How to differentiate a time series while minimizing noise in the signal?
I have a timeseries that represents the X coordinate of the 3D position of a point (see top plot in image, please ignore vertical lines as they’re not relevant). I want to compute its velocity, acceleration, and jerk. I am currently just using diff() but as you can see in the 3rd & 4th plots, the noise increases so much that it’s not very useful.
I know that differentiating will always introduce some noise. But how can I filter and/or differentiate this signal with minimal noise added?
I tried looking into FFT and Mathworks’ page on derivatives using digital signal processing techniques but am at a loss currently.
Another idea I had is to fit an exponential function to the data, then differentiate that and interpolate it, but I’m not sure if that’s the right approach.
Also, the sampling rate of my data is 250 Hz, X axis tick labels are indices.I have a timeseries that represents the X coordinate of the 3D position of a point (see top plot in image, please ignore vertical lines as they’re not relevant). I want to compute its velocity, acceleration, and jerk. I am currently just using diff() but as you can see in the 3rd & 4th plots, the noise increases so much that it’s not very useful.
I know that differentiating will always introduce some noise. But how can I filter and/or differentiate this signal with minimal noise added?
I tried looking into FFT and Mathworks’ page on derivatives using digital signal processing techniques but am at a loss currently.
Another idea I had is to fit an exponential function to the data, then differentiate that and interpolate it, but I’m not sure if that’s the right approach.
Also, the sampling rate of my data is 250 Hz, X axis tick labels are indices. I have a timeseries that represents the X coordinate of the 3D position of a point (see top plot in image, please ignore vertical lines as they’re not relevant). I want to compute its velocity, acceleration, and jerk. I am currently just using diff() but as you can see in the 3rd & 4th plots, the noise increases so much that it’s not very useful.
I know that differentiating will always introduce some noise. But how can I filter and/or differentiate this signal with minimal noise added?
I tried looking into FFT and Mathworks’ page on derivatives using digital signal processing techniques but am at a loss currently.
Another idea I had is to fit an exponential function to the data, then differentiate that and interpolate it, but I’m not sure if that’s the right approach.
Also, the sampling rate of my data is 250 Hz, X axis tick labels are indices. derivative, time series, noise, digital signal processing, signal processing, acceleration, jerk, velocity MATLAB Answers — New Questions