Chaff identification method based on Range-Doppler imaging feature

Chaff is widely used in electronic countermeasures as an effective passive jamming method. This study proposes a chaff identification method that integrates the distribution of distance, Doppler frequency, and power in Range-Doppler imaging. The traditional radar signal processing method estimates the distance and Doppler frequency, and the mean-shift algorithm is combined to complete the clustering after the constant false alarm detection. Then, the target and chaff cloud models are modelled in three dimensions: radar cross-section, range, and Doppler frequency. Using the Neyman-Pearson criterion, three chaff likelihood ratio test detectors are designed based on these assumptions. The three detectors can effectively identify the chaff, and then the theoretical detection probability and influencing factors are analysed. At the same time, the Kullback-Leibler divergence is used to describe the distribution difference between the data to be detected and the theoretical target and chaff. Besides, the different classifiers are used to identify the features of the differences in Kullback-Leibler divergence in three dimensions. Finally, the real-life chaff datasets verified the excellent recognition rate of the authors' method for the chaff.

Automated summary

This study proposes a method for identifying chaff that integrates the distribution of distance, Doppler frequency, and power. The method effectively identifies chaff using multiple detectors and analyzes the theoretical detection probability and influencing factors.