Waveform Design for Kalman Filter-Based Target Scattering Coefficient Estimation in Adaptive Radar System

• Published in: IEEE transactions on vehicular technology Vol. 67; no. 12; pp. 11805 - 11817
• Main Authors: Chen, Peng, Qi, Chenhao, Wu, Lenan, Wang, Xianbin
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive filters; Adaptive radar system; Adaptive systems; Clutter; Covariance matrices; Design optimization; Estimation; Kalman filter; Kalman filters; non-convex optimization; Optimization methods; Radar; Radar clutter; Radar systems; Scattering coefficient; Semidefinite programming; Target detection; waveform optimization
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2018.2875314

The temporal correlation of target can be exploited to improve the radar estimation performance. This paper studies the estimation of target scattering coefficients in an adaptive radar system, and a novel estimation method based on Kalman filter (KF) with waveform optimization is proposed for the temporally correlated target in the scenario with both noise and clutter. Different from the existing indirect methods, a direct optimization method is proposed to design the transmitted waveform and minimize the mean square error of the KF estimation. Additionally, the waveform is optimized subject to the practical constraints including the transmitted energy, the peak-to-average power ratio, and the target detection performance. With clutter and noise, the waveform optimization problem is non-convex. Therefore, a novel two-step method is proposed and converts the original non-convex problem into several semidefinite programming problems, which are convex and can solve efficiently. Simulation results demonstrate that the proposed KF-based method with waveform optimization can outperform state-of-art methods and significantly improve the estimation performance.