基于Hough变换和聚类的航迹起始算法

Track Initiation Algorithm Based on Hough Transform and Clustering

基于Hough变换的航迹起始是密集杂波环境中低信噪比目标起始的一种有效方法,但其存在阈值选取困难及一个目标起始多条航迹(航迹簇拥)等问题。提出了一种MeanShift多尺度聚类Hough变换航迹起始算法(MSMSC-HT)。该算法先用低阈值进行航迹初选;然后对初选航迹进行多尺度聚类,并用MeanShift算法求取聚类中心;最后通过尺度寻优自适应地确定航迹数目和航迹参数。该算法通过聚类避免阈值设计的困难,解决了航迹簇拥下航迹的检测与估计问题。仿真结果表明了算法的有效性。

Hough transform based track initiation method has been proposed as an effective means for low signal-to-noise ratio target detection in dense clutter environment. However, it is difficult to choose a proper threshold for peak detection and there exists a problem that multiple tracks might be initialized by only one target. A Mean Shift Multi-Scale Clustering based Hough transform track initiation （MSMSC-HT） algorithm was proposed, which first obtains the rough candidate tracks with a lower threshold, then filters the histograms in a multi-scale way according to the theory of multi-scale space, works out the local maximum of the filtering function under different scale via Mean Shift algorithm, and finally determines the track number and parameters adaptively through the life time of the local maximum number and the drift speed of the position. Simulation results show the efficacy of the proposed algorithm.