PHD (Probability Hypothesis Density) method for multi-target tracking in uneven clutter environment

Abstract

The embodiment of the invention provides a PHD (Probability Hypothesis Density) method for multi-target tracking in an uneven clutter environment, and relates to the field of the intelligent information processing. The method can improve the accuracy of the calculated result under the condition that the calculating amount is reduced. The method comprises the following steps: if the surveillance region is a sparse clutter region, the standard PHD can be directly predicted and updated to obtain estimated target amount and target state; if the surveillance region is an intensive clutter region, convex hulls are searched to determine a clutter region; when the PHD is predicted gradually at different moment, return waves not contained in the clutter region are selected for calculation ; when the PHD is updated, the return waves of which the real target measurement exactly drops into the clutter region are added; Gauss components in the posteriori intensity are cut and merged to obtain the target intensity; then, the weight sum of all the Gauss components in the target intensity are calculated to obtain estimated target amount in the surveillance region; the Gauss components with weight greater than Tau in the target intensity are extracted to be in the estimated target state.

Description

technical field [0001] The invention relates to the field of intelligent information processing, in particular to a probability hypothesis density method for multi-target tracking in an uneven clutter environment. Background technique [0002] Multi-target tracking is a method of estimating the number of targets and the state of each target from target measurements and clutter. Most of the traditional methods to deal with multi-target tracking problems are based on data association, and need to establish the corresponding relationship between measurement and target, such as nearest neighbor method (NN), joint probabilistic data association algorithm (JPDA), multiple hypothesis tracking algorithm (MHT) . In recent years, multi-target tracking algorithms based on Random Finite Set (RFS) theory have received great attention. The Probability Hypothesis Density (PHD) filtering algorithm proposed by Mahler is an algorithm that effectively reduces the computational complexity com...

AI Technical Summary

Problems solved by technology

[0004] The above-mentioned methods are all PHD methods for target tracking in dense clutter environments, and applying these methods in a complex clutter environment with both sparse clutter and dense clutter will result in the sparse clutter area and The amount of calculation in the dense clutter area is very large, and fast calculation cannot be achieved. However, the amount of calculation in the sparse clutter area is not so large. The application of these methods will lead to a large amount of calculation, and the results obtained may not be ideal.

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