A Two-Task Hierarchical Constrained Tri-Objective Optimization Approach for Vehicle State Estimation Under Non-Gaussian Environment
Although several estimation methods have been developed for improving the performance of vehicle’s movement information, the accuracy of the latter is still a challenging issue. The main problem is in the way noise disturbances are handled and the estimation methods which are not efficient. This paper presents a two-task hierarchical method named hierarchical constrained tri-objective optimization (HCTO) to improve the vehicle state accuracy. In this method, process and measurement noises are first optimized separately and then, based on the obtained optimal solutions, the upper bound for the state estimation error is addressed. These noises are assumed to follow a generalized error distribution (GED) and the maximum likelihood estimation (MLE) model is adopted with the intention of estimating sample parameters; thus reducing the computational burden of HCTO. Moreover, the optimal solution of the bound which is defined in terms of linear matrix inequality (LMI) approach is obtained via semi-definite programming (SDP) method. The performance is analyzed with respect to real-world data collected using Smartphone-based vehicular sensing model. The proposed method is tested when noises are both Gaussian and non-Gaussian distributed and also compared with the existing nonlinear estimation methods. Experimental results confirm that HCTO presents higher accuracy estimation and lower root mean-square error (RMSE) for vehicle state than for instance, PF and UKF.
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Document Type: Research Article
Affiliations: College of Computer Science and Electronic Engineering, Hunan University, 410082, Changsha, China
Publication date: December 1, 2015
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