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An Adaptive Multi-Mode Navigation Method with Intelligent Virtual Sensor Based on Long Short-Term Memory in GNSS

Rong Wang1, Yu Rui1, Jingxin Zhao1

  • 1Navigation Research Center, College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.

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Summary

This study introduces an intelligent virtual sensor using long short-term memory (LSTM) networks to improve inertial navigation systems (INS) in GPS-denied environments. The method enhances navigation accuracy and efficiency, especially with limited data.

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GNSS restricted environmentINS/GNSS integrated navigation systemback propagation neural networksfireworks optimization algorithmlong short-term memory

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Area of Science:

  • Navigation Systems
  • Artificial Intelligence
  • Signal Processing

Background:

  • Pure inertial navigation systems (INS) suffer from rapid divergence without external corrections, particularly in Global Navigation Satellite System (GNSS) restricted environments.
  • Accurate and reliable navigation is crucial in scenarios where GNSS availability is compromised.

Purpose of the Study:

  • To propose a multi-mode navigation method using an intelligent virtual sensor based on Long Short-Term Memory (LSTM) networks to address INS divergence in GNSS-restricted environments.
  • To enhance the adaptability and efficiency of the navigation system through intelligent sensor mode switching and hyperparameter optimization.

Main Methods:

  • Designed a multi-mode intelligent virtual sensor with training, predicting, and validation modes, flexibly switching based on GNSS availability and LSTM network status.
  • Employed the fireworks algorithm to optimize LSTM hyperparameters (learning rate, hidden layers) for improved estimation performance.
  • Integrated the intelligent virtual sensor to correct the INS and maintain LSTM network availability.

Main Results:

  • The proposed method maintains online prediction accuracy of the intelligent virtual sensor and adaptively shortens training time based on performance requirements.
  • Demonstrated significant improvements in training efficiency and availability ratio under small sample conditions compared to conventional Backpropagation (BP) neural networks and standard LSTM networks.
  • Effectively and efficiently improved navigation performance in GNSS-restricted environments.

Conclusions:

  • The intelligent virtual sensor approach offers a robust solution for enhancing INS performance in challenging GNSS environments.
  • The adaptive multi-mode strategy and optimized LSTM hyperparameters lead to superior navigation accuracy and efficiency, particularly in data-scarce scenarios.
  • This method provides a significant advancement for reliable navigation when GNSS signals are unavailable or unreliable.