Cognitive radar waveform optimization based on Kalman filtering for target estimation

Abstract

As the target-radar feature is time-variant in the cognitive radar (CR) system, the target information should be continuously updated by the receiver and considered to provide the prior knowledge for the optimization of the next waveform. To solve this problem, a two-step CR waveform optimization approach for target estimation is proposed. During the first echo pulse, waveform optimization for target-radar signature estimation is done by minimizing the mean square error of target power spectral density estimation. Then, to take advantage of the temporal correlation of target scattering coefficients (TSC) during the pulses interval, a Kalman filtering-based method is used to processing successive radar echoes for TSC estimation. A convex cost function is established and the optimal solution can be obtained by the existing convex programming algorithm with multiple iterations. Finally, subject to the transmitted power, peak-to-average power ratio, and detection performance constraints, the simulation results show that the proposed waveform optimization algorithm is able to improve the performance of target estimation for extended target.