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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Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2014/186785 |
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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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