Anti-jamming algorithm for radio fuze based on dual-band fusion detection combined with TG-DBSCAN clustering and fitting

With the rapid development of electronic technology, fuze jammers have advanced significantly, severely threatening the normal functionality of radio fuzes. In response, this paper proposes an anti-jamming algorithm for radio fuzes based on dual-band fusion detection, Target-Guided Density-Based Spatial Clustering of Applications with Noise (TG-DBSCAN) clustering, and linear fitting target recognition. First, dual-band detection is used to acquire target and jamming information, and the Constant False-Alarm Rate (CFAR) detection is employed to preliminarily screen the information. Second, a Fusion Detection Results Scatter Plot (FDRS) is designed to fuse detection information by establishing the deterministic relationship between dual-band detection parameters. Next, TG-DBSCAN is proposed to perform clustering on the FDRS, improving the traditional DBSCAN algorithm to meet the practical operational requirements of fuzes. Finally, a target recognition strategy is designed to identify targets and eliminate jamming based on the feature of the fitting results, achieving accurate estimation of target distance. The algorithm does not require complex signal processing, making it easy to deploy in fuze systems. Simulation and experimental results demonstrate that, compared to traditional methods, the proposed algorithm exhibits excellent capabilities in resisting both artificial active jamming and noise interference. It can reliably detect target information even under conditions with a jamming-to-signal ratio (JSR) of 15 dB, a signal-to-noise ratio (SNR) of -15 dB, while maintaining high ranging accuracy and stability.