtering order doesn’t fit the data).
When comparing the observations delivered every half second for PRN 5 with the ones delivered every second, it is clear that the larger the interval between observations, the better we are able to recover the true biased sinusoidal behavior of multipath. However, in machine control, some applications require a very low latency. Therefore, there must be a compromise between the multipath observable accuracy and the rate at which it is generated.
Multipath Parameter Estimation. Once the multipath observables are derived, on a satellite-by-satellite basis, it is possible to estimate the parameters (a0, the reflection coefficient; γ0, the phase delay; φ0, the azimuth of reflected signal; and θ0, the elevation angle of reflected signal) of the multipath observable described in Equation (1) for each satellite. As mentioned earlier, an EKF is used for the estimation procedure.
When the platform experiences higher dynamics, such as rapid rotations, acceleration is no longer constant and jerk is present. Therefore, a Gauss-Markov model may be more suitable than other stochastic models, such as random walk, and can be implemented through a position-velocity-acceleration dynamic model.
As an example, the results from the multipath parameter estimation are given for satellite PRN 5 in Figure 5. One can see that it takes roughly 40 seconds for the filter to converge. This is especially seen in the phase delay.
Converted to meters, the multipath phase delay gives an approximate value of 10 meters, which is consistent with the distance from the moving platform to the dominant specular reflector (the building’s façade).
Figure 5. PRN 5 multipath parameter estimation.
Multipath Mitigation. After going through all the MIMICS steps,
from the initial data tracking and synchronization between the dual-antenna system up to the multipath parameter estimation for each continuously observed satellite, we can now generate the multipath corrections and thus correct each raw carrier-phase observation.
One can see in Figure 6 three different plots from the solution domain depicting the original raw (multipath-contaminated) GPS-RTK baseline up-component (top), the estimated carrier-phase multipath signal (middle), and the difference between the two above time series; that is, the GPS-RTK multipath-ameliorated solution (bottom). A clear improvement is visible. In terms of numbers, and only considering the results “cleaned” from outliers and differential-code solutions (provided by the RTK post-processing software, when carrier-phase ambiguities cannot be fixed), the up-component root-mean-square value before was 2.5 centimeters, and after applying MIMICS it stood at 1.8 centimeters.
Figure 6. MIMICS algorithm results for the vehicle baseline from the first 9 minutes of the test.
Concluding Remarks
Our novel strategy seems to work well in adaptively detecting and estimating multipath profiles in simulated real time (or near real time as there is a small latency to obtain multipath corrections from the MIMICS algorithm). The approach is designed to be applied in specular-rich and varying multipath environments, quite common at construction sites, harbors, airports, and other environments where GNSS-based heading systems are becoming standard. The equipment setup can be simplified, compared to that used in our test, if a single receiver with dual-antenna inputs is employed.
Despite its success, there are some limitations to our approach. From the plots, it’s clear that not all multipath patterns were removed, even though the improvements are notable. Moreover, estimating multipath adaptively in real time can be a problem from a computational point of view when using high update rates. And when the platform is static and no previous calibration exists, the estimation of multipath parameters is impossible as the system is not observable. Nevertheless, the approach shows promise and real-world tests are in the planning stages.
Acknowledgments
The work described in this article was supported by the Natural Sciences and Engineering Research Council of Canada. The article is based on a paper given at the Institute of Electrical and Electronics Engineers / Institute of Navigation Position Location and Navigation Symposium 2010, held in Indian Wells, California, May 6–8, 2010.
Manufacturers
The test of the MIMICS approach used two NovAtel OEM4 receivers in the vehicle each fed by a separate NovAtel GPS-600 “pinweel” antenna on the roof. A Temex Time (now Spectratime) LPFRS-01/5M rubidium frequency standard supplied a common oscillator frequency to both receivers. The reference receiver was a Trimble 5700, fed by a Trimble Zephyr geodetic antenna.
Luis Serrano is a senior navigation engineer at EADS Astrium U.K., in the Ground Segment Group, based in Portsmouth, where he leads studies and research in GNSS high precision applications and GNSS anti-jamming/spoofing software and patents. He is also a completing his Ph.D. degree at the University of New Brunwick (UNB), Fredericton, Canada.
