For my project i need estimate the point on a grid that i use. To check my method works i took some Readings through x axis as below when y=2;
Blue, Brown and Grey are the access point 1,2 and 3 average RSSI readings. The node moves from red dot to blue which is 120 cm.The fluctuations of the RSSI readings are not linear and this is a very big problem in my case to get the accurate position. I use Knn to get the nearest position. What can i do to make it correct.? Use some other classifier will help ?
Check out Is rssi a reliable parameter in sensor localization algorithms: An experimental study
While i dont completely agree to the way those tests were executed and analyzed, they miss alot of details on analyzing the results such as differentiating the readings of RSSI over the different BLE channels, or measurements on antenna characteristics and orientation, the core statement i consider quite on point.
RSSI cannot be used as a reliable metric in localization algorithms
Beside the issues reflection, shadowing, antenna characteristics, etc. pose on the difficulty, the BLE Specification itself adds to the problem as the RSSI is not defined as an absolute value, it is specified to be used in a relative manner related to the distance to the golden receiver range, the rx power that would be not too weak and not too strong for the receiver to have the best receive quality. Also this RSSI can vary +/- 6dBm from the real value.
This means, we can hardly rely on the same readings across different devices and secondly, the accurracy is allowed to vary alot according to the specification.
For that reason, projects relying too hard on that accurracy are doomed to fail one way or another. However there are still applications possible getting something positive out of these RSSI readings, i.e. not relying completely on them, but instead use them as indicator in a supportive way.
If you are interested more on this matters, search for Indoor localization rssi i.e. on google scholar.
Related
As per current specs only duration in 5 min increments is tracked. Suggested interval is 200-300ms it seems. In Singapore signal strength was accounted for but this is variable per device. What if we do still also track the signal strength during that time? You would get a curve from weak to strong that gives an indication of the speed of travel while approaching, and couldn't you also derive fairly accurate indications of proximity after just one day of data?
I noticed that beacon libraries already attempt to estimate distance: Understanding ibeacon distancing
But it does not seem these self-calibrate yet based for instance on min-max readings versus moving targets. I'm thinking that could work especially as phones are modified to be always on in that respect.
It is very difficult to accurately determine distance by Bluetooth RSSI measured between two phones because there is a huge variation in the way different phone models measure bluetooth signals. Check out this graph produced by the Open Trace folks behind the effort in Singapore:
Those variations are consistent with my work in this area for the Android Beacon Library open source project. The fragmentation of Android devices has made it impossible to keep up with all the variations in signal strength response.
One point that the Open Trace team did not address in their work, is that there are a number of different bluetooth channels, and RSSI varies greatly on a given phone depending on which channel is being used. Mobile phones give you no indication of what channel the radio was on when a measurement was taken. The channel difference probably accounts for much of the "height" of the blue bars in the graph.
Unfortunately, there is no way to know if a device is approaching or stationary by reading RSSI updates. The changes could be because of natural variation, motion, or changes in obstacles. I do not believe self-calibration in a contact tracing app is viable.
This does not mean that RSSI is worthless for distance estimates, but it does mean that the margin of error is very high in what you can measure. If you see a device at all, there is a very good chance it is within 50 meters. And if you see that the RSSI is stronger than -70 dBm, there is a good chance you are within 2 meters. But there will always be false positives and false negatives.
I'm interested in developing a semi-autonomous RC lawnmower.
That is, the operator would decide when to stop, turn, etc., but could request "slightly overlap previous cut" and the mower would automatically do so. (Having operated high-end RC mowers at trade shows, this is the tedious part. Overcoming that, plus the high cost -- which I believe is possible -- would make a commercial success.)
This feature would require accurate horizontal positioning. I have investigated ultrasonic, laser, optical, and GPS. Each has its problems in this application. (I'll resist the temptation to go off on these tangents here.)
So... my question...
I know GPS horizontal accuracy is only 3-4m. Not good enough, but:
I don't need to know where I am on the planet. I only need to know where I am relative to where I was a minute ago.
So, my question is, is the inaccuracy consistent in the short term? if so, I think it would work for me. If it varies wildly by +- 1.5m from one second to the next, then it will not work.
I have tried to find this information but have had no success (possibly because of the ubiquity of other GPS-accuracy discussion), so I appreciate any guidance.
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It's looking to me like GPS is not just skewed but granular. I'd be interested in hearing from anyone who can give better insight into this, but for now I'm going to explore other options.
I realized that even though my intended application is "outdoor", this question is technically in the field of "indoor positioning systems" so I am adding that tag.
