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A Method for Predicting Inertial Navigation System Positioning Errors Using a Back Propagation Neural Network Based

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Summary
This summary is machine-generated.

This study introduces a Particle Swarm Optimization-Back Propagation Neural Network (PSO-BPNN) to mitigate Global Positioning System/Strapdown Inertial Navigation System (GPS/SINS) position errors during GPS outages. The PSO-BPNN effectively reduces navigation inaccuracies when GPS signals are unavailable.

Keywords:
GPS denialGPS/SINS integrated navigation systembackpropagation neural networkparticle swarm optimization

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Area of Science:

  • Navigation Systems Engineering
  • Artificial Intelligence in Navigation
  • Geomatics Engineering

Background:

  • Global Positioning System/Strapdown Inertial Navigation System (GPS/SINS) integrated navigation systems are crucial for accurate positioning.
  • GPS signal denial presents a significant challenge, leading to increased position errors in integrated systems.
  • Existing methods for mitigating GPS denial often have limitations in accuracy and adaptability.

Purpose of the Study:

  • To develop and evaluate a novel method for reducing position errors in GPS/SINS during GPS denial.
  • To propose a Particle Swarm Optimization-Back Propagation Neural Network (PSO-BPNN) model as a GPS replacement for positioning.
  • To assess the effectiveness of the PSO-BPNN model compared to a standard Back Propagation Neural Network (BPNN).

Main Methods:

  • A PSO-BPNN model was developed to predict and compensate for position errors.
  • The model utilizes Strapdown Inertial Navigation System (SINS) data (position, velocity, attitude) and navigation time as inputs.
  • The performance was validated through an actual ship experiment, comparing PSO-BPNN against BPNN.

Main Results:

  • The PSO-BPNN model demonstrated a significant reduction in position errors compared to the standard BPNN.
  • The proposed method effectively compensated for navigation inaccuracies during simulated GPS signal denial.
  • Experimental results confirmed the superior performance of the PSO-BPNN in GPS-denied environments.

Conclusions:

  • The PSO-BPNN is a viable and effective method for mitigating position errors in GPS/SINS during GPS denial.
  • This approach offers improved navigation accuracy and reliability in challenging signal environments.
  • The findings have implications for enhancing the robustness of autonomous navigation systems.