Next Article in Journal
Two New Philosophical Problems for Robo-Ethics
Previous Article in Journal
Improvement of Fast Simplified Successive-Cancellation Decoder for Polar Codes
Article Menu

Export Article

Open AccessArticle

Sensor Alignment for Ballistic Trajectory Estimation via Sparse Regularization

Unit 94, PLA 91550, Dalian 116023, China
*
Author to whom correspondence should be addressed.
Information 2018, 9(10), 255; https://doi.org/10.3390/info9100255
Received: 7 September 2018 / Revised: 11 October 2018 / Accepted: 16 October 2018 / Published: 18 October 2018
(This article belongs to the Section Information Processes)
  |  
PDF [4161 KB, uploaded 18 October 2018]
  |  

Abstract

Sensor alignment plays a key role in the accurate estimation of the ballistic trajectory. A sparse regularization-based sensor alignment method coupled with the selection of a regularization parameter is proposed in this paper. The sparse regularization model is established by combining the traditional model of trajectory estimation with the sparse constraint of systematic errors. The trajectory and the systematic errors are estimated simultaneously by using the Newton algorithm. The regularization parameter in the model is crucial to the accuracy of trajectory estimation. Stein’s unbiased risk estimator (SURE) and general cross-validation (GCV) under the nonlinear measurement model are constructed for determining the suitable regularization parameter. The computation methods of SURE and GCV are also investigated. Simulation results show that both SURE and GCV can provide regularization parameter choices of high quality for minimizing the errors of trajectory estimation, and that our method can improve the accuracy of trajectory estimation over the traditional non-regularization method. The estimates of systematic errors are close to the true value. View Full-Text
Keywords: sensor alignment; ballistic trajectory estimation; sparse regularization; systematic errors; SURE; GCV sensor alignment; ballistic trajectory estimation; sparse regularization; systematic errors; SURE; GCV
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

Share & Cite This Article

MDPI and ACS Style

Li, D.; Gong, L. Sensor Alignment for Ballistic Trajectory Estimation via Sparse Regularization. Information 2018, 9, 255.

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