My latest thinking is to have 3 "intelligent" high-dB ultrasonic (US) speaker units. The mower emits RF requests for a tone from each speaker in rapid sequence, measuring the time it takes to "hear" each unit's response, thereby calculating distance to each of these fixed point and using trilateration to get position. if the fixed-point speakers are 300' away from the mower, the mower may have moved several feet between the 1st and 3rd response, so this would have to be allowed for in the software. If it is possible to differentiate 3 different US frequencies, they could be requested/received "simultaneously". Though you still run into issues when you're close to one fixed unit and far from another. So some software correction may still be necessary. If we can assume the mower is moving in a straight line, this isn't too complicated.
Another variation is the mower does not request the tones. The fixed units send RF "here comes tone from unit A" etc., and the mower unit just monitors both RF info and US tones. This may simplify things somewhat, but it seems it really requires the ability to determine which speaker a tone is coming from.
This seems like the kind of thing you could (and should) measure empirically. Just set a GPS of your liking down in the middle of a field on a clear day and wait an hour. Then come back and see what you find.
Because I'm in a city, I can't run out and do this for you. However, I found a paper entitled iGeoTrans – A novel iOS application for GPS positioning in geosciences.
That includes this figure which duplicates the test I propose. You'll note that both the iPhone4 and Garmin eTrex10 perform pretty poorly versus the accuracy you say you need.
But the authors do some Math Magic™ to reduce the uncertainty in the position, presumably by using some kind of averaging. That gets them to a 3.53m RMSE measure.
If you have real-time differential GPS, you can do better. But this requires relatively expensive hardware and software.
Even aside from the above, you have the potential issue of GPS reflection and multipath error. What if your mower has to go under a deck, or thick trees, or near the wall of a house? These common yard features will likely break the assumptions needed to make a good averaging algorithm work and even frustrate attempts at DGPS by blocking critical signals.
To my mind, this seems like a computer vision problem. And not just because that'll give you more accurate row overlaps... you definitely don't want to run over a dog!
In my opinion a standard GPS is no way accurate enough for this application. A typical consumer grade receiver that I have used has a position accuracy defined as a CEP of 2.5 metres. This means that for a stationary receiver in a "perfect" sky view environment over time 50% of the position fixes will lie within a circle with a radius of 2.5 metres. If you look at the position that the receiver reports it appears to wander at random around the true position sometimes moving a number of metres away from its true location. When I have monitored the position data from a number of stationary units that I have used they could appear to be moving at speeds of up to 0.5 metres per second. In your application this would mean that the lawnmower could be out of position by some not insignificant distance (with disastrous consequences for your prized flowerbeds).
There is a way that this can be done, as has been proved by the tractor manufacturers who can position the seed drills and agricultural sprayers to millimetre accuracy. These systems use Differential GPS where there is a fixed reference station positioned in the neighbourhood of the tractor being controlled. This reference station transmits error corrections to the mobile unit allowing it to correct its reported position to a high degree of accuracy. Unfortunately this sort of positioning system is very expensive.
Partly a coding problem, partly math problem.
Q1. I have an iOS device with compass active. If it knows I'm moving through the field of an iBeacon - or the Beacon is moving through my detection range - would it be possible for a phone to work out (roughly) the relative direction/bearing of that beacon with a series of readings by comparing signal strengths? Has anyone had a try at this?
Q2. Would it be possible to change the Major and Minor values of a beacon regularly (eg: every second) to pass small pieces of info - such as a second user's Bearing and Course?
Q1. It MIGHT be possible but you would need a controlled environment. Either the beacon or the phone needs to be fixed. You also need to be in an area with no obstructions or sources of radio interference.
Then you'd need to use the signal strength (which is sloppy and varies by a fair amount) as one input, and the device's heading info (which is also grossly inaccurate) and do some petty gnarly math on it.
Assuming you could work out the math, the slop in the input readings might make the results too iffy to be useful. (For example, how would you distinguish moving directly towards the beacon from moving 30 degrees to one side or the other? The signal strength would still increase, just not as quickly.
And your algorithm would have to deal with edge cases like moving along a circle around the beacon. In that case the signal strength should not change.
My gut is that even with clever algorithms that input data is just too unreliable to make much sense out of it, beyond "getting warmer" and "getting colder."
As mentioned above, you'd have to track your device's movement within the field, including distance covered and direction, then with multiple readings of signal strength you could theoretically calculate relative direction to the beacon to some degree of accuracy.
As to your second question about changing the minor version number, I have not seen any beacon APIs that allow that, either from the beacon manufacturers or from Apple's implementation.
