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!