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A Bionic Data-driven Approach for Long-distance Underwater Navigation with Anomaly Resistance

Songnan Yang, Xiaohui Zhang, Shiliang Zhang, Xuehui Ma, Wenqi Bai, Yushuai Li, Tingwen Huang

Abstract

Various animals exhibit accurate navigation using environment cues. The Earth's magnetic field has been proved a reliable information source in long-distance fauna migration. Inspired by animal navigation, this work proposes a bionic and data-driven approach for long-distance underwater navigation. The proposed approach uses measured geomagnetic data for the navigation, and requires no GPS systems or geographical maps. Particularly, we construct and train a Temporal Attention-based Long Short-Term Memory (TA-LSTM) network to predict the heading angle during the navigation. To mitigate the impact of geomagnetic anomalies, we develop the mechanism to detect and quantify the anomalies based on Maximum Likelihood Estimation. We integrate the developed mechanism with the TA-LSTM, and calibrate the predicted heading angles to gain resistance against geomagnetic anomalies. Using the retrieved data from the WMM model, we conduct numerical simulations with diversified navigation conditions to test our approach. The simulation results demonstrate a resilience navigation against geomagnetic anomalies by our approach, along with precision and stability of the underwater navigation in single and multiple destination missions.

A Bionic Data-driven Approach for Long-distance Underwater Navigation with Anomaly Resistance

Abstract

Various animals exhibit accurate navigation using environment cues. The Earth's magnetic field has been proved a reliable information source in long-distance fauna migration. Inspired by animal navigation, this work proposes a bionic and data-driven approach for long-distance underwater navigation. The proposed approach uses measured geomagnetic data for the navigation, and requires no GPS systems or geographical maps. Particularly, we construct and train a Temporal Attention-based Long Short-Term Memory (TA-LSTM) network to predict the heading angle during the navigation. To mitigate the impact of geomagnetic anomalies, we develop the mechanism to detect and quantify the anomalies based on Maximum Likelihood Estimation. We integrate the developed mechanism with the TA-LSTM, and calibrate the predicted heading angles to gain resistance against geomagnetic anomalies. Using the retrieved data from the WMM model, we conduct numerical simulations with diversified navigation conditions to test our approach. The simulation results demonstrate a resilience navigation against geomagnetic anomalies by our approach, along with precision and stability of the underwater navigation in single and multiple destination missions.
Paper Structure (13 sections, 41 equations, 12 figures, 2 tables, 1 algorithm)

This paper contains 13 sections, 41 equations, 12 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Fundamental
  3. Mathematical Description of the Geomagnetic Field
  4. Navigation and the Geomagnetic field
  5. The Proposed Underwater Navigation with Anomaly Resistance
  6. Description of Long Short-Term Memory (LSTM) Networks
  7. Encoding and Decoding of Geomagnetic Information with Temporal Attention in LSTM
  8. Geomagnetic Anomaly and Heading Angle Calibration
  9. Simulations and Results
  10. Underwater Navigation without Geomagnetic Anomaly
  11. Navigation with Geomagnetic Anomalies
  12. Navigation with Multiple Destination
  13. Conclusion

Figures (12)

  • Figure 1: Main elements in the geomagnetic field.
  • Figure 2: Schematic diagram of our geomagnetic navigation with temporal attention-based Long Short-Term Memory network and anomaly resistance.
  • Figure 3: The geomagnetic navigation process of the proposed approach.
  • Figure 4: The structure of encoder with local temporal attention.
  • Figure 5: The structure of decoder with global temporal attention.
  • ...and 7 more figures