Next Article in Journal
DOA Estimation under Unknown Mutual Coupling and Multipath with Improved Effective Array Aperture
Next Article in Special Issue
EMD-Based Symbolic Dynamic Analysis for the Recognition of Human and Nonhuman Pyroelectric Infrared Signals
Previous Article in Journal
Maximum-Likelihood Estimator of Clock Offset between Nanomachines in Bionanosensor Networks
Previous Article in Special Issue
Sea-Based Infrared Scene Interpretation by Background Type Classification and Coastal Region Detection for Small Target Detection
Article Menu

Export Article

Open AccessArticle
Sensors 2015, 15(12), 30839-30855; doi:10.3390/s151229829

Labeled RFS-Based Track-Before-Detect for Multiple Maneuvering Targets in the Infrared Focal Plane Array

College of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, Hunan, P.R. China
*
Author to whom correspondence should be addressed.
Academic Editors: Vincenzo Spagnolo and Dragan Indjin
Received: 20 October 2015 / Revised: 26 November 2015 / Accepted: 1 December 2015 / Published: 8 December 2015
(This article belongs to the Special Issue Infrared and THz Sensing and Imaging)
View Full-Text   |   Download PDF [2368 KB, uploaded 8 December 2015]   |  

Abstract

The problem of jointly detecting and tracking multiple targets from the raw observations of an infrared focal plane array is a challenging task, especially for the case with uncertain target dynamics. In this paper a multi-model labeled multi-Bernoulli (MM-LMB) track-before-detect method is proposed within the labeled random finite sets (RFS) framework. The proposed track-before-detect method consists of two parts—MM-LMB filter and MM-LMB smoother. For the MM-LMB filter, original LMB filter is applied to track-before-detect based on target and measurement models, and is integrated with the interacting multiple models (IMM) approach to accommodate the uncertainty of target dynamics. For the MM-LMB smoother, taking advantage of the track labels and posterior model transition probability, the single-model single-target smoother is extended to a multi-model multi-target smoother. A Sequential Monte Carlo approach is also presented to implement the proposed method. Simulation results show the proposed method can effectively achieve tracking continuity for multiple maneuvering targets. In addition, compared with the forward filtering alone, our method is more robust due to its combination of forward filtering and backward smoothing. View Full-Text
Keywords: labeled random finite sets; labeled multi-Bernoulli; track-before-detect; maneuvering target; Sequential Monte Carlo labeled random finite sets; labeled multi-Bernoulli; track-before-detect; maneuvering target; Sequential Monte Carlo
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, M.; Li, J.; Zhou, Y. Labeled RFS-Based Track-Before-Detect for Multiple Maneuvering Targets in the Infrared Focal Plane Array. Sensors 2015, 15, 30839-30855.

Show more citation formats Show less citations formats

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