Abstract
In this paper, the radar cross section (RCS) estimation performance is improved by cognitive radar waveform optimization in both angle and frequency domain. F-35 stealth target is modeled by physical optics (PO) approximation method. The recognition of target characteristics by cognitive radar is carried out from the perspective of maximum observation of RCS. The waveform optimization problem is formulated using maximum a posteriori probability (MAP) and Kalman filtering (KF) to estimate RCS of the stealth target. The minimum mean square error (MMSE) is taken as the objective function which is solved by different nature inspired waveform optimization (NIWO). It is demonstrated through computer simulations that the proposed method can reconstruct RCS of the stealth target and outperforms the traditional semi-definite relaxation (SDR) technique, showing a promising method of waveform optimization for stealth target RCS reconstruction in cognitive radar.
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