I'm trying to make an embedded thingy that detects the presence of a 19kHz tone from an electret microphone. I have a multistage bandpass filter/preamp hooked into the ADC of a microcontroller, and am trying to figure out the best way to digitally condition the signal in order to detect the presence of the tone.
I have implemented a Goertzel filter to look for the frequency of interest. My ADC takes 400 samples at a frequency of 4000KHz, then the micro processes the block and adds the result to a 100 point moving average. Looking at terminal output after each block, I can definitely see an overall jump in the numbers when the transmitter is turned on. However, there's a lot of noise in the power readings when the thing is turned on, and the the noise floor in the room I'm in keeps changing, too. I am not sure how to tune the thresholding level/filter out all of this noise.
I've tried a few things, but they all seem to be pretty noisy as the baseline of my signal drifts all over the place:
Preprocessing the block with Hamming/Blackman windows
Ratio of total received block power to band power in filter output
Ratio of power of band in interest (19kHz) to a band outside of,
but near band of interest (18.5kHz)
EDIT: I've done some more reading since posting this. Is calculating (2*Ew)/(N*Et) where Ew is the output from my filter and Et is the sum of the squares in my block the best way to do this test?
Any advice on how to deal with this and/or do a better method of signal extraction?
Thanks!
Related
In GNU radio I am trying to use the frequency of one signal to generate another signal of a different frequency. Here is the flow diagram that I am using:
I generate a 50 kHz signal with a signal source block and feed this into a Log Power FFT block. I used the Argmax block to find the FFT bin with the most power and multiply that with a constant. I want to use this result as the input to the complex vco block to generate another signal with a different frequency. All vectors have a length of 4096.
However, looking at the output of the complex QT Gui Time Sink block, the output of the vco is always zero. This is strange to me because using a float QT Gui Time Sink to look at the output of the multiply block (which is also going to the input of the vco block), the result is 50,000 as expected. Why am I only getting zero out of the vco?
Also, my sample rate is set to 1M. I am assuming because of the vector length of 4096 that the sample rate out of the Argmax block will be 1M/4096 = 244. Is this correct?
I am running gnu radio companion on windows 10.
The proposed solution is not a solution. Please don't abuse the signal probe, which is really just that, a probe for slow, debugging or purely visual purposes. Every time I use it myself, I see how architecturally bad it is, and I personally think the project should be removing it from the block library altogether.
Now, instead of just say "probe is bad, do something else", let's analyse where your flow graph falls short:
your frequency estimation depends on the argmax of a block that was meant for pure visualization purposes. No, the output rate is not (sampling rate/FFT length), the output rate is roughly "frame rate" (but not actually exactly. That block is terrible and mixes "sample times" with "wall clock times"). Don't do that. If you need something like that, use the FFT block, followed my "complex to magnitude squared". You don't even want the logarithm - you're just looking for a maximum
Instead of looking for a maximum absolute value in an FFT, which is inherently a quantizing frequency estimator, use something that actually gives you an oscillation. There's multiple ways you can do that with a PLL!
your VCO solution probably does what it's programmed to do. You just use an inadequate sensitivity!
The sampling rate you assume in your time sinks is totally off, which is probably why you have the impression of a constant output – it just changes so slowly that you'll not notice.
So, I propose to instead, either / or:
Use the PLL Freq det. Feed the output of that into the VCO. Don't scale with a constant, but simply apply the proper sensitivity. Sensitivity is the factor between "input amplitude" and "phase advance per sample on the output in radians".
Use the PLL Carrier recovery. Use a resampler, or some other mathematical method, to generate the new frequency. You haven't told us how that other frequency relates to the input frequency, so I can't give you concrete advice.
Also notice that this very much suggest this being a case of "I'm trying to recreate an analog approach in digital"; that might be a good approach, but in many cases it's not.
If I might be as brazen: Describe why you need to generate that other frequency, for which purpose, in a post on https://dsp.stackexchange.com or to the GNU Radio mailing list discuss-gnuradio#gnu.org (sign up here). This really only barely is a programming problem, but really a signal processing problem. And there's a lot of people out there eager to help you find an appropriate solution that actually tackles your problem!
