A Study on Sub-array Level Sample Matrix Inversion Anti-interference Algorithm of Polarized Phased Array Radar

In the face of increasingly complex electromagnetic battlefield environments, it is significant to study the engineering anti-interference algorithms for existing polarized phased array radars. To address this issue, we introduce the sub-array level sample matrix inversion anti-interference algorithm of polarized phased array radar, which is derived from the polarization-space joint anti-interference theory of one-dimensional linear arrays and adaptive filtering techniques commonly used in engineering. Firstly, the dual-polarization planar array is divided into sub-arrays to achieve physical dimensionality reduction for subsequent adaptive processing. Then, the sub-array level adaptive weight vector is dynamically adjusted by updating the sample matrix inversion, to suppress strong interference signals and facilitate weak target detection. Finally, the anti-interference performance of the sub-array level sample matrix inversion anti-interference algorithm is further discussed. The simulation results demonstrate that the proposed sub-array level sample matrix inversion anti-interference algorithm of polarized phased array radar can rapidly converge to a stable filtering state, and effectively suppressing multiple main/side lobe interferences.