航向角辅助的模糊数据关联算法

为了在不增加运算复杂度的条件下有效提高多目标跟踪的跟踪精度和关联正确率,本文提出了一种采用航向角进行辅助的多目标模糊数据关联新方法。算法首先分析航向和距离信息是区分不同航路的有效参数，然后介绍了航向角的定义及求解方法，即利用当前时刻的雷达测量和前一时刻的滤波状态向量计算目标的测量航向角，并通过容积卡尔曼滤波器（Cubature Kalman Filter, CKF）对包括目标航向在内的状态向量进行更新，利用模糊逻辑推理进行多目标数据关联。实验结果表明，提出算法与传统的最近邻方法（Nearest Neighbor, NN）相比具有较高的关联正确率，与联合概率数据关联方法（Joint Probability Data Association, JPDA）相比，在保证关联正确率的前提下跟踪精度和运算效率均得到了较大提高，更适合工程应用。 

The Heading-angle aided Data Association Algorithm based on Fuzzy Logic

In order to improve the tracking accuracy and the data association accuracy without increasing the computational complexity, a new data association method with the heading angle aided is proposed for the multi-target tracking based on the fuzzy logic inference system in this paper. Firstly, the heading-angle and distance are analyzed to be the effective parameters for separating the different trajectories, the definition of heading-angle and the method of how to calculate the heading-angle have been given, the measurement heading-angle could be calculated using the radar measurement at the current moment and the updated state vector of the target at the previous moment, the state vector of the target including the measurement heading-angle is updated using the cubature Kalman filter (CKF), then the fuzzy logic inference system is used for data association of multi-target tracking. Simulation results show that the proposed method has better association accuracy than the Nearest Neighbour (NN) algorithm, and in guarantee of the association accuracy, the tracking accuracy and the operation efficient are improved than the Joint Probability Data Association (JPDA) algorithm. It is fit for the real application.