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基于回波特征的连续主动声呐目标检测

Continuous active sonar target detection based on echo features

  • 摘要: 相比传统脉冲主动声呐,连续主动声呐优势显著,可通过子脉冲处理提供更高的目标检测刷新率与时间增益。为充分发挥连续主动声呐的探测性能,系统需要具备稳定的检测和跟踪能力。然而,常规的恒虚警(CFAR)检测算法基于能量阈值,极易受混响和直达波强干扰的影响。针对回波、混响与直达波在回波方位-时延图像上的特征差异,本文提出了一种基于回波能量分布与相关系数的模板匹配检测方法。进一步地,本文提出了基于回波能量直方图与均值漂移的模板匹配检测方法。该方法在连续的方位-时延图像中利用均值漂移算法沿图像梯度方向进行搜索运动目标的概率分布,从而实现对目标的精准检测。海试结果表明,模板匹配检测方法能有效克服干扰,实现对目标的稳定、连续检测。

     

    Abstract: 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.

     

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