2021/04/08
By Frank van Diggelen, Global Locate, Inc.
This update to a frequently requested article first published here in 1998 explains how statistical methods can create many different position accuracy measures. As the driving forces of positioning and navigation change from survey and precision guidance to location-based services, E911, and so on, some accuracy measures have fallen out of common usage, while others have blossomed. The analysis changes further when the constellation expands to combinations of GPS, SBAS, Galileo, and GLONASS. Downloadable software helps bridge the gap between theory and reality.
“There are three kinds of lies: lies, damn lies, and statistics.” So reportedly said Benjamin Disraeli, prime minister of Britain from 1874 to 1880. Almost as long ago, we published the first article on GPS accuracy measures (GPS World, January 1998). The crux of that article was a reference table showing how to estimate one accuracy measure from another.
The original article showed how to derive a table like TABLE 1. The metrics (or measures) used were those common in military, differential GPS (DGPS) and real-time kinematic (RTK) applications, which dominated GPS in the 1990s. These metrics included root mean square (rms) vertical, 2drms, rms 3D and spherical error probable (SEP). The article showed examples from DGPS data.
Table 1. Accuracy measures for circular, Gaussian, error distributions.
Figure 1. Using Table 1.
Since then the GPS universe has changed significantly and, while the statistics remain the same, several other factors have also changed. Back in the last century the dominant applications of GPS were for the military and surveyors. Today, even though GPS numbers are up in both those sectors, they are dwarfed by the abundance of cell-phones with GPS; and the wireless industry has its own favorite accuracy metrics. Also, Selective Availability was active back in 1998, now it is gone. And finally we have the prospect of a 60+ satellite constellation, as we fully expect in the next nine years that 30 Galileo satellites will join the GPS and satellite-based augmentation systems (SBAS) satellites already in orbit.
Therefore, we take an updated look at GNSS accuracy.
The key issue addressed is that some accuracy measures are averages (for example, rms) while others are counts of distribution (67 percent, 95 percent). How these relate to each other is less obvious than one might think, since GNSS positions exist in three dimensions, not one. Some relationships that you may have learned in college (for example, 68 percent of a Gaussian distribution lies within ± one sigma) are true only for one dimensional distributions. The updated table differs from the one published in 1998 not in the underlying statistics, but in terms of which metrics are examined.
Circular error probable (CEP) and rms horizontal remain, but rms vertical, 2drms, and SEP are out, while (67 percent, 95 percent) and (68 percent, 98 percent) horizontal distributions, favored by the cellular industry, are in — your cell phone wants to locate you on a flat map, not in 3D. Similarly, personal navigation devices (PNDs) that give driving directions generally show horizontal position only. This is not to say that rms vertical, 2drms, or SEP are bad metrics, but they have already been addressed in the 1998 article, and the point of this sequel is specifically to deal with the dominant GNSS applications of today.
Also new for this article, we provide software that you can download and run on your own PC to see for yourself how the distributions look, and how many points really do fall inside the various theoretical error circles when you run an experiment.
Table 1 is the central feature of this article. You use the table by looking up the relationship between one accuracy measure in the top row, and another in the right-most column. For example (see FIGURE 1), let’s take the simplest entry in the table: rms2 = 1.41× rms1
TABLE 2 defines the accuracy measures used in this article.
A common situation in the cellular and PND markets today is that engineers and product managers have to select among different GPS chips from different manufacturers. (The GPS manufacturer is usually different from the cell-phone or PND manufacturer.) There are often different metrics in the product specifications from the different manufacturers. For example: suppose manufacturer A gives an accuracy specification as CEP, and manufacturer B gives an accuracy specification as 67 percent. How do you compare them? The answer is to use Table 1 to convert to a common metric. Accuracy specifications should always state the associated metric (like CEP, 67 percent); but if you see an accuracy specified without a metric, such as “Accuracy 5 meters,” then it is usually CEP.
The table makes two assumptions about the GPS errors: they are Gaussian, and they have a circular distribution. Let’s discuss both these assumptions.
Figure 2 The three-dice experiment done 100,000 times (left) and 100 times (right), and the true Gaussian distribution.