However, a typical beacon is an ARM or other low power processor with a BLE transceiver, running a program. In theory it should be possible to create your own iBeacon transmitter that changed one of the parameters in order to transmit changing information. You'd have to set up the iOS device with the beacon region only specifying the UUID or UUID and major ID (depending on whether you wanted to change just the minor or change both the major and minor ID in order to transmit changing information.)
Note, too, that iBeacons are a special case of BLE, and the BLE standard does support the sending of arbitrary, changing data. You might be better off implementing your own BLE scheme either instead of or in addition to iBeacons.
I wrote a delphi program generating a gpx file as input for a "poor man's guidance system" for aerial spray by means of ultralight plane.
By and large, it produces route (parallel swaths) using gpx file as output.
The route's engine is based on the "Vincenty" algorithm which works fine for any wgs84 computation but
I can't get the accuracy of grid generated by ExpertGPS of Topografix (requirement).
I assume a 2D computation on the ellipsoïd :
1) From the start rtept (route point), compute the next rtept given a bearing and an arbitrary distance (swath length).
2) Compute the next rtept respective respective to previous bearing (90° turn) and another arbitrary distance (swath distance).
3) Redo 1) with the last rtept as starting point but in the opposite direction, and so on.
What's wrong with it ?
You do not describe your Pascal implementation of Vincenty's earth ellipsoid model so the following is speculation:
The model makes use of numerous geometrical trig functions-- ATAN2,
COS, SIN etc. Depending whether you use internal Delphi functions
or your own versions, there is the possibility of lack of precision
in calculations. The precision in the value of pi used in your
calculations could affect the precision you require.
Floating point arithmetic can cause decimal place errors. It will
make a difference whether you use single, double or real. I
believe some of the internal Delphi functions have changed with
different versions so possibly the version of Delphi you are
using will affect how the internal function is implemented.
If implemented accurately, Vincenty’s formula is supposed to be
accurate to within 0.5mm. Amazing accuracy. If there are rounding
errors or lack of precision in your Delphi implemention, the positional
errors can be significantly larger.
Consider the accuracy of your GPS information. Depending on how
many satellites are being used by the GPS receiver at any one time,
the accuracy of the positional information changes. Errors on
the order of 50 feet or more is possible. Additionally, the refresh
of positional information on the GPS receiver is not necessarily
instantaneous; therefore if the swath 'turns' occur rapidly, you
will have to ensure the GPS has updated at the turning point.
Your procedure to calculate the pattern seems reasonable so look
at your implementation of Vincenty's algorithm in your Delphi code.
This list is not exhaustive, I imagine others can improve it
dramatically. What I mention is based on my experience with GPS and
various versions of Delphi and what I could recall off the top of my head.
Something you might try is compare your calculations of
distance/bearing using your implementation of the algorithm with
examples provided on the Internet. There are several online
calculators. If you have not been there, the Aviation Formulary
is an excellent place to find examples of other navigational tricks.
http://williams.best.vwh.net/avform.htm . A comparison will
allow you to gain confidence in the precision of the Delphi
implementation of Vincenty's algorithm with data calculated by
mathematicians. Simply, your implementation of Vincenty may not be
precise. Then again, the error may be elsewhere.
I am doing farm GPS guidance similar for ground rig just with Android. Great for second tractor to help follow previous A B tracks especially when they disappear for a bit .
GPS accuracy repeat ability from one day to next will give larger distance. Expensive system's use dGPS2cm-10cm.5-30metres different without dGPS. Simple solution is recalibrate at known location. Cheaper light bars use this method.
Drift As above except relates to movement during job. Mostly unnoticeable <20cm 3hrs. Can jump 1-2metres rarely. I think when satellite connect or disconnect. Again recalibrate regularly at known coordinates ,i.e. spray fill point
GPS accuracy. Most phone update speed 1hz. 3? seconds between fixes at say 50km/hr , 41.66m between fixes. On ground rig 18km hrs but will be tracks after first run. Try a Bluetooth GPS 10hz check update speed and as mentioned fast turns a problem.
Accuracy of inputs and whether your guidance uses dGPS will make huge difference.
Once you are off your line say 5 metres at 100metres till next point, then at 50 metres your still 2.5 metres off unless your guidance takes you back to the route not the next coordinates.
I am not using Vincenty as I can 'bump'back onto line manually and over 1km across difference <30cm according to only reference I saw however I am taking 2 points and create parrallel points across.
Hope these ideas help your situation.
