Abstract
In this paper, multiple resolvable group target tracking was considered in the frame of random finite sets. In particular, a group target model was introduced by combining graph theory with the labeled random finite sets (RFS). This accounted for dependence between group members. Simulations were presented to verify the proposed algorithm.
Highlights
Multi-target tracking is widely used in defense and civilian fields
Random Finite Set (RFS) method is already one of the important research directions of multi-target tracking [7] today. It can be deployed in a wide range of applications through a series of algorithms, such as the Probability Hypothesis Density (PHD) filter [8,9,10], Cardinalized PHD filter (CPHD) [11,12], multi-Bernoulli filter (MeMBer) [6], Generalized Labeled
A group structure is similar to a graph structure, so we use the asymmetric adjacency matrix to describe the structure of the resolvable group target
Summary
Multi-target tracking is widely used in defense and civilian fields. When multiple targets move in the air, they usually perform tasks in formation. RFS method is already one of the important research directions of multi-target tracking [7] today It can be deployed in a wide range of applications through a series of algorithms, such as the Probability Hypothesis Density (PHD) filter [8,9,10], Cardinalized PHD filter (CPHD) [11,12], multi-Bernoulli filter (MeMBer) [6], Generalized Labeled. Mahler proposed the MeMBer filter to solve this problem, which makes use of the existence probability and probability density of the target to make numerical approximation to the multi-target probability distribution function It simplifies the process of state extraction. Reference [51] considered the structure of the groups, but did not consider the impact of the cooperative relationship between group targets on the estimation, while References [19,52] made some work on collaborative noise
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