Multi-sources information fusion algorithm in airborne detection systems

Yan, Yang; Zhan-rong, Jing; Gao, Tian; Wang, Hui‐Long

doi:10.1016/s1004-4132(07)60070-x

Cited by 22 publications

(10 citation statements)

References 1 publication

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“…In comparison with the existing methods [1][2][3][4][5][6][7][8][9][10][11][12][13][14], the proposed method in this paper considers sufficiently the risk attitude of DM, and determines the attributes weights by min-max optimization model, while in [6] the attributes weights are artificially given, and [1][2][3][4][5][6][7][8][9][10][11][12][13][14] did not consider the risk preference of DM. In addition, the data of the characteristic value and observations of sensors are all exact real numbers in [1][2][3][4][5][6][7][8][9][10][11][12][13][14], whereas this paper deals with the triangular fuzzy number, which is the most difference between the existing literature and this paper.…”

Section: Simulation Examplementioning

confidence: 98%

“…In addition, the data of the characteristic value and observations of sensors are all exact real numbers in [1][2][3][4][5][6][7][8][9][10][11][12][13][14], whereas this paper deals with the triangular fuzzy number, which is the most difference between the existing literature and this paper. The above discussion shows the efficiency of the developed theoretic results of this paper to some extent.…”

Section: Simulation Examplementioning

confidence: 99%

“…Many different fusion methods for multi-sensor object recognition have been developed [1][2][3][4][5][6][7][8][9][10][11][12][13][14], e.g., methods based on Shafer-Dempster evidence theory [2,4], methods based on fuzzy theory [5][6][7][8][9][10], and Bayesian approach [11], etc. While these fusion methods all work well when the sensor readings may be described by real numbers, the methods based on Shafer-Dempster evidence and fuzzy theories excessively depend on the selection of basic probability assignment and the membership function.…”

Section: Introductionmentioning

confidence: 99%

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Method Based on Triangular Fuzzy Number for Multi-Sensor Object Recognition

Wan

2008

2008 3rd International Conference on Innovative Computing Information and Control

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Aimed at the type recognition problem in which the characteristic values of object types and observations of sensors are in the form of triangular fuzzy numbers, a new fusion method from the viewpoint of decision making theory is proposed. The method transforms the triangular fuzzy numbers elements of decision matrix into the expected value elements. After solving the optimization problem of minimizing the maximum deviation between the object types and the unknown object, the weights of the attributes are obtained. The result of recognition for the unknown object is given by the comprehensive attribute expected values. This method can avoid the subjectivity of selecting attributes weights. It is straightforward and can be performed on computer easily. Finally, a simulated example is given to demonstrate the feasibility and practicability of the proposed method.

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Section: Simulation Examplementioning

confidence: 98%

Section: Simulation Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Method Based on Triangular Fuzzy Number for Multi-Sensor Object Recognition

Wan

2008

2008 3rd International Conference on Innovative Computing Information and Control

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“…Many different fusion methods for multi-sensor object recognition have been developed [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15], e.g., methods based on Shafer-Dempster evidence theory [2][3][4][5], methods based on fuzzy theory [6][7][8][9][10][11], and Bayesian approach [12], etc. While these fusion methods all work well when the sensor readings may be described by real (precise) numbers, the methods based on Shafer-Dempster evidence and fuzzy theories excessively depend on the selection of basic probability assignment and the membership function.…”

Section: Introductionmentioning

confidence: 99%

Method based on interval number for multi-sensor information fusion

Wan

2008

2008 IEEE International Conference on Automation and Logistics

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These Aimed at the object recognition problem in which the characteristic values of object types and observations of sensors are in the form of interval numbers, a new fusion method for multi-sensor data is proposed from the viewpoint of decision making theory. The method defines the distance between the two interval numbers, obtains the distance matrix between all object types and unknown object. After solving the optimization problem of minimizing the distance between the object types and the unknown object, the weights of the attributes are obtained. Thus, the result of recognition for the unknown object is given by the overall distance. This method can avoid the subjectivity of selecting attributes weights. It is straightforward and can be performed on computer easily. Finally, a simulated example is given to demonstrate the feasibility and practicability of the proposed method.

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“…There already have been many authors proposed target recognition methods, which are also well known in multiattribute decision field. For example, Dempster-Shafer evidence theory method [3][4][5][6], fuzzy-Bayesian approach [7], vague set method [8][9][10], variable fuzzy set method [11], extension method [12], VIKOR method [13] and entropy weights method [14].…”

Section: Introductionmentioning

confidence: 99%