Approximation method of label set probability density function

Abstract

The invention discloses an approximation method of a label set probability density function. An existing set probability density function approximation method does not consider the real correlation between targets, and is unsuitable for a generalized multi-target system. Therefore, the invention provides a set probability density function approximation method based on correlation between the targets. The method comprises the following steps of: firstly computing a correlation coefficient between the Bernoulli sets corresponding to the targets; and then grouping the Bernoulli sets according to the obtained correlation coefficient; secondly providing the probability density function of each group of subsets based on the computing criterion of the set marginal probability density function; and finally utilizing a mutual independent relation between the subsets to jointly represent the multi-target posterior probability approximation by the probability density function of each group of subsets. Through the adoption of the method disclosed by the invention, not only the real correlation between the targets is retained, but also the dimensionality reduction of the high-dimensional and complex set multi-target probability density is performed; the representation of the set probability density function is greatly simplified, the approximation error is small, and the method has good engineering application prospects.

Description

technical field [0001] The invention belongs to the field of statistical signal processing, in particular to an approximate characterization method of an aggregate probability density function under a random set framework. Background technique [0002] The main purpose of multi-object inference is to simultaneously estimate the number of objects and the state of multiple objects. The application of multi-target inference involves a wide range of fields, such as biology, molecular physics, computer vision, wireless sensor networks, multi-target tracking and other fields. The set cardinality of the random set and the number of elements in each set are random variables, which match the behavior of the multi-objective state very well. Therefore, the statistical characteristics of the multi-objective state in the multi-objective system are usually described by the random set. [0003] The aggregate probability density function is a basic statistical description tool for random a...

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Problems solved by technology

[0004] The technical problem to be solved by the present invention is that the unapproximated set probability density function has the problem of explosive growth in the number of combinations and

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