Abstract
In this paper, we propose a distributed Gaussian mixture cardinalized probability hypothesis density (GM-CPHD) filter based on generalized inverse covariance intersection that fuses multiple node information effectively for multi-target tracking applications. Covariance intersection (CI) is a well-known fusion method that produces a conservative estimate of the joint covariance regardless of the actual correlation between the different nodes. Inverse covariance intersection (ICI) is the updated version to obtain fusion results that guarantee consistency and less conservative than CI. However, the ICI is not extended to multi-sensor multi-target tracking system yet. Since the ICI formula can be re-structured as naïve fusion with covariance inflation in Gaussian pdf, this method was applied to the GM-CPHD with generalization. The formula for random finite set (RFS) fusion was derived in the same way as the conventional generalized covariance intersection (GCI) based fusion. The simulation results for multi-target tracking show that the proposed algorithm has smaller optimal sub-pattern assignment (OSPA) errors than naïve fusion and the GCI-based fusions.
Highlights
Multi-target tracking (MTT) is the research that tracks multiple targets with their states, such as position and velocity
MTT problem can be solved by finite set statistics (FISST) without complicated data association, which is based on a random finite set (RFS) [1]
We propose a distributed Gaussian mixture cardinalized probability hypothesis density (GM-cardinalized PHD (CPHD)) filter based on generalized inverse covariance intersection (GICI)
Summary
Multi-target tracking (MTT) is the research that tracks multiple targets with their states, such as position and velocity. After the research on centralized MS-MT system, distributed MS-MT system with Gaussian mixture (GM) implementation was proposed, including average consensusbased GM-CPHD [22], [23] and GM-MB [24], [25]. To obtain a fusion result that guarantees consistency and less conservative than CI, inverse covariance intersection (ICI) was proposed [32]. We propose a distributed GM-CPHD filter based on GICI. The suggested method uses the covariance inflation technique to naïve fusion, which will be discussed Notice that it is based on covariance inflation of the CI fusion without feedback which is different from the information sharing of the federated Kalman filter [36], [37]. By this approach, we can formulate GICI based GM-CPHD filter.
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