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Sensors 2017, 17(6), 1431; doi:10.3390/s17061431

A Cost-Effective Vehicle Localization Solution Using an Interacting Multiple Model−Unscented Kalman Filters (IMM-UKF) Algorithm and Grey Neural Network

1
School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
2
California Partners for Advanced Transportation Technology (PATH), University of California, Berkeley, CA 94720, USA
*
Author to whom correspondence should be addressed.
Received: 13 May 2017 / Revised: 14 June 2017 / Accepted: 15 June 2017 / Published: 18 June 2017
(This article belongs to the Special Issue Inertial Sensors for Positioning and Navigation)
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Abstract

In this paper, we propose a cost-effective localization solution for land vehicles, which can simultaneously adapt to the uncertain noise of inertial sensors and bridge Global Positioning System (GPS) outages. First, three Unscented Kalman filters (UKFs) with different noise covariances are introduced into the framework of Interacting Multiple Model (IMM) algorithm to form the proposed IMM-based UKF, termed as IMM-UKF. The IMM algorithm can provide a soft switching among the three UKFs and therefore adapt to different noise characteristics. Further, two IMM-UKFs are executed in parallel when GPS is available. One fuses the information of low-cost GPS, in-vehicle sensors, and micro electromechanical system (MEMS)-based reduced inertial sensor systems (RISS), while the other fuses only in-vehicle sensors and MEMS-RISS. The differences between the state vectors of the two IMM-UKFs are considered as training data of a Grey Neural Network (GNN) module, which is known for its high prediction accuracy with a limited amount of samples. The GNN module can predict and compensate position errors when GPS signals are blocked. To verify the feasibility and effectiveness of the proposed solution, road-test experiments with various driving scenarios were performed. The experimental results indicate that the proposed solution outperforms all the compared methods. View Full-Text
Keywords: vehicle localization; uncertain noise; Interacting Multiple Model; Grey Neural Network vehicle localization; uncertain noise; Interacting Multiple Model; Grey Neural Network
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Xu, Q.; Li, X.; Chan, C.-Y. A Cost-Effective Vehicle Localization Solution Using an Interacting Multiple Model−Unscented Kalman Filters (IMM-UKF) Algorithm and Grey Neural Network. Sensors 2017, 17, 1431.

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