Confidence-Level-Based New Adaptive Particle Filter for Nonlinear Object Tracking
Confidence-Level-Based New Adaptive Particle Filter for Nonlinear Object Tracking
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DOI：
10.5772/54047
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发表时间：
2012-11
影响因子：
2.3
通讯作者：
Xiaoyong Zhang;Jun Peng;Wentao Yu;Kuo-Chi Lin
中科院分区：
文献类型：
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作者：
Xiaoyong Zhang;Jun Peng;Wentao Yu;Kuo-Chi Lin
噪声背景下的非线性目标跟踪是移动的机器人的一项基本技能，也是一项具有挑战性的任务，尤其是在动态环境下。粒子滤波是一种有效的非线性目标跟踪工具。非线性目标跟踪需要粒子滤波的实时处理能力。虽然传统的粒子滤波器中的数量是固定的，但这会导致大量不必要的计算。针对这一问题，提出了一种基于置信度的自适应粒子滤波算法。该算法利用了置信区间的思想。根据估计状态的置信度和方差估计下一时刻的最少粒子数。相应地，一种改进的系统重采样算法被用于新的改进的粒子滤波器。NAPF在保证非线性目标跟踪精度的同时，有效地减少了计算量。仿真结果和机器人的球跟踪结果验证了算法的有效性。
Nonlinear object tracking from noisy measurements is a basic skill and a challenging task of mobile robotics, especially under dynamic environments. The particle filter is a useful tool for nonlinear object tracking with non-Gaussian noise. Nonlinear object tracking needs the real-time processing capability of the particle filter. While the number in a traditional particle filter is fixed, that can lead to a lot of unnecessary computation. To address this issue, a confidence-level-based new adaptive particle filter (NAPF) algorithm is proposed in this paper. In this algorithm the idea of confidence interval is utilized. The least number of particles for the next time instant is estimated according to the confidence level and the variance of the estimated state. Accordingly, an improved systematic re-sampling algorithm is utilized for the new improved particle filter. NAPF can effectively reduce the computation while ensuring the accuracy of nonlinear object tracking. The simulation results and the ball tracking results of the robot verify the effectiveness of the algorithm.