Target Recognition via Information Aggregation Through Dempster–Shafer's Evidence Theory

2015 ◽  
Vol 12 (6) ◽  
pp. 1247-1251 ◽  
Author(s):  
Ganggang Dong ◽  
Gangyao Kuang
Keyword(s):  
Target Recognition ◽  
Evidence Theory ◽  
Information Aggregation

Determination of Basic Probability Assignment Based on Recognition Sequence

2014 ◽  
Vol 1049-1050 ◽  
pp. 1171-1175
Author(s):  
Yan Fei Chen ◽  
Xue Zhi Xia ◽  
Kui Tu
Keyword(s):  
Target Recognition ◽  
Evidence Theory ◽  
Practical Application ◽  
Recognition Sequence ◽  
Probability Assignment ◽  
Basic Probability Assignment ◽  
Recognition Result ◽  
Basic Probability ◽  
Simulation Results

In some practical application of target recognition with sensors, the sensors will give the recognition sequence of targets, which is more detailed than the single recognition result. How to properly construct the basic probability assignment by the recognition sequence becomes the key to successful application of evidence theory. For the recognition sequence of the target recognition results of general sensor is incomplete, and the importance of the types in the recognition sequence is in descending order, this paper proposes a method to construct weights of recognition sequence, the basic probability assignments constructed by the weights are closer to the real recognition results. Simulation results show that this method is more reasonable and effective than the method of contrast.

scholarly journals An Evidential Fractal Analytic Hierarchy Process Target Recognition Method

2018 ◽  
Vol 68 (4) ◽  
pp. 367 ◽  
Author(s):  
Yuzhen Han ◽  
Yong Deng
Keyword(s):  
Analytic Hierarchy Process ◽  
Target Recognition ◽  
Fractal Theory ◽  
Evidence Theory ◽  
Belief Function ◽  
Uncertain Information ◽  
Fusion Algorithm ◽  
Analytic Hierarchy ◽  
Sensory Data ◽  
Hierarchy Process

<p>Target recognition in uncertain environments is a hot issue, especially in extremely uncertain situation where both the target attribution and the sensor report are not clearly represented. To address this issue, a model which combines fractal theory, Dempster-Shafer evidence theory and analytic hierarchy process (AHP) to classify objects with incomplete information is proposed. The basic probability assignment (BPA), or belief function, can be modelled by conductivity function. The weight of each BPA is determined by AHP. Finally, the collected data are discounted with the weights. The feasibility and validness of proposed model is verified by an evidential classifier case in which sensory data are incomplete and collected from multiple level of granularity. The proposed fusion algorithm takes the advantage of not only efficient modelling of uncertain information, but also efficient combination of uncertain information.</p>

scholarly journals An improved method to determine basic probability assignment based on fuzzy mathematics

2022 ◽  
Vol 355 ◽  
pp. 02002
Author(s):  
Leihui Xiong ◽  
Xiaoyan Su
Keyword(s):  
Target Recognition ◽  
Evidence Theory ◽  
Intersection Point ◽  
Fuzzy Mathematics ◽  
Improved Method ◽  
Probability Assignment ◽  
Basic Probability Assignment ◽  
Robot Hands ◽  
Basic Probability

In D-S evidence theory, the determination of the basic probability assignment function (BPA) is the first and important step. However, the generation of BPA is still a problem to be solved. Based on the concepts in fuzzy mathematics, this paper proposes an improved BPA generation method. By introducing the value of the intersection point of membership function of different targets under the same index to describe the overlap degree of targets, the assignment of unknown items is optimized on this basis. This article applies it to target recognition of robot hands. The results show that the proposed method is more reliable and more accurate.

COMBINATION METHODS AND CONFLICT HANDLING IN EVIDENTIAL THEORIES

2008 ◽  
Vol 16 (03) ◽  
pp. 337-369 ◽  
Author(s):  
S. EMAD MARASHI ◽  
JOHN P. DAVIS ◽  
JIM W. HALL
Keyword(s):  
Imperfect Information ◽  
Evidence Theory ◽  
Information Aggregation ◽  
Combination Rules ◽  
Stochastic Independence ◽  
Combination Methods ◽  
Important Approach ◽  
Classical Models ◽  
Conflict Handling ◽  
Dominant Characteristic

Evidence theory has been acknowledged as an important approach to dealing with uncertain, incomplete and imperfect information. In this framework, different formal techniques have been developed in order to address information aggregation and conflict handling. The variety of proposed models clearly demonstrates the range of possible underlying assumptions in combination rules. In this paper we present a review of some of the most important methods of combination and conflict handling in order to introduce a more generic rule for aggregation of uncertain evidence. We claim that the models based on mass multiplication can address the problem domains where randomness and stochastic independence is the dominant characteristic of information sources, although these assumptions are not always adhered to many practical cases. The proposed combination rule here is not only capable of retrieving other classical models, but also enables us to define new families of aggregation rules with more flexibility on dependency and normalization assumptions.

scholarly journals The Improvement of DS Evidence Theory and Its Application in IR/MMW Target Recognition

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Yibing Li ◽  
Jie Chen ◽  
Fang Ye ◽  
Dandan Liu
Keyword(s):  
Target Recognition ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Evidence Theory ◽  
Single Mode ◽  
Recognition System ◽  
Battlefield Environment ◽  
Compound Decision ◽  
The Military ◽  
Decision Making Rule

ATR system has a broad application prospect in the military field, especially in the field of modern defense technology. When paradoxes are in existence in ATR system due to adverse battlefield environment, integration cannot be effectively and reliably carried out only by traditional DS evidence theory. In this paper, a modified DS evidence theory is presented and applied in IR/MMW target recognition system. The improvement of DS evidence theory is realized by three parts: the introduction of sensor priority and evidence credibility to realize the discount processing of evidences, the modification of DS combination rule to enhance the accuracy of synthesis results, and the compound decision-making rule. The application of the modified algorithm in IR/MMW system is designed to deal with paradoxes, improve the target recognition rate, and ensure the reliability of target recognition system. Experiments are given to illustrate that the introduction of the modified DS evidence theory in IR/MMW system is better able to realize satisfactory target recognition performance through multisensor information fusion than any single-mode system.

scholarly journals A New Conflict Management in Evidence Theory Based on DEMATEL Method

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Wentao Fan ◽  
Fuyuan Xiao
Keyword(s):  
Decision Making ◽  
Data Fusion ◽  
Conflict Management ◽  
Correlation Coefficient ◽  
Averaging Method ◽  
Target Recognition ◽  
Evidence Theory ◽  
New Method ◽  
Weighted Averaging ◽  
Combination Rule

D-S evidence theory is widely used in data fusion. However, the result of Dempster’s combination rule is not efficient and in highly conflicting situation. Though the existing methods have been proved efficient to deal with conflict in some applications, the indirect conflict among evidence is neglected to some degree. To solve this problem, a new method is proposed based on decision-making trial and evaluation laboratory (DEMATEL) and the belief correlation coefficient in this paper. The application in target recognition illustrates the efficiency of the proposed method. Compared with Dempster’s rule, averaging method and weighted averaging method, etc., the results obtained by the proposed method have better performance. The main reason is that the indirect conflict is well addressed in the proposed method.

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