Continuous active sonar target detection based on echo features

Compared with traditional pulsed active sonar, continuous active sonar offers significant advantages, providing higher target detection refresh rates and temporal gain through sub-pulse processing. To fully leverage the detection performance of continuous active sonar, the system requires robust detection and tracking capabilities. However, conventional constant false alarm rate algorithms, which rely on energy thresholds, are highly susceptible to strong interference from reverberation and direct waves. Addressing the feature differences between target echoes, reverberation, and direct waves in Bearing-delay figures, this paper first proposes a template matching detection method based on echo energy distribution and correlation coefficients. Building upon this, this paper further proposes a detection method utilizing echo energy histograms and the mean shift algorithm. By employing the mean shift algorithm to search for the probability distribution of moving targets along the figure gradient direction within continuous bearing-delay figures, this method achieves precise target detection. Sea trial results demonstrate that the template matching method effectively overcome interference and achieve stable, continuous target detection.