自适应选取聚类中心K-means航迹起始算法

Algorithm of multi-radar multi-target track initiation based on adaptive K-means clustering

摘要:

为揭示多传感器观测数据的正态分布态势,实现对源于异类目标的跟踪,提出一种新的多传感器航迹起始算法,本算法主要特点是初始聚类中心的自适应选取以及对逻辑估计法的起始夹角修正．估计算法中采用不相似性度量阈值的角度衡量方法,同时还结合聚类数目自适应归纳及初始聚类中心的推演逼近,从而使单传感器的航迹起始估计算法可以应用于多传感器的航迹起始根据;然后对聚类后的数据采用修正的逻辑航迹起始算法起始目标航迹．蒙特卡洛估计表明,新的自适应K-means聚类估计区分呈团状分布的不同目标的能力好,且通过估计算法得到的目标非常接近真实目标位置．经过自适应聚类处理后的目标航迹起始估计可有效滤除杂波干扰,降低虚警概率,能够获得较好的多传感器航迹起始．

Abstract:

According to the feature that the measurements of the same target at the same time have spherical shape, an algorithm of track initiation based on adaptive K-means clustering and modified logic-based approach is proposed in this paper. The improved K-means clustering algorithm can determine the cluster number and the initial cluster centers adaptively. Then the center of each cluster is found and taken as the measurement of the targets at this moment. By doing so, the track initiation process is simplified. According to the target’s movement characteristic, a modified logic-based method is used to initiate the target track. Simulation results show that the improved K-means clustering algorithm can recognize the number of targets correctly and the recognized targets are close to the true targets; the modified logic-based approach can effectively suppresses clutter and reduces the probability of false alarm.