A New Technique for Integrating MEMS-Based Low-Cost IMU and GPS in Vehicular Navigation

Neda Navidi,René Jr Landry,Denis Gingras,Jianhua Cheng

doi:10.1155/2016/5365983

Abstract

In providing acceptable navigational solutions, Location-Based Services (LBS) in land navigation rely mostly on integration of Global Positioning System (GPS) and Inertial Navigation System (INS) measurements for accuracy and robustness. The GPS/INS integrated system can provide better land-navigation solutions than the ones any standalone system can provide. Low-cost Inertial Measurement Units (IMUs), based on Microelectromechanical Systems (MEMS) technology, revolutionized the land-navigation system by virtue of their low-cost miniaturization and widespread availability. However, their accuracy is strongly affected by their inherent systematic and stochastic errors, which depend mainly on environmental conditions. The environmental noise and nonlinearities prevent obtaining optimal localization estimates in Land Vehicular Navigation (LVN) while using traditional Kalman Filters (KF). The main goal of this paper is to effectively eliminate stochastic errors of MEMS-based IMUs. The proposed solution is divided into two main components: (1) improving noise cancellation, using advanced stochastic error models in MEMS-based IMUs based on combined Autoregressive Processes (ARP) and first-order Gauss-Markov Process (1GMP), and (2) modeling the low-cost GPS/INS integration, using a hybrid Fuzzy Inference System (FIS) and Second-Order Extended Kalman Filter (SOEKF). The results obtained show that the proposed methods perform better than the traditional techniques do in different stochastic and dynamic situations.

Highlights

Increasing economical and environmental constraints as well as stringent safety requirements have triggered the development and widespread use of low-cost Land Vehicular Navigation (LVN) systems over the last decade
From the foregoing data, it can be seen that the proposed hybrid Fuzzy Inference System (FIS)-Second-Order Extended Kalman Filter (SOEKF), which used Autocorrelation Function (ACF)-based 1st-order GaussMarkov Process (1GMP) after 6LOD denoising, performed significantly better than did the traditional SOEKF without updating stochastic error states
It can be seen that the proposed hybrid FIS-SOEKF, which used ACF-based 1GMP after 2LOD and 6-Levels of Decomposition (LODs) denoising, “FS(ACF2)” and “FS(ACF6),” performed significantly better than did the traditional SOEKF without updating stochastic error states

Summary

Introduction

Increasing economical and environmental constraints as well as stringent safety requirements have triggered the development and widespread use of low-cost Land Vehicular Navigation (LVN) systems over the last decade. LVNs can be used for protecting vehicles from theft in vehicle tracking systems or for reducing the greenhouse gas emissions in environmental monitoring systems. They can be used in autonomous car navigation systems and emergency assistance services. Two main topics will be studied : Second-Order Extended Kalman Filter (SOEKF) as a data-fusion technique for automotive navigation and stochastic error modeling technique in Inertial Navigation Systems. As mentioned in Introduction, in order to perform optimal estimation using the classical Kalman Filter-based technique on the nonlinear and stochastic INS dynamic model, the model must be linearized around assumptions. First-order and second-order Extended Kalman Filter (EKF) are two Kalman Filter-based techniques which linearized based on the assumption of first and second orders of a Taylor series expansion

Objectives

Methods

Findings

Conclusion