Next Article in Journal
Vibro-Shock Dynamics Analysis of a Tandem Low Frequency Resonator—High Frequency Piezoelectric Energy Harvester
Next Article in Special Issue
Flexible Fusion Structure-Based Performance Optimization Learning for Multisensor Target Tracking
Previous Article in Journal
Distributed Data Service for Data Management in Internet of Things Middleware
Previous Article in Special Issue
State Estimation Using Dependent Evidence Fusion: Application to Acoustic Resonance-Based Liquid Level Measurement
Article Menu
Issue 5 (May) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(5), 972; doi:10.3390/s17050972

Auxiliary Truncated Unscented Kalman Filtering for Bearings-Only Maneuvering Target Tracking

Automatic Target Recognition Key Laboratory (ATR), Shenzhen University, Shenzhen 518060, China
*
Author to whom correspondence should be addressed.
Academic Editors: Shuli Sun, Hong Wei and Feng-Bao Yang
Received: 13 March 2017 / Revised: 24 April 2017 / Accepted: 25 April 2017 / Published: 27 April 2017
View Full-Text   |   Download PDF [1247 KB, uploaded 4 May 2017]   |  

Abstract

Novel auxiliary truncated unscented Kalman filtering (ATUKF) is proposed for bearings-only maneuvering target tracking in this paper. In the proposed algorithm, to deal with arbitrary changes in motion models, a modified prior probability density function (PDF) is derived based on some auxiliary target characteristics and current measurements. Then, the modified prior PDF is approximated as a Gaussian density by using the statistical linear regression (SLR) to estimate the mean and covariance. In order to track bearings-only maneuvering target, the posterior PDF is jointly estimated based on the prior probability density function and the modified prior probability density function, and a practical algorithm is developed. Finally, compared with other nonlinear filtering approaches, the experimental results of the proposed algorithm show a significant improvement for both the univariate nonstationary growth model (UNGM) case and bearings-only target tracking case. View Full-Text
Keywords: bearings-only target tracking; statistical linear regression; auxiliary truncated unscented Kalman filtering bearings-only target tracking; statistical linear regression; auxiliary truncated unscented Kalman filtering
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Li, L.-Q.; Wang, X.-L.; Liu, Z.-X.; Xie, W.-X. Auxiliary Truncated Unscented Kalman Filtering for Bearings-Only Maneuvering Target Tracking. Sensors 2017, 17, 972.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top