Adaptive Gaussian mixture-probability hypothesis density based multi sensor multi-target tracking

Chinmay Shinde, Kaushik Das, Rolif Lima, Madhu Babu Vankadari, Swagat Kumar

Research output: Chapter in Book/Report/Conference proceedingConference proceeding (ISBN)

Abstract

This paper addresses a novel multiple target tracking (MTT) problem in a decentralized sensors network (DSN) framework. The algorithm jointly estimates the number of targets and the states of the targets from a noisy measurement in the presence of data association uncertainty and missed detection. The standard GM-PHD filters estimate the multi-targets in a cluttered environment with an assumption that the target birth intensity is known or homogeneous. It results in inefficient tracking for new, occluded or missed targets. The issue is addressed by the proposed adaptive Gaussian birth components based estimation. A method based on covariance intersection fusion is proposed to address inter-sensor target data association.

Original languageEnglish
Title of host publication2019 18th European Control Conference, ECC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages862-868
Number of pages7
ISBN (Electronic)9783907144008
DOIs
Publication statusPublished - 1 Jun 2019
Event18th European Control Conference, ECC 2019 - Naples, Italy
Duration: 25 Jun 201928 Jun 2019

Publication series

Name2019 18th European Control Conference, ECC 2019

Conference

Conference18th European Control Conference, ECC 2019
CountryItaly
CityNaples
Period25/06/1928/06/19

Keywords

  • Decentralized data association
  • Multi-sensor systems
  • Multi-target tracking
  • Probability hypothesis density filter

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    Shinde, C., Das, K., Lima, R., Vankadari, M. B., & Kumar, S. (2019). Adaptive Gaussian mixture-probability hypothesis density based multi sensor multi-target tracking. In 2019 18th European Control Conference, ECC 2019 (pp. 862-868). [8796014] (2019 18th European Control Conference, ECC 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/ECC.2019.8796014