基于模板匹配的集中式多传感器群内目标精细跟踪算法

为解决多传感器探测下群内目标精细跟踪的难题,基于非机动情况下各探测周期内群内目标真实回波位置相对固定的特性,提出了一种基于模板匹配的集中式多传感器群内目标精细跟踪算法。该算法通过预关联成功的群状态集合与群量测集合分别建立模板形状矩阵和待匹配形状矩阵,利用匹配搜索模型和匹配矩阵确认规则选出代价最小的匹配矩阵,并基于模板和对应的匹配矩阵利用 kalman滤波完成群内各目标航迹的状态更新。仿真表明,与传统多传感器多目标跟踪算法中性能优越的基于数据压缩的集中式多传感器多假设算法相比,该算法在跟踪精度、实时性、有效跟踪率方面的性能明显优越,能很好的满足群内目标精细跟踪的实际工程需求。

Centralized Multi-Sensor Refined Tracking Algorithm within Targets Inside Cluster Based on Template Matching

Aiming to solve the track refined tracking problem of the targets inside cluster with the multi-sensor detections,based on the relative invariant of the actual positons of the targets inside cluster in each detection period, a new algorithmnamed centralized multi-sensor refined tracking algorithm within targets inside cluster based on template matching wasproposed. In the algorithm, the template shape matrix and the shape matrix to be matched were respectively obtained withthe previous associated group state set and group measurement set. The least-cost matching matrix was obtained with thematching search model and the matching matrix validation rules. Moreover, based on the template and the correspondingmatching matrix, the state update of each track within the targets inside cluster was completed with the Kalman filter. Theanalysis results of the simulation data showed that obvious advantages of this algorithm were esrablished in the aspects oftracking accuracy, real-time performance and effective tracking rate, compared with the multisensor multipled hypothesisalgorithm based on data compressing technic which waqs a superior performance algorithm in the traditional multi-sensormulti-target tracking field. The real engineering requirement of the refined tracking of the targets inside cluster was metvery well with this algorihtm.