u/IsThisOneStillFree

I've had my Suunto 9 Peak for a couple of years. The battery still has sufficient capacity for me of maybe a week, however the battery indicator is completely off. The watch turns off at 60% charge.

Is there any way to recalibrate the battery indicator?

I'm looking for a cheap SBC (or, maybe, as SFF PC) that I exclusively want to use to connect it to my TV. The PC I'm looking for must:

• be able to stream 4K YouTube vidoes without dropping frames left and right

• be affordable (let's say max 150€, ideally less than 100€)

• be reasonablly quiet

• Available in Norway

in addition, it should

• be able to be used with Steam In-house-streaming for the occational gaming-from-bed-session. Here, 4K is not a requirement.

• be passively cooled

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So far, I've figured out that the Raspberry Pi 4 is not up to the task as it does not support h.265. The RPi 5 might be able to do it, but is rather expensive these days, especially the 16GB RAM version.

The Radxa ROCK 5C seems to fit the bill, but I have no experience with it. It would be nice to have it confirmed that it's up to the task and what your opinions on the minimum memory requirements are.

I'm happy to tinker with it and consider myself a Linux power user.

I'm struggling to find resources on how to approach my problem, likely because I'm not looking for the right terms or do not have the right books. I'm mainly looking for pointers to further reading, I need to really understand this.

I'm trying to filter a "GNSS-like" measurement, except that my observation terms are linearly dependent (or underdetermined? unobservable? - not sure what the correct term is!).

Suppose a 1D-case: I try to solve for a position. I observe pseudoranges to different anchors, and each pseudorange has a common, and an individual bias. The common bias is caused by my local clock errors, and the individual biases are caused by the clock errors of the anchors. Thus,

x = (p_x, b_c, b_1, b_2, ... b_n)

and

y_i = d_i + b_c + b_i

where d_i is the geometric distance, b_c the common bias, and b_i the i-th bias, each expressed in units of distance. In other words, a change in measurement could ether be casued by a change in position, in local, or in remote clock error - it's not known to me (yet) what the correct explanation of change in an observation is.

Now, in GNSS the problem is slightly different: there, the individual bias is negligible, and four (or five) measurements make the system of equations determined. However, in my case, each additional observation introduces one additional bias, meaning that the overall system of equation is always underdetermined.

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I'm unsure how to approach this problem. I'm pretty sure it's possible to use a KF-variant to solve this problem, given some assumptions (in particular that the biases are slowly varying and that the common bias is much less stable than the individual ones, i.e. the remote oscillators are much better than my own one). I'm happy with some additional assumptions such as "known stationary at startup until initial biases are estimated", "remote biases are uncorrelated" or "biases are zero-mean", and I also have additional, faulty measuremens from an IMU. My actual problem is more complicated than described, but the simplified 1D-case should be sufficient to describe my fundamental problem, which, when solved, I should be able to extend to the "full" solution.

If this is fundamentally not solvable, it would be really important to know why, and if there are other filtering approaches that allow this.