# An Electro-Magnetic Log (EML) Integrated Navigation Algorithm Based on Hidden Markov Model (HMM) and Cross-Noise Linear Kalman Filter

## 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.

## Key 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 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.