Smooth (not) equally-spaced signal strength data I'm currently trying to reduce the noise in/smooth sampled signal strength data. The context is that I collected data from a transmitting device using bluetooth - the available data is basically a series of timestamps and the receiving signal strength.

The problem: The signal strength fluctuates quite strongly, which is why I want to smooth my data. Actually the data is not equally spaced - some signals get lost, the transmitter has some quirks leading to irregular periods in which signals are sent etc. - but I could aggregate them on a fixed time-period, if that's necessary / recommended.

I read some articles but are still not sure how to approach this. What kind of filter would probably lead to the best results? Kalman, Savitzky-Golay, adaptive, exponential...many to choose from and not enough experience to do so.

What would you recommend?

Thanks for your time!
Between these I only have experience with Kalman filters. They don't need equally spaced measurements but you can make it a lot easier with it. Missing measurements are really easy to cope with.

If you want a simple solution and you think that the most likely outcome of your next measurement is the same as the last one (there are no 'dynamics') you could also interpolate your missing values and use a linear filter.