Extended Kalman filter synthesis for integrated global positioning/inertial navigation systems
Applied Mathematics and Computation
Engineering Applications of Artificial Intelligence
Global Positioning Systems, Inertial Navigation, and Integration
Global Positioning Systems, Inertial Navigation, and Integration
Approximate nonlinear filtering and its application in navigation
Automatica (Journal of IFAC)
De-noising by soft-thresholding
IEEE Transactions on Information Theory
Sensor Integration for Satellite-Based Vehicular Navigation Using Neural Networks
IEEE Transactions on Neural Networks
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This paper presents a new algorithm for de-noising global positioning system (GPS) and inertial navigation system (INS) data and estimates the INS error using wavelet multi-resolution analysis algorithm (WMRA)-based genetic algorithm (GA) with a well-designed structure appropriate for practical and real time implementations because of its very short training time and elevated accuracy. Different techniques have been implemented to de-noise and estimate the INS and GPS errors. Wavelet de-noising is one of the most exploited techniques that have been recently used to increase the precision and reliability of the integrated GPS/INS navigation system. To ameliorate the WMRA algorithm, GA was exploited to optimize the wavelet parameters so as to determine the best wavelet filter, thresholding selection rule (TSR), and the optimum level of decomposition (LOD). This results in increasing the robustness of the WMRA algorithm to estimate the INS error. The proposed intelligent technique has overcome the drawbacks of the tedious selection for WMRA algorithm parameters. Finally, the proposed method improved the stability and reliability of the estimated INS error using real field test data.