STUDY OF OPTIMAL LINEAR METHODS OF FILTERING AND PREDICTION OF NAVIGATION SIGNALS

Vinogradova Ekaterina Denisovna, Ivanov Yuri Pavlovich

As one of the potential alternatives to Kalman filtering and prediction, this article considers finite-time feedback, spectral-finite free optimal methods of filtering and predicting discrete navigation signals. The methods presented are versatile and easy to implement, with their accuracy and dynamic signal processing characteristics asymptotically approaching those of Kalman algorithms depending on the value of memory capacity r (number of measurement results). In the course of the research, a comparative analysis is performed of Kalman filtering and predicting methods with finite-time and spectral-finite optimal methods for processing and predicting navigation signals, with and without feedback, in terms of accuracy, transition time, robustness, and noise immunity. A linear discrete measurement model is considered with an additive error, a Gaussian Markov stationary random process is used as a useful signal, and uncorrelated stationary white Gaussian noise is used as interference.

Keywords:finite-time processing, spectral-finite processing, Kalman filtering and prediction, optimal filtering, optimal prediction, estimation of accuracy and dynamic properties.