Mainlobe Jamming Suppression Using MIMO-STCA Radar

TL;DR

This paper presents advanced mainlobe jamming suppression techniques using a novel MIMO-STCA radar with enhanced degrees of freedom, introducing algorithms for interference sample selection and robust noise subspace mitigation to improve anti-jamming performance.

Contribution

The study develops a MIMO-STCA radar framework with additional DoFs and proposes novel algorithms for interference sample selection and adaptive noise subspace mitigation, improving mainlobe jamming suppression.

Findings

Significant improvement in mainlobe jamming suppression demonstrated.

Enhanced SINR and beampattern control verified through simulations.

Robust algorithms maintain performance under non-ideal conditions.

Abstract

Radar jamming suppression, particularly against mainlobe jamming, has become a critical focus in modern radar systems. This article investigates advanced mainlobe jamming suppression techniques utilizing a novel multiple-input multiple-output space-time coding array (MIMO-STCA) radar. Extending the capabilities of traditional MIMO radar, the MIMO-STCA framework introduces additional degrees of freedom (DoFs) in the range domain through the utilization of transmit time delays, offering enhanced resilience against interference. One of the key challenges in mainlobe jamming scenarios is the difficulty in obtaining interference-plus-noise samples that are free from target signal contamination. To address this, the study introduces a cumulative sampling-based non-homogeneous sample selection (CS-NHSS) algorithm to remove target-contaminated samples, ensuring accurate interference-plus-noise covariance matrix estimation and effective noise subspace separation. Building on this, the subsequent step is to apply the proposed noise subspace-based jamming mitigation (NSJM) algorithm, which leverages the orthogonality between noise and jamming subspace for effective jamming mitigation. However, NSJM performance can degrade due to spatial frequency mismatches caused by DoA or range quantization errors. To overcome this limitation, the study further proposes the robust jamming mitigation via noise subspace (RJNS) algorithm, incorporating adaptive beampattern control to achieve a flat-top mainlobe and broadened nulls, enhancing both anti-jamming effectiveness and robustness under non-ideal conditions. Simulation results verify the effectiveness of the proposed algorithms. Significant improvements in mainlobe jamming suppression are demonstrated through transmit-receive beampattern analysis and enhanced signal-to-interference-plus-noise ratio (SINR) curve.