A Study on Redundant High-Precision Accelerometer Navigation System Based on Convolutional Neural Networks
The Inertial Navigation System(INS)is a pivotal technology for autonomous navigation in GPS-denied environ-ments.However,existing INS systems are confronted with challenges of error accumulation,which affects long-term navigational accuracy.To enhance the accuracy of INS,by introducing high-precision accelerometers and using Convolutional Neural Network(CNN),this paper proposes a new type of CNN-based redundant high-precision accelerometer navigation system,which optimizes the error prediction and inversion correction capabilities,and significantly improves the navigation accuracy.Experimental results prove that this system is superior to the traditional INS methods in reducing error accumulation and improving the long-term navi-gation stability.
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