Track‐to‐track fusion with target existence

Mušicki, D.; Song, Taek Lyul; Lee, Hae Ho; Chen, Xin; Kirubarajan, T.

doi:10.1049/iet-rsn.2013.0396

Cited by 12 publications

(15 citation statements)

References 31 publications

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“…In MTT over a sensor network, each sensor node orders its estimated tracks differently and therefore track-totrack association is required to associate the tracks from different sensors that represent the ith target [44]. There are a number of elegant choices for solving the track-to-track association problem in combinatorial optimisation by reformulating the problem as a network flow [45] or using approximate Lagrangian relaxation approach [46]- [48].…”

Section: Centralised Jpda Filter: a Benchmarkmentioning

confidence: 99%

Distributed Joint Probabilistic Data Association Filter With Hybrid Fusion Strategy

Shin

Tsourdos

2020

IEEE Trans. Instrum. Meas.

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This paper investigates the problem of distributed multi-target tracking over a large-scale sensor network, consisting of low-cost sensors. Each local sensor runs a joint probabilistic data association filter to obtain local estimates and communicates with its neighbours for information fusion. The conventional fusion strategies, i.e., consensus on measurement and consensus on information, are extended to multi-target tracking scenarios. This means that data association uncertainty and sensor fusion problems are solved simultaneously. Motivated by the complementary characteristics of these two different fusion approaches, a novel distributed multi-target tracking algorithm using a hybrid fusion strategy, e.g., a mix between consensus on measurement and consensus on information, is proposed. A distributed counting algorithm is incorporated into the tracker to provide the knowledge of the total number of sensor nodes. The new algorithm developed shows advantages in preserving boundedness of local estimates, guaranteeing global convergence to the optimal centralised version and being implemented without requiring no global information, compared with other fusion approaches. Simulations clearly demonstrate the characteristics and tracking performance of the proposed algorithm. Index Terms Multi-target tracking, Multi-sensor fusion, Distributed fusion, Joint probabilistic data association, Hybrid fusion I. INTRODUCTION Wireless sensor networks have attracted great attention in recent decades thanks to their critical importance in a wide range of applications, including environmental monitoring [1], ground vehicle tracking [2]-[4], air traffic control [5], spacecraft navigation [6], vision-based pedestrian tracking [7], etc. The availability of low-cost sensors has enabled employment of multiple sensor nodes to large-scale sensing tasks [8]. Low-cost sensors, however, are generally subject to high clutter rate and low detection probability, leading to performance degradation, especially in multi-target tracking (MTT) scenarios [9]. Leveraging proper fusion algorithms over the sensor network could counteract the drawbacks of low-cost sensors and thus enhance the tracking performance. To this end, this paper aims to address the problem of distributed MTT in a sensor network. The multi-sensor data integration or fusion can be categorised into three architectures in general: centralised, decentralised and distributed [10], [11], as shown in Fig. 1. The centralised fusion architecture simultaneously Shaoming He, Hyo-Sang Shin and Antonios Tsourdos are with the School of Aerospace, Transport and Manufacturing, IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2 processes the measurements provided by all sensors in a fusion centre, which directly connects with all sensor nodes. Although data fusion through a fusion centre is ideally Bayesian optimal, the fusion centre cannot effectively communicate with all sensors for large-scale sensor networks because of physical constraints, e.g., communication delay, limiting communic...

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Section: Centralised Jpda Filter: a Benchmarkmentioning

confidence: 99%

Distributed Joint Probabilistic Data Association Filter With Hybrid Fusion Strategy

Shin

Tsourdos

2020

IEEE Trans. Instrum. Meas.

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“…In multi-target tracking cases, trackto-track association is essential to find the target source of local tracks that come from different sensors. Track-to-track association, similar to measurement-to-track association in MHT, is a multidimensional assignment problem, known as S − D (S−dimensional) assignment, where S refers to the number of sensors [20]- [25].…”

Section: Introductionmentioning

confidence: 99%

Multi-Sensor Multi-Target Tracking Using Domain Knowledge and Clustering

Shin

Tsourdos

2018

IEEE Sensors J.

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This paper proposes a novel joint multi-target tracking and track maintenance algorithm over a sensor network. Each sensor runs a local joint probabilistic data association (JPDA) filter using only its own measurements. Unlike the original JPDA approach, the proposed local filter utilises the detection amplitude as domain knowledge to improve the estimation accuracy. In the fusion stage, the DBSCAN clustering in conjunction with statistical test is proposed to group all local tracks into several clusters. Each generated cluster represents the local tracks that are from the same target source and the global estimation of each cluster is obtained by the generalized covariance intersection (GCI) algorithm. Extensive simulation results clearly confirms the effectiveness of the proposed multisensor multi-target tracking algorithm.

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“…In this paper, the confirmed tracks generated by each local sensor, regardless of the false and true nature of the tracks, are transmitted to a fusion centre with track information, including the PTE. The local sensor tracks play the role of measurements and are used to update the system tracks at the fusion centre [26, 27]. The track fusion in [26] is only used to handle a fusion problem with both a local sensor and a remote sensor, where the LMITS algorithm is applied; however, it is clear that the fusion approach is based on sequential track fusion with track existence, which is the hallmark of this work.…”

Section: Introductionmentioning

confidence: 99%

“…The local sensor tracks play the role of measurements and are used to update the system tracks at the fusion centre [26, 27]. The track fusion in [26] is only used to handle a fusion problem with both a local sensor and a remote sensor, where the LMITS algorithm is applied; however, it is clear that the fusion approach is based on sequential track fusion with track existence, which is the hallmark of this work. In [27], the authors propose multi‐sensor track fusion with track existence in a parallel method for distributed sensor systems.…”

Section: Introductionmentioning

confidence: 99%