I'm very new to the world of signal processing and looking for some guidance.
I have 5 kHz accelerometer data from a car and looking to process this to remove some of the high frequency noise and also determine whether some of the 'spikes' in the data are in fact real events (Some are 6G, so I believe they may not be 'real').
I've read lots of conflicting approaches but one that I keep seeing is the use of a low pass Butterworth filter. I've tried this using python and found that the reduction of noise goes hand in hand with the reduction of magnitude of 'spikes', so I'm now concerned about the removal of data that could be 'real'.
I then went to look at the frequency domain and found most of my high power events are at lower frequencies, giving me 4 distict events (I think);
- How do I determine which approach to use in order to remove any erroneous data points?
- Is there a better method/approach of doing this that I'm yet to come across?