In this paper, an error-ellipse-resampling-based particle filter (EER-PF) algorithm is proposed for target tracking in wireless sensor networks. In order to improve the effectiveness of the particles, in the process of resampling, the error ellipse of different confidence levels is established according to the error covariance matrix of particles. The particles are divided into different levels based on the geometrical position, and then the particles are screened and optimized. The effectiveness of the proposed method in a cumulative error optimization was verified by comparing with the performance of posterior Cramér-Rao lower bound (PCRLB). Experimental results show that the proposed algorithm can effectively solve the problem of sample degeneracy and impoverishment, and has higher positioning accuracy.

中文翻译：

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基于误差椭圆重采样的目标跟踪粒子滤波算法

在本文中，提出了一种基于误差椭圆重采样的粒子滤波器（EER-PF）算法用于无线传感器网络中的目标跟踪。为了提高粒子的有效性，在重采样过程中，根据粒子的误差协方差矩阵建立不同置信度的误差椭圆。根据几何位置将粒子划分为不同级别，然后对粒子进行筛选和优化。通过与后验克拉梅拉奥下界（PCRLB）的性能进行比较，验证了所提出方法在累积误差优化中的有效性。实验结果表明，该算法能够有效解决样本退化和贫化问题，具有更高的定位精度。