A New Spatial Registration Algorithm of Aerial Moving Platform to Sea Target Tracking

Author:

Dai Qiuyang1,Lu Faxing1

Affiliation:

1. College of Weaponry Engineering, Naval University of Engineering, Wuhan 430033, China

Abstract

Spatial registration is the primary challenge affecting target tracking accuracy, especially for the aerial moving platform and sea target tracking. In this environment, it is important to account for both the errors in sensor observations and the variations in platform attitude. In order to solve the problem of complex types of errors in the tracking of sea targets by aerial moving platforms, a new spatial registration algorithm is proposed. Through separating and analyzing observation data, the influence of sensor observation error and attitude error on observation data is obtained, and a systematic error consistency matrix is established. Based on observation information from multiple platforms, accurate tracking of sea targets can be accomplished without estimating systematic error. In order to verify the effectiveness of the algorithm, we carried out simulation experiments and practical experiments on the lake, which showed that the new algorithm was more efficient than traditional algorithms.

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

Reference31 articles.

1. A Survey of Enabling Technologies for Network Localization, Tracking, and Navigation;Laoudias;IEEE Commun. Surv. Tutor.,2018

2. Simulation of Seepage Flow through Embankment Dam by using a novel Extended Kalman Filter based neural network Paradigm: Case Study of Fontaine Gazelles Dam, Algeria;Rehamnia;Measurement,2021

3. Julier, S.J., Uhlmann, J.K., and Durrant-Whyte, H.F. (1995, January 21–23). A New Approach for Filtering Nonlinear Systems. Proceedings of the 1995 American Control Conference—ACC’95, Seattle, WA, USA.

4. Cubature Kalman Filters;Arasaratnam;IEEE Trans. Autom. Control,2009

5. Neural network-aided variational Bayesian adaptive cubature Kalman filtering for nonlinear state estimation;Miao;Meas. Sci. Technol.,2017

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