A RLSN-based Current Statistical Model for Target Range Tracking

In classical “Current” statistical model, maneuvering frequency is normally set as typical value, and an acceleration limit has to be set. In order to break this limitation, this paper utilizes recursive linear smoothed Newton method to predict acceleration. An analytical solution of target maneuvering frequency is then achieved, by which the need of setting maneuvering acceleration limit is avoided. Meanwhile, adaptive calculation of process noise covariance matrix is accomplished. Moreover, usage of simplified Sage-Husa filter to realize an adaptive update of measuring noise covariance matrix is established. Simulation demonstrates that the proposed algorithm is with a better range tracking performance.