A Situational Awareness-Based Off-Board Active Mainlobe Interference Suppression Method

A method for suppressing off-board active mainlobe interference based on situational awareness is proposed to address the challenge of inaccurate real target tracking and detection in linear frequency modulation (LFM) radar systems under dynamic confrontation scenarios. Initially, a cognitive closed-loop integrated interference suppression framework is outlined, encompassing interference cognition, weight determination, and cognitive mainlobe interference suppression. Subsequently, a temporal sequence control mainlobe interference suppression method is proposed. This method dynamically adjusts the weights of sum and difference receiving channels of the LFM radar system to combat mainlobe interference by analyzing the temporal sequence of various signal types in radar echoes. Furthermore, an unsupervised hybrid cognition model for interference cognition and parameter estimation is proposed to support the temporal control mechanism. Signal temporal sequence and type information is achieved by combining complex domain features. Ultimately, the dynamic interference suppression effect of the cognitive closed-loop system is realized. The effectiveness of the proposed method is validated through simulation and over-the-air experiments, demonstrating an 18.7 dB improvement in the signal-to-interference-plus-noise ratio (SINR) compared to the traditional non-weighted strategy.