A low-cost integrated MEMS-based INS/GPS vehicle navigation system with challenging conditions based on an optimized IT2
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ORIGINAL ARTICLE
A low‑cost integrated MEMS‑based INS/GPS vehicle navigation system with challenging conditions based on an optimized IT2FNN in occluded environments Elahe Sadat Abdolkarimi1 · Mohammad‑Reza Mosavi1 Received: 19 April 2020 / Accepted: 6 August 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Integration of both global positioning system (GPS) and inertial navigation system (INS) assures a continuous and accurate navigation system. In low-cost low-precision micro-electromechanical system (MEMS)-based INS/GPS integration navigation systems, one of the major concerns is high-level stochastic noise and uncertainties existing in INS sensors and complex model of real noisy data. In such uncertainty-oriented environments, an intelligence structure with extra degrees of freedom which can handle and model a high-level of uncertainties in INS sensors, and an efficient denoising technique as a precursor to the intelligence structure can be efficient solutions. Our approach to these problems is taken in different steps. First, a denoising technique based on empirical mode decomposition (EMD) is used to provide more accurate INS sensor outputs and better generalization ability. Second, an optimized interval type-2 fuzzy neural network is used to model and handle a high-level of uncertainties efficiently and estimate the positioning error of INS sensors when GPS signals are blocked, and still meet both accuracy maximization and complexity minimization. Fast learning and convergence of the algorithm and less computational complexity can be achieved by using an extended Kalman filter in the learning of algorithm and an accurate and simple type-reduction, respectively, which can be utilized in real-time applications with significant performance. The results of EMD-based denoising technique, as a preprocessing phase, verify superior performance in comparison with the discrete wavelet transform denoising method in the signal-to-noise ratio improvement for raw and noisy signals of INS sensors. To verify the effectiveness of our proposed model, we applied challenging conditions consisting of low-cost lowprecision inertial sensors based on MEMS technology, long-term outages of GPS satellites, a high-speed experimental test vehicle and noisy real-world data in the real-time flight experiments. The achieved experimental accuracies are compared with the results that we have achieved in other methods, and our proposed method verifies significant improvements. Keywords EMD · High-speed · Low-cost low-precision MEMS-based INS/GPS integration · Navigation · Optimized IT2FNN
Introduction
This article is published as part of the special collection “Artificial Intelligence Applications in GNSS”. * Mohammad‑Reza Mosavi [email protected] Elahe Sadat Abdolkarimi [email protected] 1
Department of Electrical Engineering, Iran University of Science and Technology Narmak, Tehran 16846‑13114, Iran
Since the global positioning system (GPS) navigation is not a self-contained system and
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