Spatio-temporal adaptive radar clutter mitigation in nonstationary environments.

摘要

Sensor failures in large uniform linear arrays cause significant degradation of the conventional beampattern. Simply zeroing the faulty elements results in high beamformer sidelobes. Adaptive beamforming using only the good sensors provides asymptotically optimal array gain only if sufficient training data is available. In highly nonstationary environments, obtaining this asymptotic performance becomes increasingly hard with large arrays. On the other hand, the multi-layer structure of the ionosphere results in multi-mode propagation in high frequency over-the-horizon radar (OTHR), which causes the Doppler spreading of the backscattered clutter. In ideal conditions, the Doppler spectrum is dominated by two strong peaks called the Bragg lines due to resonant backscattering from ocean waves. However, in multi-mode propagation, there are multiple sets of Bragg lines shifted in Doppler due to propagation from different layers of ionosphere, and thus slow moving ships with small Doppler shifts are masked by the clutter. This thesis addresses the problem of radar clutter mitigation in nonstationary environments. Spatial methods for the faulty sensor problem and spatio-temporal methods for Doppler spread clutter problem due to multi-mode ionospheric propagation are discussed in this thesis. The proposed beamspace adaptive channel compensation (BACC) method adaptively reconstructs the receive beams of the full array so that strong directional components have minimal leakage into the adjacent beams. The results of four single snapshot spatial beamforming methods, BACC, minimum variance (MV) adaptive beamforming using an augmented Toeplitz covariance matrix, principal solution beamforming, and conventional beamforming in the presence of faulty sensors are compared in terms of detection performance, and unmasking of targets in estimated range-Doppler spectra. The simulation and real data results suggest that BACC provides as much as 10 dB improvement in signal to clutter-plus-noise ratio compared to conventional methods. For multi-mode ionospheric propagation problem, the proposed wavefront adaptive sensing (WAS) method combines MV adaptive beamforming and blind source separation (BSS). The signal-free covariance matrix is formed from the separated clutter components using BSS, and used in MV beamforming to suppress the clutter. In mismatched steering angle scenarios, WAS outperforms MV adaptive beamforming at high SNR and avoids the threshold effects of BSS at low SNR.