>On 29 Jun, 19:19, "Michael Plante" <michael.pla...@gmail.com> wrote:
>> >On 29 Jun, 07:37, "Michael Plante" <michael.pla...@gmail.com> wrote:
>
>> >What 'value' is this? Frequency? Amplitude? Both?
>>
>> Neither...a cleaned-up time domain signal. =A0Instantaneous amplitude
if
>> you will. =A0Plus the derivative...
>
>Well, if this is a random-walk type signal, heck out the
>Durbin-Koopman book on Kalman filters:
>
>http://www.amazon.com/Analysis-Metho...nce/dp/019852=
>3548/ref=3Dsr_1_1?ie=3DUTF8&s=3Dbooks&qid=3D1246297738& sr=3D8-1

I'm kinda short on money right now, but I have about a half dozen books o
Kalman Filters, most of which I've read (can't quite make it throug
Maybeck, but I've read the other ones, including Gelb, Grewal&Andrews, an
a couple others). I appreciate the suggestion, but, given th
circumstances, is it fair to say I could find it somewhere other tha
Durbin-Koopman? If I took the KF route, there are two major caveats:

1) It would have to be a fixed-interval smoother, not a filter, per se.
This is not an objection, but I do think that got lost in the origina
reaction. (Tim had cautioned about "some really wimpy embedded processor.
This is post-processing on a desktop computer.) I don't want to thro
away the advantage of "noncausality" (if you choose to look at it tha
way...really just a gigantic delay).

2) This is part of the system ID stage, so I don't yet trust my syste
model (circular reasoning, and all that). Consequently, would I model i
as, say, a pair of states giving a (damped for stability?) sinusoid, with
third state representing the instantaneous frequency? I.e., somewha
similar to page 62 of Gelb, but adaptive? Would that even work?


>Within a couple of pages they come up with a motivational
>example of a simple Kalman filter that works on an RW proces.

Just to be clear, the RW is just the drifting bias of the sensor. It i
long term and nearly DC for the time periods and amplitudes of interest.
would add a fourth state to the above for the bias, and choose its proces
noise on the basis of my Allan Variance plots.


>One benefit of the Kalman filter
>is that it easily handles missing samples.

I had previously been averaging adjacent ones to fill them in, as Tim als
suggested (I have timestamped everything. They stick out.), but the KF i
definitely less ad-hoc in that regard.


>Apart from that, don't dismiss the bandpass filter quite yet.
>True, there might be some delays, but if you are a bit
>cautious when designing the filter, you would be able to
>correct for them.

Hrm. Okay, would you say the BPF is the best option mentioned so far?
Can I provide some other sort of information (a particular plot?) tha
would make the decision more clear-cut? I don't know if it's relevant, bu
I neglected to mention that the accelerometer channels show ver
significant quantization (the IMU is marketed for model aircraft, whic
have greater dynamics than my app).