Multi-DRFM Jamming Localization Method Using hybrid TDOA-AOA Measurements in Distributed Array Radar

This paper focuses on locating passively multiple digital radio frequency memory (DRFM) jammers in the three-dimensional (3D) space, using hybrid measurements of time difference of arrival (TDOA) and angle of arrival (AOA) observed by a distributed array radar. The method mainly comprises two steps: the grouping of multiple DRFM jamming signals and the jamming localization. Initially, jamming feature sets are generated by extracting amplitude distribution features from the received echoes. Next, leveraging within-cluster compactness and between-cluster separation of different DRFM jamming in the feature sets, we introduce an improved K-means clustering algorithm termed Davies-Bouldin index-based K-means (DBI-K-means). This algorithm integrates the Davies-Bouldin index into the traditional K-means clustering algorithm to improve cluster formation without prior information, enabling accurate estimation of the number of jammers and signal grouping. Furthermore, we propose a closed-form solution method based on multi-station TDOA and single-station AOA measurements. Numerical simulations validate the effectiveness of the proposed method and investigate the impact of AOA measurement errors on its performance.