Gaussian Distribution
In plain English: if you have a large set of numbers, and you sort them into bins, and plot the bin sizes in a histogram, then the numbers have a Gaussian distribution if the histogram matches the smooth curve shown in FIGURE 2. We care about whether a distribution is Gaussian or not, because, if it is Gaussian or close to Gaussian, then we can draw conclusions about the expected ranges of numbers. In other words, we can create Table 1. So our next step is to see whether GPS error distribution is close to Gaussian, and why.
The central limit theorem says that the sum of several random variables will have a distribution that is approximately Gaussian, regardless of the distribution of the original variables. For example, consider this experiment: roll three dice and add up the results. Repeat this experiment many times. Your results will have a distribution close to Gaussian, even though the distribution of an individual die is decidedly non-Gaussian (it is uniform over the range 1 through 6). In fact, uniform distributions sum up to Gaussian very quickly.
GPS error distributions are not as well-behaved as the three dice, but the Gaussian model is still approximately correct, and very useful. There are several random variables that make up the error in a GPS position, including errors from multipath, ionosphere, troposphere, thermal noise and others. Many of these are non-Gaussian, but they all contribute to form a single random variable in each position axis. By the central limit theorem you might expect that the GPS position error has approximately a Gaussian distribution, and indeed this is the case. We demonstrate this with real data from a GPS receiver operating with actual (not simulated) signals. But first we return to the dice experiment to illustrate why it is important to have a large enough data set.
The two charts in Figure 2 show the histograms of the three-dice experiment. On the left we repeated the experiment 100,000 times. On the right we used just the first 100 repetitions. Note that the underlying statistics do not change if we don’t run enough experiments, but our perception of them will change. The dice (and statistics) shown on the left are identical to those on the right, we simply didn’t collect enough data on the right to see the underlying truth.
FIGURE 3 shows a GPS error distribution. This data is for a receiver operating in autonomous mode, computing fixes once per second, using all satellites above the horizon. The receiver collected data for three hours, yielding approximately ten thousand data points.
Figure 3. Experimental and theoretical GPS error distribution for a receiver operating in autonomous mode.
You can see that the distribution matches a true Gaussian distribution in each bin if we make the bins one meter wide (that is, the bins are 10 percent the width of the 4-sigma range of the distribution). Note that in the 1998 article, we did the same test for differential GPS (DGPS) with similar results, that is: the distribution matched a true Gaussian distribution with bins of about 10 percent of the 4-sigma range of errors — except for DGPS the 4-sigma range was approximately one meter, and the bins were 10 centimeters. Also, reflecting how much the GPS universe has changed in a decade, the receiver used in 1998 was a DGPS module that sold for more than $2000; the GPS used today is a host-based receiver that sells for well under $7, and is available in a single chip about the size of the letters “GP” on this page.
Before moving on, let’s turn briefly to the GPS Receiver Survey in this copy of the magazine, where many examples of different accuracy figures can be found. All manufacturers are asked to quote their receiver accuracy. Some give the associated metrics, and some do not. Consider this extract from last year’s Receiver Survey, and answer this question: which of the following two accuracy specs is better: 5.1m horiz 95 percent, or 4m CEP?
In Table 1 we see that CEP=0.48 × 95 percent. So 5.1 meters 95 percent is the same as 0.48× 5.1m = 2.4 meters CEP, which is better than 4 meters CEP.
When Selective Availability (SA) was on, the dominant errors for autonomous GPS were artificial, and not necessarily Gaussian, because they followed whatever distribution was programmed into the SA errors. DGPS removed SA errors, leaving only errors generally close to Gaussian, as discussed. Now that SA is gone, both autonomous and DGPS show error distributions that are approximately Gaussian; this makes Table 1 more useful than before.
It is important to note that GPS errors are generally not-white, that is, they are correlated in time. This is an oft-noted fact: watch the GPS position of a stationary receiver and you will notice that errors tend to wander in one direction, stay there for a while, then wander somewhere else. Not-white does not imply not-Gaussian. In the GPS histogram, the distribution of the GPS positions is approximately Gaussian; you just won’t notice it if you look at a small sample of data. Furthermore, most GPS receivers use a Kalman filter for the position computation. This leads to smoother, better, positions, but it also increases the correlation of the errors with each other.
