Pardon? Too noisy, can’t hear you!

The difference between Chloe’s flight in the video, and the ones I tested the day before is the digital low pass filter, or to be more specific the accelerometer’s dlpf since the MPU-9250 provides independent filters for gyro and accelerometer data.

This isn’t the anti-aliasing filter which is applied prior to digital sampling of the analogue  accelerometer at 1kHz – the dlpf is aimed solely at getting the data you want, and not the noise you don’t.  I don’t know what the anti-aliasing filter is set to, but it must be less that 500Hz in the same way music is sampled at 44.1kHz even though what’s nominally audible is below 20kHz – there’s a high-order low-pass anti-aliasing filter just above 20kHz,

Anyway, for the previous flights, Chloe’s accelerometer filter was set to 184Hz on the basis that the higher the filter frequency, the lesser the delay in processing the filter (5.8ms according to the spec).  I dropped it to 92Hz (7.8ms delay), and the improvement was dramatic.

And that suggests the primary cause of my drift now is possibly noise: higher frequency vibrations from the motors (for example) are irrelevant to the flight speeds, but could cause errors in velocity integration because the pass band of the DLPF is set too high.

There’s clearly a balance however which is that if the DLPF is set too low (41, 20, 10 and 5Hz are the other options), while the noise will be filtered out, the risk of filtering out the good stuff increases as does the lag (11.8, 19.8, 35.7 and 66.9ms respectively) i.e. how out of date the filtered data is!  Perhaps that’s where Kalman steps in as it offers a level of prediction (so I’ve read) which could overcome this lag.  But for now, Kalman can continue sitting on the back-burner while I play with the dlpf values to find that magic number between noisy and necessary data samples.

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