Abstract
When the extension state of the non-ellipsoidal extended target (NET) changes, the performance of traditional multiple target tracking algorithms based on the constant number of sub-objects will decrease. To solve this problem, this paper proposes a gamma Gaussian inverse Wishart probability hypothesis density filter for non-ellipsoidal extended targets with varying number of sub-objects, called VN-NET-GGIW-PHD filter. In the proposed filter, each NET is considered as a combination of multiple spatially close sub-objects, and the label management is introduced to realize the association between the NET and corresponding sub-objects. Then, by target spawning and combination, the number of sub-objects for approximating the extension state of each NET can be adjusted automatically. Furthermore, to obtain the partition of the measurement set, an approach based on the clustering by fast search and find of density peaks (CFSFDP) algorithm and expectation maximization (EM) algorithm is proposed. Simulation results show that the proposed filter can adaptively adjust the number of sub-objects and has better performance when the extension state of the NET changes.
Highlights
In the traditional point target tracking algorithms, each target can at most generate one measurement per time step [1], [2]
This paper proposes a VN-NET-GGIW-PHD filter for multiple non-ellipsoidal extended targets tracking with varying number of sub-objects
Combined with ellipsoidal extended target model and target label management, each non-ellipsoidal extended target is regarded as a combination of multiple spatially close sub-objects and the association between the NET and corresponding sub-objects is realized
Summary
In the traditional point target tracking algorithms, each target can at most generate one measurement per time step [1], [2]. The above filters can realize the multiple extended targets tracking, they cannot estimate the extension state of the target. Y. Gong et al.: GGIW-PHD Filter for Multiple NETs Tracking With Varying Number of Sub-Objects a Gamma distribution, a gamma GIW-PHD (GGIW-PHD) filter is further proposed by Granstrom in [16] to estimate the unknown measurement rate. An approach which models the extension state of target by multiple sub-objects is proposed in [19]. The multiple sub-objects approach has been further applied into PHD (called NET-GGIW-PHD) filter [20], CBMeMBer filter [21], GLMB filter [22] and realizes the multiple non-ellipsoidal extended targets tracking. A filter, called VN-NET-GGIW-PHD filter, is proposed with the varying number of sub-objects for multiple non-ellipsoidal extended targets tracking.
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