I am making real-time measurements, which are affected by some noise. This noise has oscillating pattern, i.e. the total signal tends to oscillate around the mean value. The examples of measurements are shown in figures below.
Filtering this noise after the signal is received is quite straitforward - we may use mean filter, for example.
What I would like to do is to implement real-time noise filtering, so that filtered signal would be close to its constant mean value.
What kind of methods of filtering could you recommend? I thought about Kalman filter, but the state model is unknown. Maybe there is something else?..
P.S. I am totally unexperienced in signal processing, please excuse me for any ignorance.
UPD: I am not considering mean filters, because the two pictures below represent only particular stages of evolution. It means that the signal evolves in an unpredictable manner in between. Mean filter usually leads to loss of information about such evolution.