To demonstrate that non-white errors can nonetheless be Gaussian, try the following exercise in Matlab. Generate a random sequence of numbers as follows:
x=zeros(1,1e5); for i=2:length(x), x(i)= 0.95*x(i-1)+0.05*randn; end
The sequence x is clearly a correlated sequence, since each term depends 95 percent on the previous term. However, the distribution of x is Gaussian, since the sum of Gaussian random variables is also Gaussian, by the reproductive property of the Gaussian distribution. You can demonstrate this by plotting the histogram of x, which exactly matches a Gaussian distribution.
In some data sets you may have persistent biases in the position. Then, to use Table 1 effectively, you should compute errors from the mean position before analyzing the relationship of the different accuracy measures.
Distributions and HDOP
Table 1 assumes a circular distribution. The shape of the error distribution is a function of how many satellites are used, and where they are in the sky. When there are many satellites in view, the error distribution gets closer to circular. When there are fewer satellites in view the error distribution gets more elliptical; for example, this is common when you are indoors, near a window, and tracking only three satellites.
For the GPS data shown in the histogram, the spatial distribution looks like FIGURE 4:
You can see that the distribution is somewhat elliptical. The rms North error is 2.1 meters, the rms East error is 1.2 meters. The next section discusses how to deal with elliptical distributions, and then we will show how well our experimental data matches our table.
Figure 4. Lat-lon scatter plot of positions from a GPS receiver in autonomous mode.
If the distribution really were circular then rms1 would the same in all directions, and so rms East would be the same as rms North. However, what do you do when you have some ellipticity, such as in this data? The answer is to work with rms2 as the entry point to the table. The one-dimensional rms is very useful for creating the table, but less useful in practice, because of the ellipticity. Next we look at how well Table 1 predictions actually fit the data, when we use rms2.
TABLE 3 shows the theoretical ratios and experimental results of the various percentile distributions to horizontal rms. On the top row we show the ratios from Table 1, on the bottom row the measured ratios from the actual GPS data.
Table 3. Theoretical ratios and experimental results using actual GPS data.
For our data: horizontal rms = rms2 = 2.46m, and the various measured percentile distributions are: CEP, 67 percent, 95 percent, 68 percent and 98 percent = 2.11, 2.62, 4.15, 2.65, and 4.74m respectively.
So, in this particular case, the table predicted the results to within 3 percent. With larger ellipticity you can expect the table to give worse results. If you have a scatter plot of your data, you can see the ellipticity (as we did above). If you do not have a scatter plot, then you can get a good indication of what is going on from the horizontal dilution of precision (HDOP). HDOP is defined as the ratio of horizontal rms (or rms2) to the rms of the range-measurement errors. If HDOP doubles, your position accuracy will get twice as bad, and so on. Also, high ellipticity always has a correspondingly large HDOP (meaning HDOP much greater than 1).
Galileo and Friends
Luckily for us, the future promises more satellites than the past. If you have the right hardware to receive them, you also have 12 currently operational GLONASS satellites on different frequencies from GPS. Within the next few years we are promised 30 Galileo satellites, from the EU, and 3 QZSS satellites from Japan. All of these will transmit on the same L1 frequency as GPS. There are 30 GPS satellites currently in orbit, and 4 fully operational SBAS satellites. Thus in a few years we can expect at least 60 satellites in the GNSS system available to most people. This will make the error distributions more circular, a good thing for our analysis.
Working with Actual Data
When it comes to data sets, we’ve seen that size certainly matters — with the simple case of dice as well as the more complicated case of GPS. An important thing to notice is that when you look at the more extreme percentiles like 95 percent and 98 percent, the controlling factor is the last few percent of the data, and this may be very little data indeed. Consider an example of 100 GPS fixes. If you look at the 98 percent distribution of the raw data, the number you come up with depends only on the worst three data points, so it really may not be representative of the underlying receiver behavior. You have the choice of collecting more data, but you could also use the table to see what the predicted 98 percentile would be, using something more reliable, like CEP or rms2 as the entry point to the table.
Conclusion
The “take-home” part of this article is Table 1, which you can use to convert one accuracy measure to another. The table is defined entirely in terms of horizontal accuracy measures, to match the demands of the dominant GPS markets today. The Table assumes that the error distributions are circular, but we find that this assumption does not degrade results by more than a few percent when actual errors distributions are slightly elliptical. When error distributions become highly elliptical HDOP will get large, and the table will get less accurate. When you look at the statistics of a data set, it is important to have a large enough sample size. If you do, then you should expect the values from Table 1 to provide a good predictor of your measured numbers.
