Transition probability adaptivity-based interacting multiple model-based target tracking method

Abstract

The invention discloses a transition probability adaptivity-based interacting multiple model target tracking method. A motion track measurement value of a target is collected through a sensor and a motion state model set of the target is built; according to priori knowledge, a probability of an initial model and a transition probability matrix of the model are set; a state value is subjected to input interaction; an interactive value serves as an input value of filtering in the next step; parallel filtering is performed through filters under sub-models to obtain filter values under different models, and a probability of each model is updated; according to updated model change rates, a state transition matrix is corrected by adopting a hyperbolic sine inverse function to realize adaptivityof the transition probability matrix; and finally the filter values of the sub-models are subjected to weighted summation, thereby realizing target tracking. The adaptivity of the state transition matrix of an interacting multiple model algorithm is realized; and maneuvering and non-maneuvering target tracking can be realized to obtain a real motion track of the target, thereby improving trackingperformance of the interacting multiple model-based target tracking method.

Description

technical field [0001] The invention belongs to the technical field of target tracking, and in particular relates to an interactive multi-model target tracking method with adaptive transition probability. Background technique [0002] Target tracking refers to obtaining the actual trajectory of the target by filtering the target observation trajectory. It is a research direction with great practical value, wide application and far-reaching significance, especially in military applications. Accurate and fast target tracking is one of the core of military technology. one. When a moving target maneuvers, the motion model changes, and the use of single-model filtering algorithms such as Kalman filter and particle filter will reduce the tracking performance or even make it impossible to track. However, in most cases, the target to be tracked is maneuverable, so the research on maneuvering target tracking is of great significance and practical value. [0003] One of the basic id...

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Problems solved by technology

[0004] The purpose of the present invention is to provide an interactive multi-model target tracking algorithm based on transition probability matrix self-adaptation, through the posteriori information (model probability change rate) obtained after model probability update, analyze the change situation of transition probability matrix, and then adopt double The inverse function of the curved sine corrects it in real time, which solves the problem of low target tracking accuracy in the prior art due to prior information errors and cannot be updated in real time

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