Abstract
Given that the tracking accuracy and real-time performance of the particle filter (PF) target tracking algorithm are greatly affected by the number of sampled particles, a PF target tracking algorithm based on particle number optimization under the single-station environment was proposed in this study. First, a single-station target tracking model was established, and the corresponding PF algorithm was designed. Next, a tracking simulation experiment was carried out on the PF target tracking algorithm under different numbers of particles with the root mean square error (RMSE) and filtering time as the evaluation indexes. On this basis, the optimal number of particles, which could meet the accuracy and real-time performance requirements, was determined and taken as the number of particles of the proposed algorithm. The MATLAB simulation results revealed that compared with the unscented Kalman filter (UKF), the single-station PF target tracking algorithm based on particle number optimization not only was of high tracking accuracy but also could meet the real-time performance requirement.
Highlights
Target tracking refers to the process of state modeling, filtering estimation, and continuous tracking of a moving target by all kinds of observation means and computing methods
Filtering algorithms play an important role in dealing with target tracking [3,4,5], including some classical algorithms, such as Kalman filter (KF), extended Kalman filter (EKF), unscented Kalman filter (UKF), and particle filter (PF)
Jondhale and Deshpande [8] presented a modified KF based approach of real-time tracking of single target moving in 2D in wireless sensor network (WSN), which can deal with uncertainties in measurement noises and abrupt changes in target velocity
Summary
Target tracking refers to the process of state modeling, filtering estimation, and continuous tracking of a moving target by all kinds of observation means and computing methods. In order to improve the effective accuracy of PF algorithm in state estimation, Xu et al [14] established error ellipses with different confidence levels during the resampling process according to the error covariance matrix of particles and applied them to target positioning and tracking of wireless sensor network. E simulation experiment results showed that, compared with the UKF algorithm, the tracking trajectory of PF algorithm based on the particle number optimization was more approximate to the real trajectory, showing a smaller target tracking error. This algorithm could simultaneously meet the requirements for the target tracking accuracy and real-time performance
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