Author
Shuo Wang
Bio: Shuo Wang is an academic researcher from Liaoning University of Technology. The author has contributed to research in topics: Adaptive filter & Filter (signal processing). The author has an hindex of 1, co-authored 2 publications receiving 2 citations.
Papers
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01 Jul 2017
TL;DR: In this paper, an adaptive probability hypothesis density (PHD) filter was proposed for multi-target tracking, where both the completed clutter process and the single-target measurement likelihood were improved based on the Bayesian theory.
Abstract: In order to improve tracking performance of the existing probability hypothesis density (PHD) filters, we present an adaptive filter for multi-target tracking in this paper. At first, both the completed clutter process and the single-target measurement likelihood are improved based on the Bayesian theory. Then, the target cardinality is corrected using the adaptive detection gate. What's more, a novel particle implementation is explored step by step. Numerical study results have been carried out to confirm the promising tracking performance of the proposed PHD filter.
2 citations
01 Jul 2017
TL;DR: In this paper, an improved Bernoulli filter was proposed to improve tracking accuracy and filtering efficiency of the existing Bernoullis filter in order to achieve the extended target tracking (ETT).
Abstract: The extended target tracking (ETT) is widely used in various surveillance applications. In order to improve tracking accuracy and filtering efficiency of the existing Bernoulli filters, we present an improved Bernoulli filter in this paper. Firstly, the filtering equations of the proposed filter are analytically derived. After employing the weight optimization scheme, we discuss the particle implementation based on adaptive detection gate and scatter number. Finally, the numerical simulations indicate that the proposed filter can achieve the ETT with promising tracking performance.
Cited by
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Journal Article•
TL;DR: In this paper, an improved multiple model probability hypothesis density (PHD) filter is proposed to solve the particle degenerate problem and the Poisson assumption for the target number distribution.
5 citations
25 Jul 2018
TL;DR: The numeric simulation indicated that the proposed PHD filter can effectively track occluded target in cluttered environment with stable track estimation and accurate cardinality statistics.
Abstract: To deal with the problem of unsatisfactory performance of close target tracking in cluttered environmental by using the standard probability hypothesis density (PHD) filter, this paper presented an improved PHD filter. First, the track management scheme was proposed to correct the number of targets and clutters. As a result, the number of required particles was reduced. In the measurement update step, the matrix consisted of both particle weight and clutter intensity was built to reassign weight proportion under the unchanged sum of all elements based on the row-column operation. The numeric simulation indicated that the proposed filter can effectively track occluded target in cluttered environment with stable track estimation and accurate cardinality statistics.