Abstract:

A novel Suboptimal Fading Square Root Central Difference Kalman Filter (SFSRCDKF) algorithm is presented. This algorithm can enhance the observability, increase the convergence speed and improve the locating accuracy in the Single Observer Passive Location (SOPL). It uses adaptive suboptimal fading factor restricted by the orthogonality principle to real-time adjust the gain matrix, which can ensure the orthogonality of the new observation residuals at different time. Therefore, the response speed and the adaptive correction capability of the filter are improved when the state changes or the estimation is biased. The new filter also uses square root of the error covariance instead of the error covariance involved in filtering to ensure numerical stability. Simulation results show that, under different conditions, the new algorithm performs more stably with higher convergence speed and higher locating accuracy.

Key words: information processing technology, single observer passive location, adaptive suboptimal fading factor, central difference Kalman filter, observartion residuals