Rough fuzzy joint probabilistic association for tracking multiple targets in the presence of ECM

Satapathi, Gnane Swarnadh; Srihari, Pathipati

doi:10.1016/j.eswa.2018.03.067

Cited by 10 publications

(5 citation statements)

References 14 publications

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“…In all scenarios, it is assumed that local tracking on the sensors are independently performed by FD-JPDAF [34] based on received measurements, and the estimated status of targets are sent to the FC for the combination. Additionally, the clutter model is assumed to be spatially Poisson distributed with known parameter λ = 1 (the number of false measurements per unit of volume (km 2 )) [40].…”

Section: Simulationsmentioning

confidence: 99%

An Adaptive Density-Based Fuzzy Clustering Track Association for Distributed Tracking System

2019

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The problem of duplicate track determination called ''Track-to-Track association'' occurs when a target is reported by different sensors, and it is regarded as one of the most important challenges in distributed multi-sensor tracking systems. The present study aimed to propose a density-based fuzzy clustering method for solving the track-to-track association problem in distributed multi-sensor tracking systems. Unlike the previously published solutions, the proposed method does not need any information about the number of targets, due to the use of the density-based clustering approach. Proposed method has low computational overhead and can be used in real-time tracking systems. In addition, the proposed method uses the maximum entropy approach to determine the membership degree of single target related tracks and combines them. This paper presents three scenarios including sensors with complete and incomplete overlapping by considering the bias and a different number of sensors and targets for evaluating the proposed method based on the Monte Carlo simulation. The results indicate the improvement of the efficiency in comparison with the FTF approach. The efficiency of proposed method's results is close to the results of Bayesian minimum mean square error criterion that gives best possible results.INDEX TERMS Density clustering, distributed target tracking, multi sensor fusion, track association.

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Section: Simulationsmentioning

confidence: 99%

An Adaptive Density-Based Fuzzy Clustering Track Association for Distributed Tracking System

2019

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“…Recently, fuzzy theory is widely used in target tracking and location. Fuzzy inference [21]- [22] and fuzzy clustering [24]- [26] are the two most commonly used techniques. [21] proposes a selective adaptive method for adjusting noise covariance using fuzzy logic, which use IMM to locate.…”

Section: Introductionmentioning

confidence: 99%

“…In [23], RSS is used to calculate the distance between MN and BN, which is nonlinear and obtained by interpolating several linear equations by fuzzy technology. Additionally, fuzzy clustering is mainly used for data association [24]- [26], using the similarity characteristics of fuzzy membership degree and correlation probability. Such as fuzzy c-means clustering [24] and maximum entropy fuzzy clustering [25].…”

Section: Introductionmentioning

confidence: 99%

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A mobile localization algorithm based on fuzzy estimation for serious NLOS scenes

Wang

Gong

Yang

2023

Peer-to-Peer Netw. Appl.

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“…For the actual detection images, the fusion validity used to describe the degree of influence of the difference feature value is mostly predicted and estimated through the fusion results of existing, limited and similar scene images, so the measurement of fusion validity is predictable and possible. Therefore, a possibility distribution is needed to adopt the description of fusion validity change process [14][15][16]. However, the possibility distribution can express the dynamic changes of integration fusion validity, only describing double-sided properties, but it cannot describe the middle state of fusion validity of difference features [17][18][19], which has a great influence on fusion algorithm selection.…”

Section: Introductionmentioning

confidence: 99%

Image Fusion Algorithm Selection Based on Fusion Validity Distribution Combination of Difference Features

Yang

Guo

2021

Electronics

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Aiming at addressing the problem whereby existing image fusion models cannot reflect the demand of diverse attributes (e.g., type or amplitude) of difference features on algorithms, leading to poor or invalid fusion effect, this paper puts forward the construction and combination of difference features fusion validity distribution based on intuition-possible sets to deal with the selection of algorithms with better fusion effect in dual mode infrared images. Firstly, the distances of the amplitudes of difference features between fused images and source images are calculated. The distances can be divided into three levels according to the fusion result of each algorithm, which are regarded as intuition-possible sets of fusion validity of difference features, and a novel construction method of fusion validity distribution based on intuition-possible sets is proposed. Secondly, in view of multiple amplitude intervals of each difference feature, this paper proposes a distribution combination method based on intuition-possible set ordering. Difference feature score results are aggregated by a fuzzy operator. Joint drop shadows of difference feature score results are obtained. Finally, the experimental results indicate that our proposed method can achieve optimal selection of algorithms that has relatively better effect on the fusion of difference features according to the varied feature amplitudes.

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Rough fuzzy joint probabilistic association for tracking multiple targets in the presence of ECM

Cited by 10 publications

References 14 publications

An Adaptive Density-Based Fuzzy Clustering Track Association for Distributed Tracking System

An Adaptive Density-Based Fuzzy Clustering Track Association for Distributed Tracking System

A mobile localization algorithm based on fuzzy estimation for serious NLOS scenes

Image Fusion Algorithm Selection Based on Fusion Validity Distribution Combination of Difference Features

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