Online Tracking of Maneuvering Target Trajectory Based on Chaotic Time Series Prediction

Qian Wei, Peng Su, Lin Zhou, Wentao Shi

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Online prediction of maneuvering target trajectory is one of the most popular research directions at present. Specifically, the primary factors balancing, between prediction accuracy and response time, will give the research substance. This paper presents an online trajectory prediction algorithm based on small sample chaotic time series (OTP-SSCT). First, we optimize in terms of data breadth. The dynamic split window is built according to the motion characteristics of the maneuvering target, thus realizing trajectory segmentation and constructing a small sample chaotic time series prediction set. Second, since fully considering the motion patterns of maneuvering targets, we introduce the spatiotemporal features into the particle swarm optimization (PSO) model identification algorithm, which improves the identification sensitivity of key trajectory data points. Furthermore, we propose a feedback optimization strategy of residual compensation to correct the trajectory prediction values to improve the prediction accuracy. For the initial value sensitivity problem of the PSO model identification algorithm, we propose a new initial population strategy, which improves the effectiveness of initial parameters on model identification. Through simulation experiment analysis, it is verified that the proposed OTP-SSCT algorithm achieves better prediction accuracy and faster response time.

Original languageEnglish
Article number1668
JournalEntropy
Volume24
Issue number11
DOIs
StatePublished - Nov 2022

Keywords

  • feedback optimization
  • initial value sensitivity
  • online prediction
  • PSO model identification
  • trajectory segmentation

Fingerprint

Dive into the research topics of 'Online Tracking of Maneuvering Target Trajectory Based on Chaotic Time Series Prediction'. Together they form a unique fingerprint.

Cite this