A novel maneuver detector based on back propagation neural network

Abstract

Target maneuvering is always accompanied with rapid attitude variations, which are helpful to achieve high cross-range resolution for coherent pulse radar. In this paper, the high resolution Doppler profile (HRDP) is first formulated. The principle of maneuver detection using HRDP is then fully exploited. The difference of target attitude rates between nonmaneuvering and maneuvering motion mode is analyzed. Due to the nonstationarity of HRDP, the maneuver detection problem is reformulated as a pattern classification problem, where nonmaneuvering and maneuvering motion mode are distinguished. A novel detector is then developed based on the back propagation neural network. Two novel indices for performance evaluation are proposed. They reflect the dynamic performance of the maneuver detector more reasonably than the classical index, average detection delay. Finally, the simulation results show that the proposed detector possesses low detection delay and high detection probability.