Can I create one super-sensor by averaging together the readings from several LM35 sensors? Wouldn't this be more accurate because I'd be averaging out the systematic bias in the individual sensors? Also, wouldn't it be more precise, too, because any noise will be dampened/averaged out?
This seems almost too good to be true. I mean, these things are really cheap as far as sensors go, so what's to stop me from buying like 10 of them and making a super-accurate temperature sensors with this method?
Best Answer
You can not guarantee more accuracy, but you can possibly get better signal to noise ratio.
Imagine if all the sensors were off by the same amount as allowed in the specs. Averaging them would not yield better accuracy. If you had a reasonably large number of these sensors and they had a random error distribution within their allowed error band, then you would get better accuracy by averaging. However, the problem is that you have no way of knowing if you have the first case or the second. If all the units are from the same production lot, their errors are likely not randomly distributed.
The noise does go down, however. Each sensor adds some noise to its reading. This is uncorrelated with the noise from the other sensors, so averaging does lower noise. Of course this is not true of noise coming from outside the whole system since that would be correlated and averging the multiple sensor readings won't reduce it.
Note that there is more than one way to "average". You are thinking of averaging accross multiple sensors to reduce noise. However, since this noise is essentially random, you can just as well average between multiple readings from the same sensor taken at different times. In the more general case, this is really low pass filtering. Since temperatures change slowly, aggressively low pass filtering the output of a temperature sensor does reduce noise. Looking at this in frequency space, you know the temperature changes slowly so high frequency components are noise and can be safely attenuated.