Next Article in Journal
Alternative Path Communication in Wide-Scale Cluster-Tree Wireless Sensor Networks Using Inactive Periods
Next Article in Special Issue
Development of Data Registration and Fusion Methods for Measurement of Ultra-Precision Freeform Surfaces
Previous Article in Journal
Deformation Monitoring of Waste-Rock-Backfilled Mining Gob for Ground Control
Previous Article in Special Issue
Auxiliary Truncated Unscented Kalman Filtering for Bearings-Only Maneuvering Target Tracking
Article Menu
Issue 5 (May) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(5), 1045; doi:10.3390/s17051045

Flexible Fusion Structure-Based Performance Optimization Learning for Multisensor Target Tracking

1
School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
2
State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
3
School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan 232001, China
4
Science and Technology on Electro-Optic Control Laboratory, Luoyang Institute of Electro-Optical Equipment of Avic, Luoyang 471000, China
5
Shanghai Key Lab for Trustworthy Computing, East China Normal University, Shanghai 200062, China
*
Author to whom correspondence should be addressed.
Academic Editors: Xue-Bo Jin, Shuli Sun, Hong Wei and Feng-Bao Yang
Received: 27 January 2017 / Revised: 19 April 2017 / Accepted: 22 April 2017 / Published: 6 May 2017
View Full-Text   |   Download PDF [366 KB, uploaded 6 May 2017]   |  

Abstract

Compared with the fixed fusion structure, the flexible fusion structure with mixed fusion methods has better adjustment performance for the complex air task network systems, and it can effectively help the system to achieve the goal under the given constraints. Because of the time-varying situation of the task network system induced by moving nodes and non-cooperative target, and limitations such as communication bandwidth and measurement distance, it is necessary to dynamically adjust the system fusion structure including sensors and fusion methods in a given adjustment period. Aiming at this, this paper studies the design of a flexible fusion algorithm by using an optimization learning technology. The purpose is to dynamically determine the sensors’ numbers and the associated sensors to take part in the centralized and distributed fusion processes, respectively, herein termed sensor subsets selection. Firstly, two system performance indexes are introduced. Especially, the survivability index is presented and defined. Secondly, based on the two indexes and considering other conditions such as communication bandwidth and measurement distance, optimization models for both single target tracking and multi-target tracking are established. Correspondingly, solution steps are given for the two optimization models in detail. Simulation examples are demonstrated to validate the proposed algorithms. View Full-Text
Keywords: flexible fusion structure; mixed fusion method; combinatorial optimization; sensor subsets selection; tracking accuracy; system survivability flexible fusion structure; mixed fusion method; combinatorial optimization; sensor subsets selection; tracking accuracy; system survivability
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

Ge, Q.; Wei, Z.; Cheng, T.; Chen, S.; Wang, X. Flexible Fusion Structure-Based Performance Optimization Learning for Multisensor Target Tracking. Sensors 2017, 17, 1045.

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