Real-time detection algorithm of Ubiquitous radar detection based on DSP platform

For the problem of detecting "low-slow-small-group targets" using Ubiquitous radar, this study first establishes a systematic radar model. To verify model accuracy, the designed angle discrimination curves were compared with measured antenna pattern discrimination curves from microwave anechoic chamber experiments, showing high consistency that validated model reliability. Subsequently, a real-time processing framework for target detection algorithms was developed based on a DSP platform. To meet real-time requirements, a parallel 2D-OSCACFAR (Two-Dimensional Ordered Statistics Cell Averaging Constant False Alarm Rate) detection algorithm was proposed within this framework. Compared with traditional 2D-OSCFAR (Two-Dimensional Ordered Statistics Constant False Alarm Rate) algorithms, this optimized computational process significantly reduces runtime while maintaining comparable detection performance. Furthermore, a multi-beam position synchronization mechanism enabling real-time processing of multi-beam target information was designed through inter-DSP communication within this framework.Finally, field experiments demonstrated that the proposed real-time detection algorithm effectively identifies LSS group targets while satisfying real-time processing requirements.