It looks like a better solution was to probe the output of the multiplier using a probe signal block along with a Function Probe Block to create a new variable. This variable could then be used as the frequency value in a separate Signal Source Block that is used to generate the new signal. This flow diagram seems to satisfy the original intended purpose: new flow diagram
I'm trying to figure out how to correct drift errors introduced by a SLAM method using GPS measurements, I have two point sets in euclidian 3d space taken at fixed moments in time:
The red dataset is introduced by GPS and contains no drift errors, while blue dataset is based on SLAM algorithm, it drifts over time.
The idea is that SLAM is accurate on short distances but eventually drifts, while GPS is accurate on long distances and inaccurate on short ones. So I would like to figure out how to fuse SLAM data with GPS in such way that will take best accuracy of both measurements. At least how to approach this problem?
Since your GPS looks like it is very locally biased, I'm assuming it is low-cost and doesn't use any correction techniques, e.g. that it is not differential. As you probably are aware, GPS errors are not Gaussian. The guys in this paper show that a good way to model GPS noise is as v+eps where v is a locally constant "bias" vector (it is usually constant for a few metters, and then changes more or less smoothly or abruptly) and eps is Gaussian noise.
Given this information, one option would be to use Kalman-based fusion, e.g. you add the GPS noise and bias to the state vector, and define your transition equations appropriately and proceed as you would with an ordinary EKF. Note that if we ignore the prediction step of the Kalman, this is roughly equivalent to minimizing an error function of the form
measurement_constraints + some_weight * GPS_constraints
and that gives you a more straigh-forward, second option. For example, if your SLAM is visual, you can just use the sum of squared reprojection errors (i.e. the bundle adjustment error) as the measurment constraints, and define your GPS constraints as ||x- x_{gps}|| where the x are 2d or 3d GPS positions (you might want to ignore the altitude with low-cost GPS).
If your SLAM is visual and feature-point based (you didn't really say what type of SLAM you were using so I assume the most widespread type), then fusion with any of the methods above can lead to "inlier loss". You make a sudden, violent correction, and augment the reprojection errors. This means that you lose inliers in SLAM's tracking. So you have to re-triangulate points, and so on. Plus, note that even though the paper I linked to above presents a model of the GPS errors, it is not a very accurate model, and assuming that the distribution of GPS errors is unimodal (necessary for the EKF) seems a bit adventurous to me.
So, I think a good option is to use barrier-term optimization. Basically, the idea is this: since you don't really know how to model GPS errors, assume that you have more confidance in SLAM locally, and minimize a function S(x) that captures the quality of your SLAM reconstruction. Note x_opt the minimizer of S. Then, fuse with GPS data as long as it does not deteriorate S(x_opt) more than a given threshold. Mathematically, you'd want to minimize
some_coef/(thresh - S(X)) + ||x-x_{gps}||
and you'd initialize the minimization with x_opt. A good choice for S is the bundle adjustment error, since by not degrading it, you prevent inlier loss. There are other choices of S in the litterature, but they are usually meant to reduce computational time and add little in terms of accuracy.
This, unlike the EKF, does not have a nice probabilistic interpretation, but produces very nice results in practice (I have used it for fusion with other things than GPS too, and it works well). You can for example see this excellent paper that explains how to implement this thoroughly, how to set the threshold, etc.
Hope this helps. Please don't hesitate to tell me if you find inaccuracies/errors in my answer.
I have set of data which includes position of a car and unknown emitter signal level. I have to estimate the distance based on this. Basically signal levels varies inversely to the square of distance. But when we include stuff like multipath,reflections etc we need to use a diff equation. Here come the Hata Okumura Model which can give us the path loss based on distance. However , the distance is unknown as I dont know where the emitter is. I only have access to different lat/long sets and the received signal level.
What I am asking is could you guys please guide me to techniques which would help me estimate the distance based on current pos and signal strength.All I am asking for is guidance towards a technique which might be useful.
