Dual-Model Reverse CKF Algorithm in Cooperative Navigation for USV

As one of the most promising research directions, cooperative location with high precision and low-cost IMU is becoming an emerging research topic in many positioning fields. Low-cost MEMS/DVL is a preferred solution for dead-reckoning in multi-USV cooperative network. However, large misalignment an...

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Main Authors: Bo Xu, Yong ping Xiao, Wei Gao, Yong gang Zhang, Ya Long Liu, Yang Liu
Format: Article
Language:English
Published: Hindawi Limited 2014-01-01
Series:Mathematical Problems in Engineering
Online Access:http://dx.doi.org/10.1155/2014/186785
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spelling doaj-097c96da8c7e456cb0c8b373399a60172020-11-24T22:43:46ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472014-01-01201410.1155/2014/186785186785Dual-Model Reverse CKF Algorithm in Cooperative Navigation for USVBo Xu0Yong ping Xiao1Wei Gao2Yong gang Zhang3Ya Long Liu4Yang Liu5Harbin Engineering University, 145 Nantong Road, Harbin 150001, ChinaHarbin Engineering University, 145 Nantong Road, Harbin 150001, ChinaHarbin Engineering University, 145 Nantong Road, Harbin 150001, ChinaHarbin Engineering University, 145 Nantong Road, Harbin 150001, ChinaHarbin Engineering University, 145 Nantong Road, Harbin 150001, ChinaHarbin Engineering University, 145 Nantong Road, Harbin 150001, ChinaAs one of the most promising research directions, cooperative location with high precision and low-cost IMU is becoming an emerging research topic in many positioning fields. Low-cost MEMS/DVL is a preferred solution for dead-reckoning in multi-USV cooperative network. However, large misalignment angles and large gyro drift coexist in low-cost MEMS that leads to the poor observability. Based on cubature Kalman filter (CKF) algorithm that has access to high accuracy and relative small computation, dual-model filtering scheme is proposed. It divides the whole process into two subsections that cut off the coupling relations and improve the observability of MEMS errors: it first estimates large misalignment angle and then estimates the gyro drift. Furthermore, to improve the convergence speed of large misalignment angle estimated in the first subsection, “time reversion” concept is introduced. It uses a short period time to forward and backward several times to improve convergence speed effectively. Finally, simulation analysis and experimental verification is conducted. Simulation and experimental results show that the algorithm can effectively improve the cooperative navigation performance.http://dx.doi.org/10.1155/2014/186785
collection DOAJ
language English
format Article
sources DOAJ
author Bo Xu
Yong ping Xiao
Wei Gao
Yong gang Zhang
Ya Long Liu
Yang Liu
spellingShingle Bo Xu
Yong ping Xiao
Wei Gao
Yong gang Zhang
Ya Long Liu
Yang Liu
Dual-Model Reverse CKF Algorithm in Cooperative Navigation for USV
Mathematical Problems in Engineering
author_facet Bo Xu
Yong ping Xiao
Wei Gao
Yong gang Zhang
Ya Long Liu
Yang Liu
author_sort Bo Xu
title Dual-Model Reverse CKF Algorithm in Cooperative Navigation for USV
title_short Dual-Model Reverse CKF Algorithm in Cooperative Navigation for USV
title_full Dual-Model Reverse CKF Algorithm in Cooperative Navigation for USV
title_fullStr Dual-Model Reverse CKF Algorithm in Cooperative Navigation for USV
title_full_unstemmed Dual-Model Reverse CKF Algorithm in Cooperative Navigation for USV
title_sort dual-model reverse ckf algorithm in cooperative navigation for usv
publisher Hindawi Limited
series Mathematical Problems in Engineering
issn 1024-123X
1563-5147
publishDate 2014-01-01
description As one of the most promising research directions, cooperative location with high precision and low-cost IMU is becoming an emerging research topic in many positioning fields. Low-cost MEMS/DVL is a preferred solution for dead-reckoning in multi-USV cooperative network. However, large misalignment angles and large gyro drift coexist in low-cost MEMS that leads to the poor observability. Based on cubature Kalman filter (CKF) algorithm that has access to high accuracy and relative small computation, dual-model filtering scheme is proposed. It divides the whole process into two subsections that cut off the coupling relations and improve the observability of MEMS errors: it first estimates large misalignment angle and then estimates the gyro drift. Furthermore, to improve the convergence speed of large misalignment angle estimated in the first subsection, “time reversion” concept is introduced. It uses a short period time to forward and backward several times to improve convergence speed effectively. Finally, simulation analysis and experimental verification is conducted. Simulation and experimental results show that the algorithm can effectively improve the cooperative navigation performance.
url http://dx.doi.org/10.1155/2014/186785
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