基于加权信度熵的传感器数据冲突度量与融合

周颖; 唐永川; 赵晓哲

doi:doi:10.3969/j.issn.1671-637x.2018.06.011

电光与控制, 2018, 25 (6): 48, 网络出版: 2021-01-20

基于加权信度熵的传感器数据冲突度量与融合

Weighted Belief Entropy Based Conflict Measure and Fusion of Sensor Data

论文大纲

周颖唐永川赵晓哲

作者单位

西北工业大学电子信息学院西安710072

传感器数据融合目标识别 D-S证据理论信息熵 Deng熵 sensor data fusion target recognition Dempster-Shafer evidence theory belief entropy

摘要

目标识别中，来自多传感器的数据通常包含诸多不确定性。在D-S证据理论框架下，提出一种基于加权信度熵的传感器冲突数据融合与目标识别方法。首先，将辨识框架（FOD）中包含的不确定信息融入新近提出的Deng熵模型；随后，采用加权Deng熵量化不同传感器数据源中的不确定性；最后，实现传感器冲突数据融合与目标识别决策。数值仿真及不同方法的比较分析结果验证了所提方法的合理性与有效性。

Abstract

In target recognition, the sensor data is full of uncertainty.This paper proposes a sensor data fusion approach for target recognition based on weighted belief entropy in Dempster-Shafer evidence theory framework. Firstly, the uncertain information in the Frame of Discernment (FOD) is integrated into the Deng entropy model . Then, the weighted Deng entropy is applied to measure the uncertainty of the sensor data from different sources. Finally, the fusion of the conflicting data is implemented, based on which the decision-making on target recognition is realized. The rationality and effectiveness of the proposed method are validated by numerical simulations as well as the comparative experiments.

参考文献

[1] 张堃,周德云,王谦,等.基于模糊决策树的机载多传感器智能管理方法［J］.计算机应用,2011, 31(12):3255-3257.

[2] 王越,周德云,杨维,等.多种异类传感器联合跟踪的异步航迹融合算法［J］.弹箭与制导学报,2014, 34(2):153-156.

[3] 柳毅,高晓光,卢广山,等.基于加权证据组合的多传感器目标识别［J］.系统工程与电子技术,2003, 25(12):1475-1477.

[4] 宋建辉,张俊,刘砚菊,等.基于LBP-PCA的多传感器目标识别算法［J］.火力与指挥控制,2017, 42(2):60-63.

[5] DEMPSTER A P.Upper and lower probabilities induced by a multivalued mapping［J］.Annals of Mathematical Statistics, 1967, 38(2):325-339.

[6] SHAFER G.A mathematical theory of evidence［M］.Princeton：Princeton University Press, 1976.

[7] YANG Y, HAN D. A new distance-based total uncertainty measure in the theory of belief functions［J］.Knowledge-Based Systems, 2016, 94:114-123.

[8] SONG Y, WANG X, LEI L, et al.Uncertainty measure for interval-valued belief structures［J］.Measurement, 2016, 80:241-250.

[9] DENG Y.Deng entropy［J］.Chaos Solitons & Fractals, 2016, 91:549-553.

[10] DENG Y, SHI W, ZHU Z, et al.Combining belief functions based on distance of evidence［J］.Decision Support Systems200438(3):489-493.

[11] MURPHY C K.Combining belief functions when evidence conflicts［J］.Decision Support Systems, 2000, 29(1):1-9.

周颖, 唐永川, 赵晓哲. 基于加权信度熵的传感器数据冲突度量与融合[J]. 电光与控制, 2018, 25(6): 48. ZHOU yin, TANG Yongchuan, ZHAO Xiaoze. Weighted Belief Entropy Based Conflict Measure and Fusion of Sensor Data[J]. Electronics Optics & Control, 2018, 25(6): 48.

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