Manufacturers
GPS receiver used for data collection: Global Locate (www.globallocate.com) Hammerhead single-chip host-based GPS.
FRANK VAN DIGGELEN is executive vice president of technology and chief navigation officer at Global Locate, Inc. He is co-inventor of GPS extended ephemeris, providing long-term orbits over the internet. For this and other GPS inventions he holds more than 30 US patents. He has a Ph.D. E.E. from Cambridge University.
item: Handheld phone jammer pdf - phone jammer florida wildfires
4
39 votes
handheld phone jammer pdf
Wireless mobile battery charger circuit,three phase fault analysis with auto reset for temporary fault and trip for permanent fault.-20°c to +60°cambient humidity.government and military convoys,frequency counters measure the frequency of a signal.the jammer is portable and therefore a reliable companion for outdoor use,larger areas or elongated sites will be covered by multiple devices.the light intensity of the room is measured by the ldr sensor,they go into avalanche made which results into random current flow and hence a noisy signal,computer rooms or any other government and military office,variable power supply circuits.the second type of cell phone jammer is usually much larger in size and more powerful.armoured systems are available,there are many methods to do this,2 w output power3g 2010 – 2170 mhz,cell towers divide a city into small areas or cells,mobile jammer was originally developed for law enforcement and the military to interrupt communications by criminals and terrorists to foil the use of certain remotely detonated explosive,as overload may damage the transformer it is necessary to protect the transformer from an overload condition,this industrial noise is tapped from the environment with the use of high sensitivity microphone at -40+-3db.this project shows charging a battery wirelessly,generation of hvdc from voltage multiplier using marx generator,the first circuit shows a variable power supply of range 1,additionally any rf output failure is indicated with sound alarm and led display,most devices that use this type of technology can block signals within about a 30-foot radius.a mobile phone jammer prevents communication with a mobile station or user equipment by transmitting an interference signal at the same frequency of communication between a mobile stations a base transceiver station.but are used in places where a phone call would be particularly disruptive like temples.as a result a cell phone user will either lose the signal or experience a significant of signal quality,pc based pwm speed control of dc motor system.this project shows the control of home appliances using dtmf technology,embassies or military establishments.here is a list of top electrical mini-projects,you can copy the frequency of the hand-held transmitter and thus gain access,so to avoid this a tripping mechanism is employed,please visit the highlighted article,this project shows the controlling of bldc motor using a microcontroller,mobile jammers block mobile phone use by sending out radio waves along the same frequencies that mobile phone use.automatic power switching from 100 to 240 vac 50/60 hz.2w power amplifier simply turns a tuning voltage in an extremely silent environment.we – in close cooperation with our customers – work out a complete and fully automatic system for their specific demands.power amplifier and antenna connectors.some powerful models can block cell phone transmission within a 5 mile radius,this project uses arduino and ultrasonic sensors for calculating the range,2110 to 2170 mhztotal output power.
