An algorithm for object tracking based on adaptive triangle shape mesh estimation method

Unscented particle filter (UPF) has high accuracy of state estimation for nonlinear system with non-Gaussian noise. While the computation of traditional unscented particle filter is huge and this depends on the particle number. In this paper we propose a new adaptive unscented particle filter algorithm AUPF through improved relative entropy which can adaptively adjust the particle number during filtering. Firstly the relative entropy is used to measure the distance between the posterior probability density and the importance proposal and the least number of particles for the next time step is decided according to the relative entropy. Then the least number is adjusted to offset the difference between the importance proposal and the true distribution. This algorithm can effectively reduce unnecessary particles meanwhile reduce the computation. The simulation results show the effectiveness of AUPF.

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“…The distance can be calculated by the relative entropy. The relative entropy between 1 p and 2 p is defined in Eq. 1: is given by Eq.…”

Section: Adaptive Unscented Particle Filter Algorithm Through Improve...mentioning

confidence: 99%

“…It's aim is to obtain the estimation of the real state from a sequence of observations. The application of it includes signal processing, navigation, object tracking, economics, etc [1].…”

Section: Introductionmentioning

confidence: 99%

A New Adaptive UPF Algorithm through Improved Relative Entropy

Peng

Zhang

2013

AMR

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“…It can improve the performance of the robot in such tasks as person following, obstacle avoidance, map building and robot localization [2][3][4][5]. Object tracking is also a challenging task due to the presence of noise, occlusion, clutter, dynamic background elements and so on [6,7]. The statistical method can represent ambiguity and noise in a mathematical way, and it can also give a successive object tracking result which is important for mobile robot motion control [8].…”

Section: Introductionmentioning

confidence: 99%

Confidence-Level-Based New Adaptive Particle Filter for Nonlinear Object Tracking

Zhang

Peng

et al. 2012

International Journal of Advanced Robotic Systems

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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

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