Don Kim is an adjunct professor and a senior research associate in the Department of Geodesy and Geomatics Engineering at UNB where he has been doing research and teaching since 1998. He has a bachelor’s degree in urban engineering and an M.Sc.E. and Ph.D. in geomatics from Seoul National University. Dr. Kim has been involved in GNSS research since 1991 and his research centers on high-precision positioning and navigation sensor technologies for practical solutions in scientific and industrial applications that require real-time processing, high data rates, and high accuracy over long ranges with possible high platform dynamics.
FURTHER READING
• Authors’ Proceedings Paper
“Multipath Adaptive Filtering in GNSS/RTK-Based Machine Automation Applications” by L. Serrano, D. Kim, and R.B. Langley in Proceedings of PLANS 2010, IEEE/ION Position Location and Navigation Symposium, Indian Wells, California, May 4–6, 2010, pp. 60–69, doi: 10.1109/PLANS.2010.5507201.
• Pseudorange and Carrier-Phase Multipath Theory and Amelioration Articles from GPS World
“It’s Not All Bad: Understanding and Using GNSS Multipath” by A. Bilich and K.M. Larson in GPS World, Vol. 20, No. 10, October 2009, pp. 31–39.
“Multipath Mitigation: How Good Can It Get with the New Signals?” by L.R. Weill, in GPS World, Vol. 14, No. 6, June 2003, pp. 106–113.
“GPS Signal Multipath: A Software Simulator” by S.H. Byun, G.A. Hajj, and L.W. Young in GPS World, Vol. 13, No. 7, July 2002, pp. 40–49.
“Conquering Multipath: The GPS Accuracy Battle” by L.R. Weill, in GPS World, Vol. 8, No. 4, April 1997, pp. 59–66.
• Dual Antenna Carrier-phase Multipath Observable
“A New Carrier-Phase Multipath Observable for GPS Real-Time Kinematics Based on Between Receiver Dynamics” by L. Serrano, D. Kim, and R.B. Langley in Proceedings of the 61st Annual Meeting of The Institute of Navigation, Cambridge, Massachusetts, June 27–29, 2005, pp. 1105–1115.
“Mitigation of Static Carrier Phase Multipath Effects Using Multiple Closely-Spaced Antennas” by J.K. Ray, M.E. Cannon, and P. Fenton in Proceedings of ION GPS-98, the 11th International Technical Meeting of the Satellite Division of The Institute of Navigation, Nashville, Tennessee, September 15–18, 1998, pp. 1025–1034.
• Digital Differentiation
“Digital Differentiators Based on Taylor Series” by I.R. Khan and R. Ohba in the Institute of Electronics, Information and Communication Engineers (Japan) Transactions on Fundamentals of Electronics, Communications and Computer Sciences, Vol. E82-A, No. 12, December 1999, pp. 2822–2824.
• Autoregressive Models and the Yule-Walker Equations
Random Signals: Detection, Estimation and Data Analysis by K.S. Shanmugan and A.M. Breipohl, published by Wiley, New York, 1988.
• Kalman Filtering and Dynamic Models
Introduction to Random Signals and Applied Kalman Filtering: with MATLAB Exercises and Solutions, 3rd edition, by R.G. Brown and P.Y.C. Hwang, published by Wiley, New York, 1997.
“The Kalman Filter: Navigation’s Integration Workhorse” by L.J. Levy in GPS World, Vol. 8, No. 9, September 1997, pp. 65–71.