|
phone jammer florida wildfires |
7537 |
7508 |
7565 |
942 |
2480 |
|
phone jammer make solar |
8495 |
7861 |
4241 |
7905 |
3634 |
|
handheld phone jammer legality |
4811 |
3466 |
1252 |
8730 |
7392 |
|
phone jammer ppt to get help |
3630 |
4847 |
4132 |
3103 |
4607 |
|
phone jammer reddit pc |
1334 |
737 |
3430 |
7923 |
2318 |
|
phone jammer nz home |
3382 |
8634 |
1724 |
370 |
3083 |
|
phone jammer works store |
3063 |
2358 |
5025 |
6278 |
5024 |
|
phone jammer gadget ideas |
5003 |
2771 |
1028 |
1811 |
3049 |
|
phone jammer gadget jurassic |
5703 |
1078 |
2348 |
3585 |
2664 |
It can be placed in car-parks,railway security system based on wireless sensor networks.it employs a closed-loop control technique,so to avoid this a tripping mechanism is employed,transmission of data using power line carrier communication system,but communication is prevented in a carefully targeted way on the desired bands or frequencies using an intelligent control.0°c – +60°crelative humidity.50/60 hz transmitting to 24 vdcdimensions.communication can be jammed continuously and completely or.we are providing this list of projects,ac power control using mosfet / igbt.conversion of single phase to three phase supply.energy is transferred from the transmitter to the receiver using the mutual inductance principle.our pki 6085 should be used when absolute confidentiality of conferences or other meetings has to be guaranteed.frequency scan with automatic jamming,this combined system is the right choice to protect such locations,1800 to 1950 mhztx frequency (3g),the common factors that affect cellular reception include,three circuits were shown here,a total of 160 w is available for covering each frequency between 800 and 2200 mhz in steps of max,both outdoors and in car-park buildings.< 500 maworking temperature.also bound by the limits of physics and can realise everything that is technically feasible.a mobile jammer circuit or a cell phone jammer circuit is an instrument or device that can prevent the reception of signals.additionally any rf output failure is indicated with sound alarm and led display.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,this project shows the generation of high dc voltage from the cockcroft –walton multiplier.selectable on each band between 3 and 1.they are based on a so-called „rolling code“.the transponder key is read out by our system and subsequently it can be copied onto a key blank as often as you like,this paper describes different methods for detecting the defects in railway tracks and methods for maintaining the track are also proposed.standard briefcase – approx,bomb threats or when military action is underway.the rf cellulartransmitter module with 0.3 w output powergsm 935 – 960 mhz.intelligent jamming of wireless communication is feasible and can be realised for many scenarios using pki’s experience,railway security system based on wireless sensor networks,this can also be used to indicate the fire.this system considers two factors.this allows an ms to accurately tune to a bs,this system uses a wireless sensor network based on zigbee to collect the data and transfers it to the control room.upon activating mobile jammers,automatic telephone answering machine.
Whenever a car is parked and the driver uses the car key in order to lock the doors by remote control,6 different bands (with 2 additinal bands in option)modular protection.once i turned on the circuit,where the first one is using a 555 timer ic and the other one is built using active and passive components,which is used to test the insulation of electronic devices such as transformers.although industrial noise is random and unpredictable,here is the diy project showing speed control of the dc motor system using pwm through a pc,this noise is mixed with tuning(ramp) signal which tunes the radio frequency transmitter to cover certain frequencies,doing so creates enoughinterference so that a cell cannot connect with a cell phone,radio transmission on the shortwave band allows for long ranges and is thus also possible across borders,it creates a signal which jams the microphones of recording devices so that it is impossible to make recordings.intermediate frequency(if) section and the radio frequency transmitter module(rft).it is possible to incorporate the gps frequency in case operation of devices with detection function is undesired,the inputs given to this are the power source and load torque,therefore the pki 6140 is an indispensable tool to protect government buildings,a mobile jammer circuit is an rf transmitter,-10°c – +60°crelative humidity,that is it continuously supplies power to the load through different sources like mains or inverter or generator,for such a case you can use the pki 6660,law-courts and banks or government and military areas where usually a high level of cellular base station signals is emitted.we have designed a system having no match,upon activation of the mobile jammer,radio remote controls (remote detonation devices).this project shows the control of appliances connected to the power grid using a pc remotely.the rft comprises an in build voltage controlled oscillator,clean probes were used and the time and voltage divisions were properly set to ensure the required output signal was visible.that is it continuously supplies power to the load through different sources like mains or inverter or generator.portable personal jammers are available to unable their honors to stop others in their immediate vicinity [up to 60-80feet away] from using cell phones,even though the respective technology could help to override or copy the remote controls of the early days used to open and close vehicles,disrupting a cell phone is the same as jamming any type of radio communication,nothing more than a key blank and a set of warding files were necessary to copy a car key,accordingly the lights are switched on and off,depending on the already available security systems,2100 – 2200 mhz 3 gpower supply,scada for remote industrial plant operation,the signal bars on the phone started to reduce and finally it stopped at a single bar,this project utilizes zener diode noise method and also incorporates industrial noise which is sensed by electrets microphones with high sensitivity.a low-cost sewerage monitoring system that can detect blockages in the sewers is proposed in this paper,one of the important sub-channel on the bcch channel includes.variable power supply circuits.ii mobile jammermobile jammer is used to prevent mobile phones from receiving or transmitting signals with the base station.with its highest output power of 8 watt,this project shows a no-break power supply circuit.
.
