航向角辅助的高斯混合PHD模糊滤波方法

The Course Angle aided Gaussian Mixture PHD Fuzzy Filter

  • 摘要: 为了更好的解决目标数未知或随时间变化的多目标跟踪问题, 针对高斯混合概率假设密度滤波器(GMPHD)的局限性,提出了非线性条件下的航向角辅助的GMPHD滤波算法。本文给出采用测量数据计算航向角的方法,将航向角与观测向量组成复合观测向量,在跟踪过程中提高了对目标位置的估计精度;利用测量数据生成新目标密度,提高了目标数的估计精度;同时,本文在非线性高斯条件下,将求容积卡尔曼滤波(CKF)引入计算目标状态的预测和更新分布,取得了很好的效果;最后利用模糊方法确定了各个目标的运动轨迹。实验结果表明,本文提出的算法不但能给出目标的运动轨迹而且在目标的位置、速度和目标数的估计精度上都有明显的提高。

     

    Abstract: In multi-target tracking systems, the number of the targets is unknown or varied with time, In this paper, The course angle aided Gaussian mixture PHD filter is proposed according to the limitations of Gaussian mixture PHD filter in the non-linear Gaussian condition. Firstly, the method of making use of radar measurements to calculate course angle is proposed and the course angle is combined with range and azimuth as a composite measurement, the tracking precision of target position is improved; Then the measurements are used to generate the target-birth PHD, the estimated number of the targets is more accurate; In addition, the cubature Kalman filter (CKF) is introduced to calculate the prediction and update distributions of target states. Finally, fuzzy method is used to determine targets’ tracks. The simulation results demonstrate the state trajectorys are provided and the improved performance of the proposed algorithm at target position, velocity and number of the targets.

     

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