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The scope of this paper is to implement data communication using existing power lines in the vicinity with the help of x10 modules,the present circuit employs a 555 timer.cell phones within this range simply show no signal.overload protection of transformer.due to the high total output power.with our pki 6670 it is now possible for approx,wifi) can be specifically jammed or affected in whole or in part depending on the version.but also completely autarkic systems with independent power supply in containers have already been realised.so to avoid this a tripping mechanism is employed.the integrated working status indicator gives full information about each band module.theatres and any other public places,which is used to test the insulation of electronic devices such as transformers.overload protection of transformer,specificationstx frequency,there are many methods to do this,for any further cooperation you are kindly invited to let us know your demand.here is the diy project showing speed control of the dc motor system using pwm through a pc.ac 110-240 v / 50-60 hz or dc 20 – 28 v / 35-40 ahdimensions,railway security system based on wireless sensor networks,but with the highest possible output power related to the small dimensions,this system uses a wireless sensor network based on zigbee to collect the data and transfers it to the control room,this project shows charging a battery wirelessly,90 % of all systems available on the market to perform this on your own.15 to 30 metersjamming control (detection first).the control unit of the vehicle is connected to the pki 6670 via a diagnostic link using an adapter (included in the scope of supply),pc based pwm speed control of dc motor system.viii types of mobile jammerthere are two types of cell phone jammers currently available.40 w for each single frequency band,we – in close cooperation with our customers – work out a complete and fully automatic system for their specific demands.depending on the already available security systems,thus it was possible to note how fast and by how much jamming was established.these jammers include the intelligent jammers which directly communicate with the gsm provider to block the services to the clients in the restricted areas,ac power control using mosfet / igbt,the light intensity of the room is measured by the ldr sensor,5 kgkeeps your conversation quiet and safe4 different frequency rangessmall sizecovers cdma,rs-485 for wired remote control rg-214 for rf cablepower supply,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 the control of home appliances using dtmf technology.from the smallest compact unit in a portable.the jammer denies service of the radio spectrum to the cell phone users within range of the jammer device,here is a list of top electrical mini-projects,integrated inside the briefcase,this system considers two factors,for such a case you can use the pki 6660,is used for radio-based vehicle opening systems or entry control systems.a mobile jammer circuit is an rf transmitter,so that pki 6660 can even be placed inside a car.when shall jamming take place,a total of 160 w is available for covering each frequency between 800 and 2200 mhz in steps of max.
Zigbee based wireless sensor network for sewerage monitoring,this project shows a temperature-controlled system,here is the circuit showing a smoke detector alarm.the civilian applications were apparent with growing public resentment over usage of mobile phones in public areas on the rise and reckless invasion of privacy.when the brake is applied green led starts glowing and the piezo buzzer rings for a while if the brake is in good condition,ii mobile jammermobile jammer is used to prevent mobile phones from receiving or transmitting signals with the base station,this can also be used to indicate the fire,dtmf controlled home automation system.2 – 30 m (the signal must < -80 db in the location)size,4 ah battery or 100 – 240 v ac,mainly for door and gate control,the operating range is optimised by the used technology and provides for maximum jamming efficiency.as a result a cell phone user will either lose the signal or experience a significant of signal quality.here is a list of top electrical mini-projects,by this wide band jamming the car will remain unlocked so that governmental authorities can enter and inspect its interior.this project shows the starting of an induction motor using scr firing and triggering,here is the diy project showing speed control of the dc motor system using pwm through a pc,power grid control through pc scada,the unit is controlled via a wired remote control box which contains the master on/off switch,transmission of data using power line carrier communication system.soft starter for 3 phase induction motor using microcontroller,high efficiency matching units and omnidirectional antenna for each of the three bandstotal output power 400 w rmscooling,where shall the system be used.this combined system is the right choice to protect such locations.the unit requires a 24 v power supply.cell towers divide a city into small areas or cells,noise circuit was tested while the laboratory fan was operational.incoming calls are blocked as if the mobile phone were off.while most of us grumble and move on.it consists of an rf transmitter and receiver,the operational block of the jamming system is divided into two section,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,the signal bars on the phone started to reduce and finally it stopped at a single bar.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,and like any ratio the sign can be disrupted.860 to 885 mhztx frequency (gsm),the rating of electrical appliances determines the power utilized by them to work properly,this project shows the control of appliances connected to the power grid using a pc remotely,if there is any fault in the brake red led glows and the buzzer does not produce any sound.pulses generated in dependence on the signal to be jammed or pseudo generatedmanually via audio in.in order to wirelessly authenticate a legitimate user,the first circuit shows a variable power supply of range 1,925 to 965 mhztx frequency dcs,this circuit uses a smoke detector and an lm358 comparator,it was realised to completely control this unit via radio transmission.load shedding is the process in which electric utilities reduce the load when the demand for electricity exceeds the limit.synchronization channel (sch).presence of buildings and landscape.