I have looked into How to calculate distance from Wifi router using Signal Strength? but he has 3 fixed wifi signals and can use the FSPL. However in an urban environment it doesnot work.
Since the car is moving, using any diffraction model would be very difficult. The multipath environment is constantly changing due to moving car, and any reflection/diffraction model requires well-known object geometry around the car. In your problem you have moving car position time series [x(t),y(t)] which is known. You also have a time series of rough measurement of the distance between the car and the emitter [r(t)] of unknown position. You need to solve the stationary unknown emitter position (X,Y). So you have many noisy measurement with two unknown parameters to estimate. This is a classic Least Square Estimation problem. You can formulate r(ti) = sqrt((x(ti)-X)^2 + (y(ti)-Y)^2) and feed your data into this equation and do least square estimation. The data obviously is noisy due to multipath but the emitter is stationary and with overtime and during estimation process, the noise can be more or less smooth out.
Least Square Estimation
I am new to labview and I need help.
I am using myrio with gyroscope, and when I display the gyroscope values I get noise.
My question is: How can I implement lowpass filter to reduce the noise in X , Y and Z rates of the gyroscope?
I searched a lot, but I did not understand how can I know what is the sampling frequency, the low and the high cutoff frequency.
Thank you so much.
If you're data is noisy you should try to fix the problem before you digitize the data. If a physical low-pass filter will do the trick, install one. The better the signal before the DAQ the better the data will be once it's digitized.
Some other signal conditioning considerations: make sure to reduce the length of wire from the gyroscope to the DAQ to only what's necessary, if possible eliminate any sources of noise from the environment (like any large rotating magnets--seriously I once helped someone who was complaining about noise when they were using an unshielded wire next to an MRI machine), and if you're going to add any signal conditioning try to amplify close to your sensor.
If you still would like to filter in software, there's an example included with LabVIEW that demonstrates both the point-by-point VIs and the array based VIs. It's called PtByBp and Array Based Filter.vi and can be found in the Example Finder under Analysis, Signal Processing and Mathematics >> Filtering and Conditioning
Please install this FREE toolkit from ni.com: http://sine.ni.com/nips/cds/view/p/lang/en/nid/212733
There are examples and good ready to use application how to use myRIO gyroscope and how to do proper DSP.
Sampling frequency is how fast you sample. Look for this value in the ADC settings. Low and high cutoffs - play with those values. Doing an FFT on your signal may help you to determine spectral frequency density, and decide where to cut.
Most GPS systems report "accuracy" in units of meters, with the figure varying over orders of magnitude. What does this figure mean? How can it be translated to an error function for estimation, i.e. the probability of an actual position given the GPS reading and its reported accuracy?
According to the Wikipedia article on GPS accuracy, a reading down to 3 meters can be achieved by precisely timing the radio signals arriving at the receiver. This seems to correspond with the tightest error margin reported by e.g. an iPhone. But that wouldn't account for external signal distortion.
It sounds like an error function should have two domains, with a gentle linear slope out to the reported accuracy and then a polynomial or exponential increase further out.
Is there a better approach than to tinker with it? Do different GPS chipset vendors conform to any kind of standard meaning, or do they all provide only some kind of number for the sake of feature parity?
The number reported is usually called HEPE, Horizontal Estimated Position Error. In theory, 67% of the time the measurement should be within HEPE of the true position, and 33% of the time the measurement should be in horizontal error by more than the HEPE.
In practice, no one checks HEPE's very carefully, and in my experience, HEPE's reported for 3 or 4 satellite fixes are much larger than they need to be. That is, in my experience 3 satellite fixes are accurate to within a HEPE distance much more than 67% of the time.
The "assumed" error distribution is circular gaussian. So in principle you could find the right ratios for a circular gaussian and derive the 95% probability radius and so on. But to have these numbers be meaningful, you would need to do extensive statistical testing to verify that indeed you are getting around 95%.
The above are my impressions from working in the less accuracy sensitive parts of GPS over the years. Concievably, people who work on using GPS for aircraft landing may have a better sense of how to predict errors and error rates, but the techniques and methods they use are likely not available in consumer GPS devices.