An Electro-Magnetic Log (EML) Integrated Navigation Algorithm Based on Hidden Markov Model (HMM) and Cross-Noise Linear Kalman Filter
Haosu Zhang, Liang Yang, Lei Zhang, Yong Du, Chaoqi Chen, Wei Mu, Lingji Xu
TL;DR
This paper introduces a new navigation algorithm for underwater vehicles that combines electro-magnetic log data with advanced filtering techniques to improve accuracy and reliability.
Contribution
The novel algorithm integrates HMM and CNLKF for EML-based navigation, offering high accuracy and robustness in complex underwater environments.
Findings
The proposed algorithm achieved an endpoint positioning error of 20.5 m compared to 712.1 m with traditional methods.
The algorithm effectively estimates water current speed and filters out EML and GNSS outliers.
EML is shown to be more concealable, cost-effective, and energy-efficient than traditional DVL.
Abstract
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…
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Taxonomy
TopicsUnderwater Vehicles and Communication Systems · Target Tracking and Data Fusion in Sensor Networks · Maritime Navigation and Safety