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.

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.

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.

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.