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

Fingerprint

Multi-target Tracking
Gaussian Mixture
Target tracking
Sensor networks
Fusion reactions
Target
sensors
Sensors
Data Association
multiple target tracking
Multiple Target Tracking
tracking problem
estimates
Estimate
intersections
Decentralized
Sensor Networks
Uncertainty
Fusion
fusion

Keywords

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

Cite this

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
Shinde, Chinmay ; Das, Kaushik ; Lima, Rolif ; Vankadari, Madhu Babu ; Kumar, Swagat. / Adaptive Gaussian mixture-probability hypothesis density based multi sensor multi-target tracking. 2019 18th European Control Conference, ECC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 862-868 (2019 18th European Control Conference, ECC 2019).
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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.",
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Shinde, C, Das, K, Lima, R, Vankadari, MB & Kumar, S 2019, Adaptive Gaussian mixture-probability hypothesis density based multi sensor multi-target tracking. in 2019 18th European Control Conference, ECC 2019., 8796014, 2019 18th European Control Conference, ECC 2019, Institute of Electrical and Electronics Engineers Inc., pp. 862-868, 18th European Control Conference, ECC 2019, Naples, Italy, 25/06/19. https://doi.org/10.23919/ECC.2019.8796014

Adaptive Gaussian mixture-probability hypothesis density based multi sensor multi-target tracking. / Shinde, Chinmay; Das, Kaushik; Lima, Rolif; Vankadari, Madhu Babu; Kumar, Swagat.

2019 18th European Control Conference, ECC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 862-868 8796014 (2019 18th European Control Conference, ECC 2019).

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

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