Next Article in Journal
Active2Gether: A Personalized m-Health Intervention to Encourage Physical Activity
Next Article in Special Issue
Static and Dynamic Accuracy of an Innovative Miniaturized Wearable Platform for Short Range Distance Measurements for Human Movement Applications
Previous Article in Journal
Cataract Surgery Performed by High Frequency LDV Z8 Femtosecond Laser: Safety, Efficacy, and Its Physical Properties
Previous Article in Special Issue
An Approach to Speed up Single-Frequency PPP Convergence with Quad-Constellation GNSS and GIM
Article

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

by 1, 1,* and 2
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.
Sensors 2017, 17(6), 1431; https://doi.org/10.3390/s17061431
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)
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
Show Figures

Figure 1

MDPI and ACS Style

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. https://doi.org/10.3390/s17061431

AMA Style

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(6):1431. https://doi.org/10.3390/s17061431

Chicago/Turabian Style

Xu, Qimin, Xu Li, and Ching-Yao Chan. 2017. "A Cost-Effective Vehicle Localization Solution Using an Interacting Multiple Model−Unscented Kalman Filters (IMM-UKF) Algorithm and Grey Neural Network" Sensors 17, no. 6: 1431. https://doi.org/10.3390/s17061431

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop