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Kalman filtering and combined navigation
Nov 21, 2018

If the signal is disturbed by noise, in order to recover the useful signal from the measurement and minimize the influence of interference, a filter is often used for signal processing. The classical filter is assumed to have a different frequency distribution of the signal and the interference, and the separation of the wanted signal and interference is achieved by designing a specific filter bandpass and band stop band. However, if the frequency band of interference is very wide, such as white noise, interference will inevitably exist in the frequency range of the wanted signal, and the classical filter is incapable of filtering out this part of the interference noise. If the frequency bands of the wanted signal and the interference noise overlap each other, the signal processing usually no longer considers that the useful signal is deterministic but has some randomness. It is impossible to perform accurate and error-free recovery for random signals. It can only be estimated by mathematical statistics based on the statistical characteristics of signals and noise, and generally adopt some statistical criteria to make the estimation error as small as possible. Borrowing the terminology of classical filters, this method of statistical estimation for random signals is often referred to as a filter, or as a modern filter to distinguish it from classical filters, but it must be noted that there is a difference between classical filters and modern filters. Essentially different.

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