复杂场景下多扩展目标跟踪在自动驾驶、目标识别等领域具有很高的应用价值。文中提出了一种基于椭圆随机超曲面模型(ERHM)的势概率假设密度(CPHD)滤波器。首先,基于有限集统计理论,利用CPHD 滤波器建立多扩展目标的贝叶斯滤波框架;然后,采用ERHM 描述扩展目标的量测源分布,并利用无迹变换嵌入CPHD 滤波流程;最后,仿真实验结果表明,ERHM-CPHD 滤波器对椭圆扩展目标的跟踪性能优于传统的伽马高斯逆威沙特CPHD 滤波器,在杂波密度较高、目标新生的位置比较确定的场景或者扩展目标数目较多时,对扩展目标的参数估计更为准确。所提方法在高分辨率雷达多目标跟踪方面具备很好的运用前景。

Multiple extended targets tracking in complex scenes has high application value in fields such as autonomous driving and target recognition. A cardinalized probability hypothesis density (CPHD) filter built on elliptic random hypersurface model (ERHM) is presented in this paper. Firstly, based on the theory of finite set statistics, the Bayesian filtering framework of multiple extended targets is established by using CPHD filter. Then, ERHM is used to describe the measurement source distribution of the extended target, and unscented transform is used to embed the CPHD filtering process. Lastly, the simulation results show that the tracking performance of the proposed ERHM-CPHD filter is better than that of the traditional gamma Gaussian inverse Wishart CPHD (GGIW-CPHD) filter, and the parameter estimation of the extended targets is more accurate, when the clutter density is high and the position of newly generated targets is determined or the number of multiple extended targets is relatively large. The proposed method has good application prospects in using high-resolution radar for multi-target tracking.

TN957.52