Design of GNSS/INS Coupled Navigation Algorithm Using Adaptive Neuro-Fuzzy Inference Systems
DOI:
https://doi.org/10.13052/2024.ACES.J.400808Keywords:
Adaptive neuro-fuzzy inference system, data fusion, GNSS/INS coupled navigation, signal loss, system robustnessAbstract
Traditional GNSS/INS (Global Navigation Satellite Systems/Inertial Navigation Systems) coupled navigation algorithms often struggle with accuracy in GNSS-denied or challenging environments. This paper introduces a novel adaptive fusion algorithm leveraging an Adaptive Neuro-Fuzzy Inference System (ANFIS) that dynamically adjusts sensor weightings based on real-time signal quality and system performance. The core innovation lies in the real-time integration of fuzzy logic and neural network learning, enabling the system to continuously adapt and optimize its decision-making rules for navigation accuracy. A comprehensive, dynamic error source model is constructed incorporating GNSS atmospheric delays, orbit errors, and INS drift to enhance the learning-driven weight adjustment mechanism. The resulting ANFIS-based fusion strategy shows significant superiority over traditional Kalman-based methods, achieving over 90% robustness across harsh scenarios with an average execution time of 0.69 seconds, demonstrating improved adaptability, learning capability, and fault resilience in dynamic environments.
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