An Electro-Magnetic Log (EML) Integrated Navigation Algorithm Based on Hidden Markov Model (HMM) and Cross-Noise Linear Kalman Filter
In this paper, an EML (electro-magnetic log) integrated navigation algorithm based on the HMM (hidden Markov model) and CNLKF (cross-noise linear Kalman filter) is proposed, which is suitable for SINS (strapdown inertial navigation system)/EML/GNSS (global navigation satellite system) integrated nav...
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2025-02-01
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| author | Haosu Zhang Liang Yang Lei Zhang Yong Du Chaoqi Chen Wei Mu Lingji Xu |
| author_facet | Haosu Zhang Liang Yang Lei Zhang Yong Du Chaoqi Chen Wei Mu Lingji Xu |
| author_sort | Haosu Zhang |
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| description | In this paper, an EML (electro-magnetic log) integrated navigation algorithm based on the HMM (hidden Markov model) and CNLKF (cross-noise linear Kalman filter) is proposed, which is suitable for SINS (strapdown inertial navigation system)/EML/GNSS (global navigation satellite system) integrated navigation systems for small or medium-sized AUV (autonomous underwater vehicle). The algorithm employs the following five techniques: ① the HMM-based pre-processing algorithm of EML data; ② the CNLKF-based fusion algorithm of SINS/EML information; ③ the MALKF (modified adaptive linear Kalman filter)-based algorithm of GNSS-based calibration; ④ the estimation algorithm of the current speed based on output from MALKF and GNSS; ⑤ the feedback correction of LKF (linear Kalman filter). The principle analysis of the algorithm, the modeling process, and the flow chart of the algorithm are given in this paper. The sea trial of a small-sized AUV shows that the endpoint positioning error of the proposed/traditional algorithm by this paper is 20.5 m/712.1 m. The speed of the water current could be relatively accurately estimated by the proposed algorithm. Therefore, the algorithm has the advantages of high accuracy, strong anti-interference ability (it can effectively shield the outliers of EML and GNSS), strong adaptability to complex environments, and high engineering practicality. In addition, compared with the traditional DVL (Doppler velocity log), EML has the advantages of great concealment, low cost, light weight, small size, and low power consumption. |
| format | Article |
| id | doaj-art-783f6e00f1ac40beae2d7f7d16126b1c |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-783f6e00f1ac40beae2d7f7d16126b1c2025-08-20T03:11:22ZengMDPI AGSensors1424-82202025-02-01254101510.3390/s25041015An Electro-Magnetic Log (EML) Integrated Navigation Algorithm Based on Hidden Markov Model (HMM) and Cross-Noise Linear Kalman FilterHaosu Zhang0Liang Yang1Lei Zhang2Yong Du3Chaoqi Chen4Wei Mu5Lingji Xu6Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, ChinaSouthern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, ChinaState Power Investment Group (Zhuhai Hengqin) Thermoelectric Co., Ltd., Zhuhai 519000, ChinaWuhan National Laboratory for Optoelectronics, Huazhong Institute of Electro-Optics, Wuhan 430223, ChinaSouthern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, ChinaSouthern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, ChinaSouthern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, ChinaIn this paper, an EML (electro-magnetic log) integrated navigation algorithm based on the HMM (hidden Markov model) and CNLKF (cross-noise linear Kalman filter) is proposed, which is suitable for SINS (strapdown inertial navigation system)/EML/GNSS (global navigation satellite system) integrated navigation systems for small or medium-sized AUV (autonomous underwater vehicle). The algorithm employs the following five techniques: ① the HMM-based pre-processing algorithm of EML data; ② the CNLKF-based fusion algorithm of SINS/EML information; ③ the MALKF (modified adaptive linear Kalman filter)-based algorithm of GNSS-based calibration; ④ the estimation algorithm of the current speed based on output from MALKF and GNSS; ⑤ the feedback correction of LKF (linear Kalman filter). The principle analysis of the algorithm, the modeling process, and the flow chart of the algorithm are given in this paper. The sea trial of a small-sized AUV shows that the endpoint positioning error of the proposed/traditional algorithm by this paper is 20.5 m/712.1 m. The speed of the water current could be relatively accurately estimated by the proposed algorithm. Therefore, the algorithm has the advantages of high accuracy, strong anti-interference ability (it can effectively shield the outliers of EML and GNSS), strong adaptability to complex environments, and high engineering practicality. In addition, compared with the traditional DVL (Doppler velocity log), EML has the advantages of great concealment, low cost, light weight, small size, and low power consumption.https://www.mdpi.com/1424-8220/25/4/1015inertial navigation systemKalman filter (KF)-integrated navigationAUVocean current estimation |
| spellingShingle | Haosu Zhang Liang Yang Lei Zhang Yong Du Chaoqi Chen Wei Mu Lingji Xu An Electro-Magnetic Log (EML) Integrated Navigation Algorithm Based on Hidden Markov Model (HMM) and Cross-Noise Linear Kalman Filter Sensors inertial navigation system Kalman filter (KF)-integrated navigation AUV ocean current estimation |
| title | An Electro-Magnetic Log (EML) Integrated Navigation Algorithm Based on Hidden Markov Model (HMM) and Cross-Noise Linear Kalman Filter |
| title_full | An Electro-Magnetic Log (EML) Integrated Navigation Algorithm Based on Hidden Markov Model (HMM) and Cross-Noise Linear Kalman Filter |
| title_fullStr | An Electro-Magnetic Log (EML) Integrated Navigation Algorithm Based on Hidden Markov Model (HMM) and Cross-Noise Linear Kalman Filter |
| title_full_unstemmed | An Electro-Magnetic Log (EML) Integrated Navigation Algorithm Based on Hidden Markov Model (HMM) and Cross-Noise Linear Kalman Filter |
| title_short | An Electro-Magnetic Log (EML) Integrated Navigation Algorithm Based on Hidden Markov Model (HMM) and Cross-Noise Linear Kalman Filter |
| title_sort | electro magnetic log eml integrated navigation algorithm based on hidden markov model hmm and cross noise linear kalman filter |
| topic | inertial navigation system Kalman filter (KF)-integrated navigation AUV ocean current estimation |
| url | https://www.mdpi.com/1424-8220/25/4/1015 |
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