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

TLDR

The authors propose a cost‑effective vehicle localization system that adapts to uncertain inertial sensor noise and mitigates GPS outages. They implement an Interacting Multiple Model Unscented Kalman Filter with three UKFs of varying noise covariances, run two parallel IMM‑UKFs when GPS is available—one fusing GPS, vehicle sensors, and MEMS‑RISS, the other using only vehicle sensors and MEMS‑RISS—and use the state‑vector differences as training data for a Grey Neural Network that predicts and corrects position errors during GPS loss. Road‑test experiments demonstrate that the proposed solution outperforms all compared localization methods.

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.