Anti-intermittent Sampling Jamming Method Based on Frequency Agile Radar and K-means

Frequency agility can greatly improve the low interception and anti-jamming ability of the radar by randomly changing the carrier frequency from pulse to pulse. However, the emergence of intermittent sampling interference makes it face severe challenges. The jammer intermittently samples and rapidly transmits the intercepted radar signal in the time domain, forming a series of false targets in the current pulse repetition interval (PRI). Because those false targets are highly similar to the true targets, the radar cannot detect and track the true target correctly. To deal with the problem, an anti-intermittent sampling jamming method based on frequency agile radar and K-means is proposed in this paper. Firstly, the radar emitted signal is designed in the form of intra-pulse frequency coding and inter-pulse frequency agility. Secondly, the K-means, a machine learning-based algorithm, is applied to judge whether there is jamming in the echo. After the interference being suppressed, the orthogonal matching pursuit (OMP) algorithm is adopted for high resolution range-Doppler reconstruction, realizing the moving target detection (MTD). Theoretical analysis and simulation experiments verify the effectiveness of the proposed method.