For example you measure the data coming from some device, it can be a mass of the object moving on the bridge. Because it is moving the mass will give data which will vibrate in some amplitude depending on the mass of the object. Bigger the mass - bigger the vibrations.
Are there any methods for filtering such kind of noise from that data?
May be using some formulas of vibrations? Have no idea what kind of formulas or algorithms (filters) can be used here. Please suggest anything.
EDIT 2:
Better picture, I just draw it for better understanding:
Not very good picture. From that graph you can see that the frequency is the same every
time, but the amplitude chanbges periodically. Something like that I have when there are no objects on the moving road. (conveyer belt). vibrating near zero value.
When the object moves, I there are the same waves with changing amplitude.
The graph can tell that there may be some force applying to the system and which produces forced occilations. So I am interested in removing such kind of noise. I do not know what force causes such occilations. Soon I hope I will get some data on the non moving road with and without object on it for comparison with moving road case.
What you have in your last plot is basically an amplitude modulated oscillation coming from a function like:
f[x] := 10 * (4 + Sin[x]) * Sin[80 * x]
The constants have been chosen to match your plot (using just a rule of thumb)
The Plot of this function is
That isn't "noise" (although may be some noise is there too), but can be filtered easily.
Let's see your data for the static and moving payloads ....
Edit
Based on your response to several comments, and based in my previous experience with weighting devices:
You are interfacing the physical world, not just getting input from a mouse and keyboard. It is very important for you understand the device, how it works and how it is designed.
You need a calibration procedure. You have to use several master weights to be sure that the device is working properly and linearly in the whole scale, and that the static case is measured much better than your dynamic needs.
You'll not be able to predict if you can measure with several loads in the conveyor until you do some experiments and look very carefully at the resulting plots
You need to be sure that a load placed anywhere in the conveyor shows the same reading. Or at least you should be able to correlate reading and position.
As I said before, you need a lot of info, and it seems that is not available. I always worked as a team with the engineers designing the device.
Don't hesitate to add more info ...
Have you tried filters with lowpass characteristics? There are different approaches for smoothing data (i.e. Savitzky-Golay, Gauss, moving average) but often, a simple N-point median filter is already sufficient.
It really depends on what you're after.
Take a look at this book:
The Scientist and Engineer's Guide to Digital Signal Processing
You can download it for free. In particular, check chapters 14 and 15.
If the frequency changes with mass and you're trying to measure mass, why not measure the frequency of the oscillations and use that as your primary measure?
Otherwise you need a notch filter which is tunable - figure out the frequency of the "noise" and tune the notch filter to that.
Another book to try is Lyons Understanding Digital Signal Processing
In order to smooth the signal, I'd average the previous 2 * n samples where n is the maximum expected wavelength of the vibrations.
This should cause most of the noise to be eliminated.
If you have some idea of the range of frequencies, you could do a simple average as long as the measurement period were sufficiently long to give you the level of accuracy you want to achieve. The more wavelengths worth of data you average against, the smaller the ratio of contributed error from a partial wavelength.
I'd suggest first simulating/modeling this in software like Matlab.
Data you'll need to consider:
The expected range of vibration frequencies
The measurement accuracy you want to achieve
The expected range of mass you'll want to measure
The function of mass to vibration amplitude
You should be able to apply the same principles as noise-cancelling microphones: put two sensors out, then subtract the secondary sensor's (farther away from the good signal source) signal from the primary sensor's (closer to the good signal source) signal.
Obviously, this works best if the "noise" will reach both sensors fairly equally while the "signal" reaches the primary sensor much more strongly.
For things like sound, this is pretty easy to do in the sensor itself, which makes your software a lot easier and more performant. Depending on what you're measuring, this might be easier to do with multiple sets of hardware and doing the cancellation in software.
If you can characterize the frequency spectra of the unwanted vibration noise, you might be able to synthesize a set of (near) minimum phase notch or band reject filter(s) to allow you to acquire your desired signal at your desired S/N ratio with minimized latency or data set size.
Filtering noisy digital signals is straight forward, as previous posters have noted. There are lots of references. You have not however stated what your objectives are clearly, so we cannot point you into a good direction. Are you looking for a single measurement of a single object on a bridge? [Then see other answers].
Are you monitoring traffic on this bridge and weighing each entity as it passes by? Then you need to determine when entities are on the sensor and when they are not. Typically, as long as the sensor's noise floor is significantly lower than the signal you're measuring this can be accomplished by simple thresholding.
Are you trying to measure the vibrations of the bridge caused by other vehicles? In which case you need either a more expensive sensor if you're having problems doing this, or a clearer measuring objective.