Enhancing multivariate signalsStarted by 6 years ago●3 replies●latest reply 6 years ago●95 views
I have a set of multivariate signals from an MPU6050 sensor which has three axes for accelerometer measurements and three for gyroscopic measurements (all in X, Y, Z). I now need to treat each axes as an independent signal and enhance it. What are some techniques that can be used in Matlab to achieve this? In addition, are there python equivalents to those?
What do you mean by "enhance"? It's hard to recommend anything without knowing what you're trying to achieve.
I guess 'enhance' would not be the right choice of words but what I have are scaled data from the aforementioned sensor with a timestamp. The goal is to treat each column (axes of measurement) individually such that an energy change can be detected when the whole dataset is colour mapped.
My question is this: What pre-processing can I apply to the different columns such that I can observe an energy change?
Why would you treat all signals independently? There exists methods such as Kalman filter that leverage the multivariate nature of the signal.