Abstract
Conventional multi-sensor multi-target multi-Bernoulli (MS-MeMBer) filters are based on the assumption that each target produces at most one measurement per time step. However, this assumption is not always reasonable in practice as an extended target can generate multiple measurements per step due to the recent improvement in the sensor resolution. In this case, a potential estimation bias may occur in the current MS-MeMBer filters. Therefore, a novel extended target MS-MeMBer filter and its Gaussian inverse Wishart mixture implementation are given in this paper. Specifically, we modify the update process of the MS-MeMBer filter by assuming that the generation of extended target measurements follows an approximate Poisson-Body model. Simulation results validate that the proposed filter can effectively estimate the shape and position of the extended target.
Highlights
The random finite set (RFS) [1] has received much attention in the multi-target filtering domain due to its superiority in avoiding complicated data association steps [2]–[12]
As the single-sensor GGIW-cardinalized PHD (CPHD) filter is an important method for extended target tracking, in this paper, we extend it to the iterated-corrector GGIWCPHD (IC-GGIW-CPHD) filter for the multi-sensor case
We can see that with the increase of the clutter intensity, the average optimal subpattern assignment (OSPA) of the proposed ET-MS-MeMBer filter rises very slowly, while in contrast, the average OSPA of the IC-GGIW-CPHD filter grows
Summary
The random finite set (RFS) [1] has received much attention in the multi-target filtering domain due to its superiority in avoiding complicated data association steps [2]–[12]. For the extended target tracking, Gilholm et al proposed an approximate Poisson-Body (APB) model [15], in which the measurement is assumed to follow a multi-dimensional Poisson process. Other filters based on the RFS, which include the gamma Gaussian inverse Wishart CPHD (GGIWCPHD) filter, the extended target MeMBer filter, and the GGIW-GLMB/LMB filter, have been introduced for the extended target case [24]–[27]. Extended models such as the random hypersurface model [28] and the star-convex [29] have been proposed as well for targets with more complex shapes.
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