System Optimization for Temporal Correlated Cognitive Radar with EBPSK-Based MCPC Signal

The system optimization is considered in cognitive radar system (CRS) with extended binary phase shift keying- (EBPSK-) based multicarrier phase-coded (MCPC) signal. A novel radar working scheme is proposed to consider both target detection and estimation. At the detection stage, the generalized likelihood ratio test (GLRT) threshold is deduced, and the GLRT detection probability is given. At the estimation stage, an approach based on Kalman filtering (KF) is proposed to estimate target scattering coefficients (TSC), and the estimation performance is improved significantly by exploiting the TSC temporal correlation. Additionally, the optimal waveform is obtained to minimize the mean square error (MSE) of KF estimation. For the practical consideration, iteration algorithms are proposed to optimize the EBPSK-based MCPC signal in terms of power allocation and coding matrix. Simulation results demonstrate that the KF estimation approach can improve the estimation performance by 25% compared with maximum a posteriori MAP (MAP) method, and the KF estimation performance can be further improved by 90% by optimizing the transmitted waveform spectrum. Moreover, by optimizing the power allocation and coding matrix of the EBPSK-based MCPC signal, the KF estimation performances are, respectively, improved by 7% and 8%.