An Adaptive Interacting Multiple Model for Vehicle Target Tracking Method

Abstract

Abstract.In the field of traffic safety vehicle target tracking prediction as the background, this paper proposes an adaptive interacting multiple model tracking algorithm. According to the field of transportation vehicle movement state characteristics, based on the uniform(CV)and uniformly accelerated motion(CA)model, based on new information structure model of motion of the likelihood function, online adaptive adjustment model of the noise variance and the Markov matrix, realization of maneuvering target movement model and model set adaptation, not only improved IMM algorithm for tracking accuracy, and enhances the real-time performance of system, the simulation results show that, the algorithm for tracking precision compared to the traditional IMM method has bigger improvement.

An Adaptive Interactive Multiple-Model Algorithm Based on End-to-End Learning

“…But the robustness of the algorithm decreases due to over-regulation of TPM. References [18], [19] use an exponential function of the model probability change rate to map the adjustment rate of the TPM elements, and the algorithm is more stable, but the adjustment is not deep enough. Reference [20] calculates the TPM based on the likelihood ratio of models, but only two models are considered in the paper.…”

Multi-model fuzzy controller for vehicle lane tracking

“…Our main objective is to control the automated vehicle tracking path at all speeds, but the vehicle dynamic systems with characteristics of inherent nonlinear, parametric uncertainty, non-holonomic constraint and even wide range operation, it is always difficult to obtain an accurate model and there are considerable difficulties in the controller design. To handle this situation, multiple model approaches have become attractive research field [12][13][14]. Based on the divide and conquer strategy, the multi-model control algorithm is a potential solution for systems with large parameter variations [15].…”