Mainlobe suppression jamming significantly degrades radar detection performance. The conventional blind source separation (BSS) algorithms often fail under high-jamming-to-signal-ratio (JSR) and low-signal-to-noise-ratio (SNR) conditions. To overcome this limitation, we propose an enhanced BSS method combining variational mode decomposition (VMD) and wavelet packet decomposition (WPD), termed VMD-WPD-JADE. The proposed approach first applies VMD-WPD for noise reduction in radar signals and then utilizes the JADE algorithm to compute the separation matrix of the denoised signals, effectively achieving blind source separation of radar echoes for interference suppression. We evaluate the method using noise-amplitude modulation and noise-frequency modulation jamming scenarios. The experimental results show that at a JSR = 50 dB and an SNR = −5 dB, our method successfully separates the target signals. Compared with the conventional blind source separation (BSS) algorithms, the proposed technique demonstrates superior robustness, achieving a 4–11% improvement in the target detection probability under noise-amplitude modulation (NAM) jamming and a 4–16% enhancement under noise-frequency modulation (NFM) jamming within a signal-to-noise ratio (SNR) range of −5 dB to 5 dB.

In the field of electromagnetic countermeasures, suppressing mainlobe jamming represents a critical challenge requiring urgent resolution. Conventional polarization-based anti-jamming techniques, which fundamentally rely on obtaining pure jamming signals for prior parameter estimation, demonstrate limited effectiveness against co-frequency mainlobe suppression jamming. To tackle this problem, this paper proposes an innovative joint polarization-range-Doppler processing framework for airborne dual-polarized radar systems. Initially, we develop a polarized eigen-element surrogate technique to accurately estimate jamming polarization parameters, which demonstrates robust performance even under low jamming-to-signal ratio conditions. Subsequently, through Doppler compensation and range processing, we establish a combined feature projection method capable of reliably estimating target polarization from mixed signals containing target echoes, jamming, and noise. Then, leveraging the obtained polarization information, we construct an optimal target polarization projection filter. To comprehensively evaluate system performance, we introduce the novel metric of signal loss ratio, enabling rigorous analysis of the filter’s operational boundaries from dual perspectives: jamming suppression capability and target signal preservation. Extensive simulations across six distinct operational scenarios conclusively demonstrate the method’s superior performance, confirming its significant potential for practical implementation in engineering applications.

Uniform circular array electromagnetic vector sensor (UCA-EVS) can provide omni-directional and high-resolution azimuth information and polarization information, which has been widely concerned in the field of radar signal processing. Aiming at the joint estimation of direction of arrival (DOA) and polarization parameters of coherent sources via UCA-EVS, a multi-dimensional parameter estimation method with low complexity—polarization-DOA matrix method—is proposed. First, the rank of array covariance matrix is recovered by axial virtual translation, and then the direction and polarization parameters of the signal are estimated by using the eigenvalues and eigenvectors of the matrix based on constructing polarization-DOA matrix. Different from the traditional DOA matrix method, the proposed algorithm can not only estimate the azimuth information of the signals but also provide the polarization information of the targets, and the estimated parameters can be matched automatically. At the same time, it can estimate the parameters only by using the information of three elements, which can save hardware resources. In addition, the proposed method does not need to search for spectral peaks, which not only greatly reduces the computational complexity but also loses the estimation accuracy. Simulation results verify the feasibility of the proposed algorithm.

Radar target detection in a sea clutter environment is of significant importance in both civilian and military applications, with the detection of small maneuvering targets being particularly challenging. To address this issue, this paper introduces the autocorrelation characteristics of sea clutter into orthogonal projection operations to suppress sea clutter and enhance the detection capability of small maneuvering targets on the sea surface. The proposed method first generates speckle components that are consistent with the correlation characteristics of the observed sea clutter. Then, it uses these speckle components to derive the feature subspace of the sea clutter and applies this subspace in an orthogonal projection suppression algorithm, thereby achieving effective suppression of the sea clutter. This method does not rely on the covariance matrix estimation of sea clutter from reference cells but instead directly utilizes the autocorrelation characteristics of the observed sea clutter data to obtain the feature subspace, making it more adaptable to different environments. Simulation and experimental results demonstrate that this method significantly suppresses sea clutter and effectively improves the performance of target detection on the sea surface.

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 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.