Notion and Structure of Sensor Data Fusion

Koch, Wolfgang

doi:10.1007/978-3-642-39271-9_1

Cited by 1 publication

(1 citation statement)

References 18 publications

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“…Extended target tracking (ETT) draws lots of attention in recent years because of its wide range of applications in traffic control [1], autonomous driving [2,3,4], person tracking [5,6] and etc. [7,8,9,10,11]. Since one extended target generates more than one measurement per time step, its shape information can be obtained.…”

Section: Introductionmentioning

confidence: 99%

Anti-Clutter Gaussian Inverse Wishart PHD Filter for Extended Target Tracking

Huang

Wang

2019

Sensors

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The extended target Gaussian inverse Wishart probability hypothesis density (ET-GIW-PHD) filter overestimates the number of targets under high clutter density. The reason for this is that the source of measurements cannot be determined correctly if only the number of measurements is used. To address this problem, we proposed an anti-clutter filter with hypothesis testing, we take into account the number of measurements in cells, the target state and spatial distribution of clutter to decide whether the measurements in cell are clutter. Specifically, the hypothesis testing method is adopted to determine the origination of the measurements. Then, the likelihood functions of targets and clutter are deduced based on the information mentioned above, resulting in the likelihood ratio test statistic. Next, the likelihood ratio test statistic is proved to be subject to a chi-square distribution and a threshold corresponding to the confidence coefficient is introduced and the measurements below this threshold are considered as clutter. Then the correction step of ET-GIW-PHD is revised based on hypothesis testing results. Extensive experiments have demonstrated the significant performance improvement of our proposed method.

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