Next Article in Journal
Information and Meaning
Previous Article in Journal
A Novel Global Path Planning Method for Mobile Robots Based on Teaching-Learning-Based Optimization
Article Menu

Export Article

Open AccessArticle
Information 2016, 7(3), 40; doi:10.3390/info7030040

Lateral Cross Localization Algorithm Using Orientation Angle for Improved Target Estimation in Near-Field Environments

Department of Weaponry Engineering, Naval University of Engineering, Wuhan 430033, China
*
Author to whom correspondence should be addressed.
Academic Editor: Willy Susilo
Received: 22 April 2016 / Revised: 29 June 2016 / Accepted: 1 July 2016 / Published: 7 July 2016
View Full-Text   |   Download PDF [2445 KB, uploaded 9 July 2016]   |  

Abstract

Passive positioning systems with a small aperture array exhibit poor accuracy of target estimation under strong interference in near-field environments. To improve this accuracy, we propose a novel cross localization algorithm for direction-finding using the orientation angle. Improved geometric and numerical target-positioning models are constructed after analyzing the mechanism of the conventional positioning algorithm. The target prediction equation is then derived using the constructed models, and the equation for nonlinear estimation is linearized using the Taylor series. An unbiased estimation of the target is obtained by optimizing the control of the iteration process, thus achieving an accurate positioning of the target. The performance of the proposed algorithm was evaluated in terms of its effectiveness and positioning accuracy under varying signal-to-noise conditions and orientation angle-measurement errors. Simulation results show that the proposed algorithm is capable of positioning the target effectively, and offers better positioning accuracy than traditional algorithms under the conditions of large orientation angle measurement errors or high-level background noise. View Full-Text
Keywords: lateral cross localization; orientation angle; Gauss-Newton iteration; error characteristics lateral cross localization; orientation angle; Gauss-Newton iteration; error characteristics
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

Xu, P.; Yan, B. Lateral Cross Localization Algorithm Using Orientation Angle for Improved Target Estimation in Near-Field Environments. Information 2016, 7, 40.

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]
Information EISSN 2078-2489 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top