Fuzzy logic data correlation approach in multisensor–multitarget tracking systems

Aziz, Ashraf M.; Tummala, Murali; Cristi, R.

doi:10.1016/s0165-1684(99)00008-0

Cited by 62 publications

(14 citation statements)

References 28 publications

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“…The output variables in FDA are usually the degree of correlation between observations and tracks, so that the maximum values are searched for as solutions to the association problem. As an alternative to rule-based fuzzy systems, Aziz et al (1999Aziz et al ( , 2007 propose the application of fuzzy clustering means (FCM) algorithm. Their iterative algorithm applies FCM over an active set of measurements and tracks, identifies the pairs with highest membership and removes them to reduce the size of the problem for the next iteration until all measurements are assigned to a track.…”

Section: Sensor Data Association and Soft-computing Approachesmentioning

confidence: 99%

“…In the case of sensor-based multitarget tracking, these techniques usually apply statistical inputs so that the membership function estimation can be based on statistical analysis such as the possibility/ probability principle developed by Singh et al for optimal membership generation in sensor data processing (Singh et al 1997). Other authors, such as (Aziz et al 1999) propose the use of fuzzy clustering methods to avoid the development of rules, although from our point of view this means disregarding useful knowledge to solve the problem.…”

Section: If Overlap Is and Deformation Is Then Confidence Is If Comentioning

confidence: 99%

“…Different proposals range from simple and suboptimal, such as Nearest Neighbor (NN), to other more complex and close to optimal approaches, whose cost is usually excessive when the number of targets and measurements increase, such as Multiple Hypotheses Tracking (MHT), and Joint Probabilistic Data Association Filter (JPDAF). Fuzzy systems have been traditionally applied to data association (Fuzzy Data Association, FDA) with sensor sources such as radar positioning, infrared and Doppler measurements, taking advantage of the flexibility of fuzzy logic to model uncertainty coming from heterogeneous sources and natural ability to handle different types of information (Chen 2000;Han et al 2003;Ermin et al 2000;Gad et al 2002;Aziz et al 1999Aziz et al , 2007. A more detailed analysis of approaches to data association based on this paradigm is presented in the next section.…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy region assignment for visual tracking

et al. 2011

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In this work we propose a new approach based on fuzzy concepts and heuristic reasoning to deal with the visual data association problem in real time, considering the particular conditions of the visual data segmented from images, and the integration of higher-level information in the tracking process such as trajectory smoothness, consistency of information, and protection against predictable interactions such as overlap/occlusion, etc. The objects' features are estimated from the segmented images using a Bayesian formulation, and the regions assigned to update the tracks are computed through a fuzzy system to integrate all the information. The algorithm is scalable, requiring linear computing resources with respect to the complexity of scenarios, and shows competitive performance with respect to other classical methods in which the number of evaluated alternatives grows exponentially with the number of objects.

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Section: Sensor Data Association and Soft-computing Approachesmentioning

confidence: 99%

Section: If Overlap Is and Deformation Is Then Confidence Is If Comentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fuzzy region assignment for visual tracking

et al. 2011

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“…An association technique is essential processing in multisensor data fusion systems (Hall, 1992). We focus on association in case of surveillance systems (Bogner et al, 1998;Rutten et al, 2004;Aziz et al, 1999).…”

Section: Introductionmentioning

confidence: 99%

“…There are many approaches in the literatures to perform track association and track fusion (Hall, 1992;Aziz et al, 1999). The first track association technique was developed by (Singer and Kanyuck, 1970).…”

Section: Introductionmentioning

confidence: 99%

A New Method for Multipath Clustering for Over-the-Horizon Radar

Aziz¹,

Abdelrahman²,

Abouelatta³

2016

American Journal of Applied Sciences

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Over-the-Horizon Radar (OTHR) exploits the refraction of high frequency radiation through the ionosphere layers to detect targets beyond the line-of-sight horizon. Multipath propagation between the radar and the detected targets may results in multiple spatially separated tracks for a single target to be observed at the receiver site. Consequently there is a heavy traffic, especially in case of multiple targets, to be associated and combined if there are tracks represent the same target. In this study, a new method for multipath clustering for OTHR is proposed. The proposed method describes the similarities between all tracks as fuzzy degrees of membership. This method can operate in real-time and can perform clustering and fusion of OTHR tracks with tracks from other sources such as targets reporting global positioning systems and microwave radars. The proposed method has the advantages of less computations and high efficiency compared to conventional fuzzy logic clustering techniques. It has also the advantage of treating all the tracks data at once rather than pairwise. The efficiency of the proposed method is demonstrated using simulated examples.

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