In small-aperture high frequency surface wave radar, the main-lobe clutter all can be seen as a more severe space spread clutter under the influence of the smaller array aperture. It compromises the detection performance of moving vessels, especially when the target is submerged in the clutter. To tackle this issue, an improved spread clutter estimated canceller, combining spread clutter estimated canceller, adaptive selection strategy of the optimal training samples and rotating spatial beam method, is presented to suppress main-lobe clutter in both angle domain and range domain. According to the experimental results, the proposed algorithm is shown to have far superior clutter suppression performance based on the real data.

A new method for adaptive digital beamforming technique with compressed sensing (CS) for sparse receiving arrays with gain/phase uncertainties is presented. Because of the sparsity of the arriving signals, CS theory can be adopted to sample and recover receiving signals with less data. But due to the existence of the gain/phase uncertainties, the sparse representation of the signal is not optimal. In order to eliminating the influence of the gain/phase uncertainties to the sparse representation, most present study focus on calibrating the gain/phase uncertainties first. To overcome the effect of the gain/phase uncertainties, a new dictionary optimization method based on the total least squares (TLS) algorithm is proposed in this paper. We transfer the array signal receiving model with the gain/phase uncertainties into an EIV model, treating the gain/phase uncertainties effect as an additive error matrix. The method we proposed in this paper reconstructs the data by estimating the sparse coefficients using CS signal reconstruction algorithm and using TLS method toupdate error matrix with gain/phase uncertainties. Simulation results show that the sparse regularized total least squares algorithm can recover the receiving signals better with the effect of gain/phase uncertainties. Then adaptive digital beamforming algorithms are adopted to form antenna beam using the recovered data.

To solve the problem of nonhomogeneous clutter suppression for moving target detection in High Frequency Surface Wave Radar (HFSWR), a novel nonhomogeneous clutter detector (NHD) is present in this paper. This novel NHD makes an analysis for the clutter constituents with single snapshot based on the over-determined linear equations in space-time adaptive processing (STAP) and distinguish the nonhomogeneous secondary data from the whole secondary data set through calculating the correlation coefficients of the secondary data.

An improved beamformer, which uses joint estimation of the reconstructed interference-plus-noise (IPN) covariance matrix and array steering vector (ASV), is proposed. It can mitigate the problem of performance degradation in situations where the desired signal exists in the sample covariance matrix and the steering vector pointing has large errors. In the proposed method, the covariance matrix is reconstructed by weighted sum of the exterior products of the interferences' ASV and their individual power to reject the desired signal component, the coefficients of which can be accurately estimated by the compressed sensing (CS) and total least squares (TLS) techniques. Moreover, according to the theorem of sequential vector space projection, the actual ASV is estimated from an intersection of two subspaces by applying the alternating projection algorithm. Simulation results are provided to demonstrate the performance of the proposed beamformer, which is clearly better than the existing robust adaptive beamformers.