基于强散射点在线估计的距离扩展目标检测方法

传统的距离扩展目标检测一般在散射点密度或散射点数量先验条件下完成，在目标散射点信息完全未知时检测性能会大幅降低。针对这个问题，该文提出一种基于强散射点在线估计的距离扩展目标检测方法(OESS-RSTD)，该方法利用机器学习中的无监督聚类算法在线估计强散射点数量以及首次检测门限，然后再结合虚警率，确定2次检测门限，最后通过两次门限检测完成目标有无的判决。该文分别利用仿真数据和实测数据进行了试验验证，并和其他算法进行了试验对比，通过虚警概率一定时的信噪比(SNR)-检测概率曲线验证了该文所提方法相对于传统算法有更高的稳健性，且该方法不需要目标散射点的任何先验信息。

Range Spread Target Detection Based on OnlineEstimation of Strong Scattering Points

The traditional range-extended target detection is usually completed under the condition of scattering point density or scattering point number priori. The detection performance is greatly reduced when the scattering point information of the target is completely unknown. To solve this problem, a Range Spread Target Detection method based on Online Estimation of Strong Scattering(OESS-RSTD) points is proposed. Firstly, the unsupervised clustering algorithm in machine learning is used to estimate the number of strong scattering points and the first detection threshold adaptively. Then, the second detection threshold is determined according to false alarm rate. Finally, the existence of the target is determined through two detection thresholds. The simulation data and the measured data are used to verify and compare with other algorithms. By comparing the Signal-to-Noise Ratio (SNR) -detection probability curves of various methods with a given false alarm probability, it is verified that the proposed method has higher robustness than the traditional algorithm, and the method does not need any priori information of target scattering points.