An efficient algorithm for multisensor track fusion

Saha, Rajat; Chang, Kuo‐Chu

doi:10.1109/7.640278

Cited by 75 publications

(30 citation statements)

References 11 publications

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“…The fusion decision architecture based on fuzzy-neural network is as references [14][15][16][17][18][19][20]. Firstly, through a data assignment network, the input data from each sensor is allotted to every fusion sub-node.…”

Section: Fusion Decision Based On Fuzzy-neural Networkmentioning

confidence: 99%

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A new approach and system for attentive mobile learning based on seamless migration

Zhang

2010

Appl Intell

140

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Seamless migration is one of pervasive computing applications. The function of seamless mobility is suitable for mobile services such as mobile Web-based learning. In this paper, we propose an approach that supports an attentive mobile learning paradigm. This mobile learning dynamically follows the user from place to place and machine to machine without user's awareness or intervention by active service. This capability can be obtained by componentbased smart system and agent-based migrating mechanism. To demonstrate the approach, the theoretical background of fuzzy-neural network for attentive service will be explained. The proposed fusion decision method is based on fuzzyneural network which can make the input signal data to fuse better. Using online tuning, the fusion processing can be accelerated and the fusion belief degree can be improved. Description of mobile learning task and migrating granularity of the task is suggested. The design of the seamless migration mechanism is introduced. This includes solving several important sub-problems, such as transferring delay, transferring failure, and residual computation dependency. Our implemented system for attentive mobile learning based on

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Section: Fusion Decision Based On Fuzzy-neural Networkmentioning

confidence: 99%

“…Based on fuzzy-neural network, the system can reason and make decisions [14][15][16][17][18][19][20], so it can be used for deciding when to supply the attentive service. In order to realize attentive mobile learning based on seamless migration of pervasive computing, we will suggest relative fuzzy-neural network approaches.…”

Section: Introductionmentioning

confidence: 99%

A new approach and system for attentive mobile learning based on seamless migration

Zhang

2010

Appl Intell

140

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show abstract

“…Other related publications cited in the Table 2.8 are [67], [68], [91], [93,94], [95], [115], [116], [117], [124], [125], [127] and [128]. Other related publications cited in the Table 2.9 are [131], [169,170], [170,171,172], [183], [187], [200], [240], [241], [244], [249], [252], [253], [254,255,256], [257], [259], [275], [277], [278], [304] and [307]. …”

Section: Msdf Systemsmentioning

confidence: 99%

“…Perform track fusion optimally for a multiple-sensor system with a specific processing architecture is treated in [295]. Other work cited in Table 2.11 are [338], [15,22,23,24], [16], [17], [19], [21], [20], [71], [72], [73]- [74], [75]- [76], [77], [78]- [79], [124], [126], [260], [261,262], [264,265], [266], [296], [303], [305] and [306]. [266] • Perform track fusion optimally for a multiple-sensor system with a specific processing architecture [295] • Track-to-track fusion for multi-sensor data fusion [296] • Common process noise on the two-sensor fused-track covariance [303] • Track association and track fusion with non-deterministic target dynamics [305] • Comparison of two-sensor tracking methods based on state vector fusion and measurement fusion [306] 2.9.…”

Section: Msdf Systemsmentioning

confidence: 99%

Distributed Kalman filtering

Mahmoud¹

2012

Distributed Control and Filtering for Industrial Systems

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“…UAV states estimation is usually derived from sensors fusion process with application of special algorithm. The fusion process which combines several sensors signal increases the sensors accuracy and detection while the effective algorithm capable to estimate and compensate those stated errors from sensors [6][7].…”

Section: Introductionmentioning

confidence: 99%