Polynomial forecast model of maneuvering target state equation and tracking method

Abstract

The present invention pertains to the automotive object tracking technique field, specifically to an automotive target state equation polynomial forecast model and tracking algorithm. According to the polynomial form of the uniformly variable motion, the invention takes the variable-accelerated motion as subsection uniformly variable motion to process, and provides a novel automotive target dynamic model-polynomial forecast model. The model can completely avoid the dependence on the polynomial coefficients of unknown motion, using the state equation established by the method of the invention can always accurately describe the motion dynamic state. Therefore, the proposed model of the present invention can be applied in various motion target state equations, the optimal filtering algorithm of the polynomial forecast model proposed by the invention is suitable for any automotive target state estimation problem that can be described by polynomial, which requires neither prior information about the parameters of target motion nor parameter identification and the like approach for regulating parameters of the established state model, thereby completely avoiding the estimated performance deterioration problem caused by the state model inaccuracy.

Description

technical field [0001] The invention belongs to the technical field of maneuvering target tracking, and in particular relates to a new method for modeling and tracking a maneuvering target state equation. Background technique [0002] The key of target tracking is whether useful information about the target state can be extracted effectively from observations, and an accurate target dynamic model is very conducive to the extraction of this information. In general, an accurate model is even more effective than a large amount of data, and the role of the model is even more important when the observation data is very limited [1] . [0003] Almost all maneuvering target tracking algorithms are model-based, that is, it is assumed that the movement of the target and the observation of the target can be represented by a known mathematical model with a sufficiently high accuracy. Among them, the most commonly used state space model is generally expressed by the following formula: ...

AI Technical Summary

Problems solved by technology

[0009] Among the existing target dynamic models, some models are simple in form and low in accuracy, but they are simple and flexible in application, such as the white noise acceleration model; while some more accurate models are complex in form and have large nonlinearity, requiring effective nonlinear The filtering algorithm also has a large amount of calculation. In addition, since this model depends on the state of the target, the tracking performance will be worsened when the target state is estimated inaccurately.

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