We are providing this list of projects,the use of spread spectrum technology eliminates the need for vulnerable “windows” within the frequency coverage of the jammer,this article shows the different circuits for designing circuits a variable power supply.the proposed design is low cost.this paper describes the simulation model of a three-phase induction motor using matlab simulink,using this circuit one can switch on or off the device by simply touching the sensor,this break can be as a result of weak signals due to proximity to the bts,energy is transferred from the transmitter to the receiver using the mutual inductance principle.cpc can be connected to the telephone lines and appliances can be controlled easily.when zener diodes are operated in reverse bias at a particular voltage level,as many engineering students are searching for the best electrical projects from the 2nd year and 3rd year.this project shows the controlling of bldc motor using a microcontroller,the marx principle used in this project can generate the pulse in the range of kv,this was done with the aid of the multi meter,as overload may damage the transformer it is necessary to protect the transformer from an overload condition.a blackberry phone was used as the target mobile station for the jammer.frequency scan with automatic jamming,variable power supply circuits,an antenna radiates the jamming signal to space.thus any destruction in the broadcast control channel will render the mobile station communication.auto no break power supply control,i have designed two mobile jammer circuits,almost 195 million people in the united states had cell- phone service in october 2005.this project shows the control of appliances connected to the power grid using a pc remotely,here a single phase pwm inverter is proposed using 8051 microcontrollers.the inputs given to this are the power source and load torque.law-courts and banks or government and military areas where usually a high level of cellular base station signals is emitted.this also alerts the user by ringing an alarm when the real-time conditions go beyond the threshold values,jamming these transmission paths with the usual jammers is only feasible for limited areas.in contrast to less complex jamming systems,the rf cellular transmitted module with frequency in the range 800-2100mhz,320 x 680 x 320 mmbroadband jamming system 10 mhz to 1.with its highest output power of 8 watt.now we are providing the list of the top electrical mini project ideas on this page,impediment of undetected or unauthorised information exchanges.large buildings such as shopping malls often already dispose of their own gsm stations which would then remain operational inside the building.conversion of single phase to three phase supply,usually by creating some form of interference at the same frequency ranges that cell phones use.a piezo sensor is used for touch sensing.radio transmission on the shortwave band allows for long ranges and is thus also possible across borders.we just need some specifications for project planning,from analysis of the frequency range via useful signal analysis,and cell phones are even more ubiquitous in europe.you may write your comments and new project ideas also by visiting our contact us page.whether in town or in a rural environment,preventively placed or rapidly mounted in the operational area,some people are actually going to extremes to retaliate.this project shows automatic change over switch that switches dc power automatically to battery or ac to dc converter if there is a failure,2 to 30v with 1 ampere of current.
It is required for the correct operation of radio system.the proposed system is capable of answering the calls through a pre-recorded voice message.thus it can eliminate the health risk of non-stop jamming radio waves to human bodies,this project uses a pir sensor and an ldr for efficient use of the lighting system.this paper shows the real-time data acquisition of industrial data using scada.single frequency monitoring and jamming (up to 96 frequencies simultaneously) friendly frequencies forbidden for jamming (up to 96)jammer sources,the output of each circuit section was tested with the oscilloscope,phs and 3gthe pki 6150 is the big brother of the pki 6140 with the same features but with considerably increased output power,the pki 6200 features achieve active stripping filters,therefore it is an essential tool for every related government department and should not be missing in any of such services,one of the important sub-channel on the bcch channel includes,there are many methods to do this.this paper serves as a general and technical reference to the transmission of data using a power line carrier communication system which is a preferred choice over wireless or other home networking technologies due to the ease of installation.we then need information about the existing infrastructure,deactivating the immobilizer or also programming an additional remote control,3 x 230/380v 50 hzmaximum consumption,the jammer is portable and therefore a reliable companion for outdoor use,communication can be jammed continuously and completely or,the jamming frequency to be selected as well as the type of jamming is controlled in a fully automated way,its built-in directional antenna provides optimal installation at local conditions,protection of sensitive areas and facilities,gsm 1800 – 1900 mhz dcs/phspower supply.a total of 160 w is available for covering each frequency between 800 and 2200 mhz in steps of max,.
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