A single-platform underwater maneuvering target motion analysis method based on bearing and frequency measurements

This paper is concerned with the underwater bearing and frequency maneuvering target motion analysis problem. To address the existing problems of requiring known prior center frequency information and the existence of maneuvering detection errors, an adaptive unscented Kalman filter algorithm based on multiple frequencies and bearing (MFB-AUKF) algorithm is presented. The MFB-AUKF algorithm constructs a new target motion analysis model by fusing bearing and multiple frequency information. The center frequencies are introduced into the state vector and the real-time estimation of the center frequencies can be obtained by iteration. The algorithm introduces a time-varying fading factor to adjust the process noise covariance matrix, which makes the MFB-AUKF algorithm capable of processing maneuvering targets. Simulation and sea trial analysis results show that the proposed method achieves better tracking performance and the target motion analysis result can approach the Cramer-Rao lower bound with the fastest speed.