Intelligent Detection of Low–Slow–Small Targets Based on Passive Radar

Due to its unique geometric configuration, passive radar offers enhanced surveillance capabilities for low-altitude targets. Traditional passive radar signal processing typically relies on energy accumulation and Constant False Alarm Rate (CFAR) detection. However, insufficient accumulation gain or mismatched statistical models in complex electromagnetic environments can compromise detection performance. To address these challenges, this paper proposes an intelligent target detection method for passive radar. Specifically, a residual network is integrated with a Squeeze-and-Excitation (SE) module, which preserves the powerful feature extraction capabilities of the residual network while improving the model’s ability to adaptively adjust channel weights. This fusion effectively enhances the target detection process. Furthermore, based on the particle swarm algorithm, a gray wolf population search strategy and a multi-target iterative search mechanism are introduced to enable the rapid extraction of time-frequency difference parameters for multiple targets. Both simulation and field experiments demonstrate that the proposed method enables intelligent detection of low–slow–small targets in passive radar, ensuring efficient time-frequency parameter extraction while maintaining a high detection success rate.