Low observable group targets detection based on space–frequency cascaded adaptive processing for MIMO OTH radar

The spread of sea clutter in the Doppler domain caused by multi-mode propagation would make it difficult for over-the-horizon radar to detect low observable group targets. To solve this problem, a multi-mode clutter suppression method based on space–frequency cascaded adaptive processing is proposed by utilizing the spatial diversity characteristics of multiple-input–multiple-output radar. Then, the low observable group targets can be detected. The proposed method adopts the idea of space–frequency stepwise processing. First, the multi-stage Wiener filter ESPRIT algorithm is exploited to estimate the direction of departure and direction of arrival of different path signals, and then the multi-mode clutter is adaptively transformed into single-mode clutter based on the estimated angles. Second, the atomic norm minimization algorithm and Vandermonde decomposition are used to estimate the steering vector of the first order Bragg frequency of sea clutter, whose path contains the group targets. Finally, the least squares algorithm is used to realize the adaptive suppression of sea clutter in the frequency domain. Because there is no need to estimate the signal covariance matrix and its eigenvalue decomposition and conduct spectral peak searching, the proposed method greatly reduces the computation of the angle estimation. Compared with the existing second-order blind identification and spatial smoothing SOBI algorithms, the proposed algorithm does not need the information of the first order Bragg frequency of sea clutter, so it can improve the output signal-to-clutter-noise ratio when there is an error in the first order Bragg frequency of sea clutter. Therefore, the proposed algorithm is more conducive to the detection of low observable group targets. Simulation results are given to demonstrate the effectiveness of the proposed method.