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

He, Shaoming; Shin, Hyo‐Sang; Tsourdos, Antonios

doi:10.1109/jsen.2018.2863105

Cited by 56 publications

(25 citation statements)

References 33 publications

(36 reference statements)

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“…The traditional methods are often inapplicable in actual practice, such as resolving the enterprise management problem in an unpredictable dynamic environment of modern business, requiring a huge settlement volume. On the whole, distributed systems are increasingly used parallelly, dividing a batch of tasks between several computing threads (devices) [8][9][10][11]. Moreover, real-world problems lead to restrictions on communications channels in the strategies intended to solve this type of problem effectively [12].…”

Section: Related Workmentioning

confidence: 99%

Emergent Intelligence via Self-Organization in a Group of Robotic Devices

et al. 2021

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Networked systems control is a known problem complicated because of the need to work with large groups of elementary agents. In many applications, it is impossible (or difficult) to validate agent movement models and provide sufficiently reliable control actions at the elementary system components level. The evolution of agent subgroups (clusters) leads to additional uncertainty in the studied control systems. We focus on new decentralized control methods based on local communications in complex multiagent dynamical systems. The problem of intelligence in a complex world is considered in connection to multiagent network systems, including a system named airplane with feathers, load balancing, and the multisensor-multitarget tracking problem. Moreover, the new result concerning the emergency of intelligence in a group of robots is provided. All these methods follow the paradigm of the direct reaction of each element (agent) of the system to its sensory data of current situation observations and the corresponding data from a limited number of its neighbors (local communications). At the same time, these algorithms achieve a mutual goal at the macro level. All of the considered emergent intelligence appearances inspire the necessity to “rethink” the previously recognized concepts of computability and algorithm in computer science.

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Section: Related Workmentioning

confidence: 99%

Emergent Intelligence via Self-Organization in a Group of Robotic Devices

et al. 2021

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“…In [28], the suboptimal JPDA technology was used to achieve data association for multi-target tracking, and then a Gaussian particle filter was used for target tracking, which could deal with non-trivial nonlinear conditions and improve the accuracy of multi-target tracking. In [29], to accommodate the nonlinear measurements, the well-known UKF was utilized in the local JPDA filter.…”

Section: Related Workmentioning

confidence: 99%

An Improved Unscented Particle Filter Approach for Multi-Sensor Fusion Target Tracking

Luo

Wang

Chen

et al. 2020

Sensors

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In this paper, a new approach of multi-sensor fusion algorithm based on the improved unscented particle filter (IUPF) and a new multi-sensor distributed fusion model are proposed. Additionally, we employ a novel multi-target tracking algorithm that combines the joint probabilistic data association (JPDA) algorithm and the IUPF algorithm. To improve the real-time performance of the UPF algorithm for the maneuvering target, minimum skew simplex unscented transform combined with a scaled unscented transform is utilized, which significantly reduces the calculation of UPF sample selection. Moreover, a self-adaptive gain modification coefficient is defined to solve the low accuracy problem caused by the sigma point reduction, and the problem of particle degradation is solved by modifying the weights calculation method. In addition, a new multi-sensor fusion model is proposed, which better integrates radar and infrared sensors. Simulation results show that IUPF effectively improves real-time performance while ensuring the tracking accuracy compared with other algorithms. Besides, compared with the traditional distributed fusion architecture, the proposed new architecture makes better use of the advantages of radar and an infrared sensor and improves the tracking accuracy.

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“…The former shows huge computation burden and is not easy to implement [8]. The latter includes nearest neighbor data association (NNDA), probabilistic data association (PDA), multiple hypothesis tracking (MHT) and joint probabilistic data association (JPDA) algorithm [9,10]. NNDA is not appropriate for environments with severe clutters or multiple targets [11,12], and PDA is only suitable for single-target and sparse multi-target environments [13].…”

Section: Introductionmentioning

confidence: 99%

A Novel Algorithm of Joint Probability Data Association Based on Loss Function

2021

KSII TIIS

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In this paper, a joint probabilistic data association algorithm based on loss function (LJPDA) is proposed so that the computation load and accuracy of the multi-target tracking algorithm can be guaranteed simultaneously. Firstly, data association is divided in to three cases based on the relationship among validation gates and the number of measurements in the overlapping area for validation gates. Also the contribution coefficient is employed for evaluating the contribution of a measurement to a target, and the loss function, which reflects the cost of the new proposed data association algorithm, is defined. Moreover, the equation set of optimal contribution coefficient is given by minimizing the loss function, and the optimal contribution coefficient can be attained by using the Newton-Raphson method. In this way, the weighted value of each target can be achieved, and the data association among measurements and tracks can be realized. Finally, we compare performances of LJPDA proposed and joint probabilistic data association (JPDA) algorithm via numerical simulations, and much attention is paid on real-time performance and estimation error. Theoretical analysis and experimental results reveal that the LJPDA algorithm proposed exhibits small estimation error and low computation complexity.

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

Cited by 56 publications

References 33 publications

Emergent Intelligence via Self-Organization in a Group of Robotic Devices

Emergent Intelligence via Self-Organization in a Group of Robotic Devices

An Improved Unscented Particle Filter Approach for Multi-Sensor Fusion Target Tracking

A Novel Algorithm of Joint Probability Data Association Based on Loss Function

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