Question on analog sensor signal processing
Question on analog sensor signal processing
(OP)
Not sure if its a basic question to ask. If you have an analog sensor and the output drifts over time or output is non repeatable(input being constant). Also assume thats the best sensor you can get and you are relying on those values to set thresholds etc. Is there a way to take care of imperfections in the sensor on the signal processing side. I know ways to get rid of noise but not drifts in values or non repetabilty. Are there standard ways to fix them as many sensors promise to be very repeatable , non drifting etc but in actuality for the same input they may give varying (or slightly varying ) values each time.One way could be when looking at the output of the sensor using a band( +/- say 5%) around each value to take care of drift rather then using exact values but hoping there are better ways. If someone can point me in the right direction of where to look. Thanks





RE: Question on analog sensor signal processing
Dan - Owner

http://www.Hi-TecDesigns.com
RE: Question on analog sensor signal processing
A more classical approach to drift would be to use some form of chopper stabilization, where the sensor is cyclically zeroed out. Obviously, this has limitations on bandwidth and settling time, but it was a tried and trued approach from 40 years ago. AC coupling is a similar approach, if it can be mechanized.
Your last couple of sentences seem to be talking about repeatability, which is a low-frequency read noise, of sorts, and massive averaging or filtering will reduce that.
Ultimately, though, you can never get completely noise-free behavior, since that's inherent in the quantum mechanical nature of physical processes. To wit, even a perfectly noise-free, but physically real current source will show noisy behavior, i.e., Shot noise, since not all the electrons will arrive or leave at the times they're scheduled for.
In most measurement and control systems, you need to ensure that your design isn't asking for more performance than is physically achievable.
TTFN
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RE: Question on analog sensor signal processing
Cheers
Greg Locock
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RE: Question on analog sensor signal processing
the sensor 'drift' is hopefully a very low frequency component of your signal such that you can develop a filter that tracks the average output of your sensor and yet still responds to the 'signal of interest'.
MacGyver has the best solution although i think it can be implemented without much painful math.
First form an average of the signal.
Av=K*(Y-Av)
Execute this with each new value as Y in order to update 'Av' as the average estimate. Select K to cancel the drift. Smaller K makes the average longer to attain but also filters out more slowly moving drift errors.
Hopefully your real signal of interest is S.
S = Y-Av
Hopefully this signal removes the slowly moving drift and S is what you are really measuring.
RE: Question on analog sensor signal processing
1) Periodic Built In Test (PBIT), end-to-end. Or 'self-calibration'. On the 'Physical World' side of the sensor, present it with a calibrated stimulus (or several) on a periodic basis. If it was a pressure sensor (for example), then you'd add a plumbing system with valves to connect the sensor to either the measurement source (normal operation), or to the calibration system.
2) Another trick is to "flip" the sensor over so the offset cancels out. The reversal has to be done quicker than the drift. This concept is obviously only applicable to very few technologies.
RE: Question on analog sensor signal processing
Dan - Owner

http://www.Hi-TecDesigns.com
RE: Question on analog sensor signal processing
Av=Av + K*(Y-Av)
Where Av is the average estimate and Y is each new reading.
RE: Question on analog sensor signal processing
RE: Question on analog sensor signal processing
RE: Question on analog sensor signal processing
A sensor that varies 10% of reading is a pretty poor choice for accurate measurements. Usually, weight sensors, ala strain gauges, can be designed in different circuit configurations, such as temperature-compensated bridges, to eliminate most of the uncertainties induced by the physical structure of the sensor.
Without taking globs of data, you won't even know if the errors are truly random or systematic. Averaging and filtering ALL assume a zero-mean behavior. If your data is not zero-mean, then averaging will give you a false sense of noise reduction, when it might actually be doing nothing at all.
TTFN
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RE: Question on analog sensor signal processing
Dan - Owner

http://www.Hi-TecDesigns.com
RE: Question on analog sensor signal processing
Your A/D converter could be averaging too fast. There should be no reason for anything relative to the measurement to change that rapidly.
If your A/D is acquiring too fast then you read all the little hills and valleys of noise you would see if you hooked a scope up to the signal. Note this is not the same as overall sample rate.
If you cannot make your A/D integrate over longer time then adding a filter prior to the A/D input pin would be the right stuff.
RE: Question on analog sensor signal processing
Cheers
Greg Locock
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RE: Question on analog sensor signal processing
If the sensor in question is measuring - for example - atmospheric pressure (that varies from hour to hour), and the sensor is drifting (also from hour to hour), then there's no filtering that can separate one from the other.
OP is being a bit coy about what sort of sensor is being used and exactly what is being measured.
RE: Question on analog sensor signal processing
Otherwise you have the wrong wiegh scale and does not give you the degree of accurancy.
plus if your using this for shipping purposes, the scale has to be certified for trade.
Using scaling to fudge it probably breaks the above rule.
RE: Question on analog sensor signal processing
The only linear processing thing that can be done is filter out the spectrum of the error,noise as best as possible.
To start with this requires some detailed knowledge of the spectrum of both the signals and error OR the statistics of the error.
Kalman filtering or Weiner filtering begins with the statistics of the noise but their implementation is a bit difficult.
Only the OP can help qualify the frequency spectrum of his signal and his errors (noise) and then we may be able to help.