Low-Cost GPS/INS Integrated Land-Vehicular Navigation System for Harsh Environments Using Hybrid Mamdani AFIS/KF Model

Navidi, Néda; Landry, René

doi:10.17265/2328-2142/2016.01.004

Cited by 1 publication

(1 citation statement)

References 25 publications

(22 reference statements)

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“…Tian et al [ 21 ] utilized improved RBFNN to predict INS errors during GPS outages. Navidi et al [ 22 ] employed adaptive fuzzy inference systems (AFIS) aided KF algorithm to fuse low-cost IMU and GPS robust measurements. Generally speaking, most studies are focused on filtering robustness and estimation correction during GNSS outages with artificial intelligence algorithms.…”

Section: Introductionmentioning

confidence: 99%

An Optimal Radial Basis Function Neural Network Enhanced Adaptive Robust Kalman Filter for GNSS/INS Integrated Systems in Complex Urban Areas

Ning

Wang

Han

et al. 2018

Sensors

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Inertial Navigation System (INS) is often combined with Global Navigation Satellite System (GNSS) to increase the positioning accuracy and continuity. In complex urban environments, GNSS/INS integrated systems suffer not only from dynamical model errors but also GNSS observation gross errors. However, it is hard to distinguish dynamical model errors from observation gross errors because the observation residuals are affected by both of them in a loosely-coupled integrated navigation system. In this research, an optimal Radial Basis Function (RBF) neural network-enhanced adaptive robust Kalman filter (KF) method is proposed to isolate and mitigate the influence of the two types of errors. In the proposed method, firstly a test statistic based on Mahalanobis distance is treated as judging index to achieve fault detection. Then, an optimal RBF neural network strategy is trained on-line by the optimality principle. The network’s output will bring benefits in recognizing the above two kinds of filtering fault and the system is able to choose a robust or adaptive Kalman filtering method autonomously. A field vehicle test in urban areas with a low-cost GNSS/INS integrated system indicates that two types of errors simulated in complex urban areas have been detected, distinguished and eliminated with the proposed scheme, success rate reached up to 92%. In particular, we also find that the novel neural network strategy can improve the overall position accuracy during GNSS signal short-term outages.

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Section: Introductionmentioning

confidence: 99%