How Are GM and Auto-Regression Models Used in GPS/INS Systems?

[ramesses]

Hi

I'm using INS with GPS. and I have some question about noise.

After denoising measures from high frequency noise with wavelet. the short frequency will stay on measures.

we generally GM or Auto-regression to model the remain error.

Is this models used only for kalman filter or it can be used for remove the noise also ?

 

[Achy47]

Hello,

Thank you for your question. The use of INS (Inertial Navigation System) with GPS (Global Positioning System) is a common practice in navigation and positioning systems. As you have rightly pointed out, denoising measures from high frequency noise is an important step in improving the accuracy of the system.

Regarding your question about using GM (Gaussian Model) or Auto-regression to model the remaining error, these models can be used for both Kalman filtering and removing noise. Kalman filtering is a popular technique used in navigation systems to combine measurements from multiple sources and estimate the most accurate position and velocity of a moving object. In this process, the remaining error is modeled using either GM or Auto-regression to improve the accuracy of the estimated position and velocity.

However, these models can also be used for removing noise from the measurements. In this case, the models are used to estimate the noise component in the measurements and then subtract it from the original measurements to obtain a denoised signal. This can be helpful in situations where the noise is not purely random and has a certain pattern or structure that can be modeled using these techniques.

I hope this answers your question. If you have any further doubts, please feel free to ask.