Self-adapting interactive multiple model mobile target tracking method

Abstract

The invention relates to mobile tracking of vehicle targets in the communication and transportation field. A self-adapting interactive multiple model mobile target tracking method comprises the following steps of establishing mixed initialized input. including a covariance matrix of a mixed initial condition and a mixed initial state. of each model; establishing constant velocity (CV) and constant acceleration (CA) motion models; updating: calculating covariance matrix and innovation of an error according to a Kalman filter formulation; constructing a likelihood function of a target motion module by utilizing the innovation of a Kalman filter result, and calculating a Markov state transition probability matrix; carrying out estimation output after being fused by utilizing the Markov state transition probability matrix as weight of switch among each motion model. According to the self-adapting interacting multiple model mobile target tracking method disclosed by the invention, the problem that the error is increased or the tracking is failed caused by non-matching of a filter model and a target motion model due to the motion of a target in the traditional interactive multiple model algorithm is solved; the self-adapting interacting multiple model mobile target tracking method has the advantages of low calculation complexity and good tracking effect, and can be applied to target tracking of motor vehicles in the communication and transportation field.

Description

technical field [0001] The invention belongs to the field of maneuvering target tracking, and in particular relates to an adaptive interactive multi-model maneuvering target tracking method in the field of transportation when a moving car suddenly turns, accelerates, decelerates, and the like. Background technique [0002] Target tracking is to filter the target movement data received by the detection sensor combined with different observation sets generated by various uncertain information sources, and estimate the state parameters of the moving target, such as the distance, orientation, speed, acceleration, etc. of the target. [0003] The single-model maneuvering target tracking algorithm only performs well on non-maneuvering targets. When the target maneuvers, the tracking performance of the system decreases, and even causes the target to be lost. In order to track the maneuvering target, it is necessary to establish a reasonable motion model according to the motion cha...

AI Technical Summary

Problems solved by technology

In fact, when the motion model of the target has a certain tendency, the traditional IMM algorithm only realizes the "synthesis" between the motion models by mediating the weighting of the posterior probability of the motion model under different observation vector conditions, and does not take into account the Marl Irrationality of Cove's Transition Probability Matrix Design

[0004] In the field of transportation, when a moving car suddenly turns, accelerates, and decelerates, it also belongs to the maneuver of the target. Especially when the vehicle target suddenly enters a curve, the vehicle's motion state will be greater than that of the previous uniform speed or uniform acceleration linear motion. If it cannot effectively adapt to the maneuvering state of the target, it is easy to cause the loss of the target, and when the target is maneuvering, it is the moment when traffic accidents are most likely to occur

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