Liyuan Wang

In scenarios where mainlobe interference and steering vector (SV) mismatch coexist, conventional robust adaptive beamforming techniques are prone to main-beam distortion, desired signal self-cancellation, and degradation of output performance. To address this problem, this paper proposes a mainlobe interference suppression method based on robust adaptive beamforming in the space-polarization joint domain. Firstly, the extended form of the Maximum Entropy Power Spectrum (MEPS) for polarization-sensitive arrays is theoretically derived. Owing to the high-resolution capability of MEPS under limited snapshots and multiple closely spaced mainlobe interferences, the Capon power spectrum is replaced by it for covariance matrix reconstruction. Then, a convex optimization problem with newly designed constraints is formulated to estimate the SV of the desired signal, which prevents the estimated SV from biasing toward interference directions. Subsequently, diagonal loading technology is introduced to further strengthen the robustness of the proposed method. Simulations are conducted under three typical SV mismatch scenarios. The results show that the proposed method exhibits stronger robustness under the coexistence of mainlobe interference and SV mismatch, achieving output performance closer to the theoretical optimum.

Since the heavy clutter seriously restricts the ability of radar to detect target, it is significant to build the target detector under heavy clutter. For practical situations without the secondary data or prior knowledge of target and clutter, this paper proposes a polarization-space-time detector. First, a general radar model is constructed for multiple pulses, multiple arrays, and multiple polarizations. Based on the theory of ternary hypothesis, the secondary data free (SDF) GLRT detector is proposed, which can maintain the constant false alarm probability (CFAR) in inhomogeneous clutter. Then, this paper proposes a matrix transform operator and an adaptive detection method using sliding window respectively, which do not need to know the steering vector of radar and the noncentral parameter of clutter in advance, so that the SDF-GLRT detector can adapt to different application scenarios. In addition, this paper optimizes the polarization waveform of the radar system by constructing a projection matrix, which can give the closed-form solutions of the optimal polarization and worst polarization, instead of relying on numerical solution. Finally, the performances of SDF-GLRT detector and other three detectors are compared by simulated and real data, which verifies that SDF-GLRT detector in this paper can still maintain superior performance in various clutter environments without secondary data.

To address robust polarization-domain adaptive anti-jamming for dual-polarized radars with limited secondary data and time-varying interference, this paper proposes a covariance-reliability-driven MVDR framework based on forgetting-factor covariance estimation and adaptive diagonal loading. The forgetting-factor recursion assigns larger weights to recent jammer-plus-noise snapshots to track nonstationary interference, while the adaptive loading coefficient is jointly controlled by sample deficiency and covariance condition-number degradation to improve inversion stability. Unlike many robust adaptive beamforming methods that require steering-vector uncertainty sets, mismatch distributions, or subspace information, the proposed method relies only on secondary data and a small set of scalar design parameters. Simulation results based on a synthetic dual-polarized array model show that the proposed method achieves competitive output SINR, effective jammer suppression, and improved robustness to moderate DOA and polarization mismatch under limited-snapshot and time-varying interference conditions. Complexity analysis indicates that the proposed method has the same dominant computational order as standard covariance-based MVDR beamforming, apart from condition-number evaluation. The present validation is simulation-based, and further verification using measured polarimetric radar data, realistic propagation models, or hardware experiments is still required.

Adaptive beamforming based on polarization-sensitive arrays enables joint spatial–polarization filtering for mainlobe interference suppression, but mainlobe distortion and performance degradation occur when the received data include the desired signal or multiple mainlobe interferences. Accordingly, this paper proposes a mainlobe interference suppression method based on Polarimetric Sparse Iterative Covariance-based Estimation (POL-SPICE) and covariance matrix reconstruction. This method utilizes the POL-SPICE algorithm to accurately estimate the direction of arrival (DOA), polarization, and power parameters. It reconstructs the covariance matrix by nulling the corresponding source power and constructs a feature projection matrix to preprocess the received signal. These eliminate the impact of the desired signal and mainlobe interference components on subsequent joint spatial–polarization domain beamforming, ultimately achieving interference suppression and mainlobe shape preservation. Simulation results illustrate that the proposed method is applicable to scenarios with the coexistence of the desired signal and multiple mainlobe interferences, and its superiority over existing methods is verified.

To address the poor performance of conventional sea clutter suppression methods when target echoes share the same frequency as sea clutter, this paper proposes a suppression approach based on feature transformation. The autocorrelation matrix of sea clutter is first employed to construct a feature transformation matrix, through which the received radar data are normalized in the feature space. A differential operation is then applied to cancel clutter components, and the residual energy is combined with a cell-averaging constant false alarm rate (CA-CFAR) detector to achieve target detection in co-frequency scenarios. The proposed method requires no additional prior information and provides a new solution for detecting low-speed maritime targets. Simulation results demonstrate that the method significantly improves both signal-to-clutter ratio (SCR) and detection probability under various sea states and SCR conditions, with particularly notable advantages when the target frequency is close to the sea clutter spectral peak.

To address the challenge of strong sea clutter masking weak target signals for shore-based radar under complex sea states, this article proposes a complex-valued single-stage sea clutter suppression network (CV-SCSNet) which is characterized by a multilevel residual structure. The interpretability of the network is enhanced by establishing a theoretical linkage between traditional subspace cancellation methods and residual learning. To mitigate the challenges posed by the nonuniform and nonstationary characteristics of sea clutter, a Feature Residual Attention Module (FRAM) is designed by incorporating large-kernel depthwise separable convolutions, a nonlinear feature fusion process, and an attention mechanism. This design enables the fusion of multiscale features and contextual information, thereby improving the network’s feature extraction capabilities. Furthermore, a complex-valued Huber loss function and an evaluation metric that considers target reconstruction performance are constructed to ensure training robustness and evaluation accuracy. Experimental results demonstrate that under input Signal-to-Clutter Ratio (SCR) conditions of down to $-$20 dB, the proposed CV-SCSNet can maintain the distortion rate of the reconstructed target below 20% on the radar scan evaluation dataset for sea states 3–4. It achieves a net SCR gain of over 40 dB, showcasing excellent clutter suppression performance and target reconstruction capability.

To address the challenge of detecting sea surface targets under complex non-Gaussian sea clutter backgrounds, this paper proposes a general adaptive detection method based on amplitude distribution mapping. First, a mapping relationship is established between non-Gaussian sea clutter data and the Rayleigh distribution, enabling unified processing of various non-Gaussian clutter environments. Based on this, a Generalized Likelihood Ratio Test (GLRT) detector suitable for the mapped clutter data is derived. The proposed detector does not require parameter estimation of the sea clutter’s amplitude distribution model, thereby exhibiting strong adaptability to non-Gaussian sea clutter. Simulation and real sea clutter data experiments demonstrate that the proposed method outperforms conventional adaptive target detectors in terms of detection performance.

Aiming at the problem that the anti-range-deception-jamming effect of a networked moving radar system is severely affected by the spatial distribution of each radar, an anti-range-deception-jamming method for networked moving radar based on trajectory optimization is proposed. Firstly, the anti-jamming method of networked moving radar considering the radar position error (RPE) is proposed. Then, the theoretical expression for the false target (FT) misjudgment probability of networked moving radar is deduced. Based on the theoretical expression, a trajectory optimization model is formulated to minimize FT misjudgment probability. Simulation experiments validate both the correctness of the derived probability expression and the significant influence of the radar spatial distribution position on the FT misjudgment probability. Moreover, the simulation results verify that the proposed anti-jamming method can effectively reduce the FT misjudgment probability of networked moving radar under the condition of a high discrimination probability of the physical target (PT).