传统多传感器环境下的目标识别方法主要有两种：利用多传感器获得的数据进行数据融合、利用每个传感器信号的特征向量进行特征融合。但这两种方法均存在目标识别精度不高的问题。针对这一问题，本文提出了一种基于D-S 证据理论两次组合规则的融合方法。该方法在提出多传感器目标识别系统模型的基础上，运用D-S 证据理论对单传感器的多特征信息进行数据融合；根据传感器接收信号信噪比来确定传感器可信度，将该可信度作为D-S 证据理论组合规则中的证据权值，以此来完成目标识别。本文提出的方法综合考虑了传感器的多特征信息和传感器的可信度，克服了传统的D-S 证据理论对证据冲突处理能力有限的缺陷。实验结果表明，该方法具有较高的正确性和有效性，提高了目标识别的精度。
The traditional target recognition based on multi-sensor environment has two methods. On the one hand, the data obtained from the multi-sensor are used for data fusion, on the other hand the signal obtained from each sensor is used for feature fusion. The two methods both have the problem that the target recognition accuracy is not high. In order to solve this problem, this paper presents a fusion method based on D-S evidence theory. On the basis of a multi-sensor target recognition system model, D-S evidence theory is used for data fusion based on the multi-feature information of a single sensor. According to the sensor signal-noise ratios of the received signal the credibility is determined, which is taken as the weight of evidence of the weighted combination of D-S evidence theory rule to complete the target recognition. This method considers many characteristics information of the sensor and the reliability of the sensor, overcomes the defect that evidence conflict management ability is limited by the traditional D-S evidence theory. Experimental results show the correctness and validity of this method, as well as the improved accuracy of target recognition.
[1] 万树平. 多传感器目标识别的改进灰关联度法[J]. 计算机工程与应用, 2009, 45(24): 25-27. Wan Shuping. Method of improved grey relational degree for multi-sensor target recognition[J]. Computer Engineering and Applications, 2009, 45 (24): 25-27.
[2] Algridas A, Laprie J C, Randell B, et al. Basic concepts and taxonomy of dependable and Secure computing[J]. IEEE Transactions on Dependable and Secure Computing, 2004, 1(1): 11-33.
[3] 李琼, 杨晓祥. 多传感器数据融合模型的研究[J]. 计算机与现代化, 2013(4): 158-161. Li Qiong, Yang Xiaoxiang. Research on multi-sensor data fusion model[J]. Computer and Modernization, 2013(4): 158-161.
[4] 王宁, 郭立, 金大胜, 等. 遗传算法在多传感器多目标静态数据关联中 的应用[J]. 数据采集与处理, 1999, 60(1): 18-21. Wang Ning, Guo Li, Jin Dasheng, et al. Application of genetic algorithm in multi sensor multi target static data association[J]. Journal of Data Acquisition and Processing, 1999, 60(1): 18-21.
[5] 王俊林, 张剑云. 基于统计证据的Mass函数和D-S证据理论的多传感 器目标识别[J]. 传感技术学报, 2006, 212(3): 862-864. Wang Junlin, Zhang Jianyun. Multisensor target identification based on mass function of statistical evidence and D-S evidence theory[J]. Chinese Journal of Sensors and Actuators, 2006, 212(3): 862-864.
[6] 孙勇, 景博, 张吉力. 最优加权与递推最小二乘法相结合的多传感器 信息融合[J]. 传感技术学报, 2004, 156(4): 630-634. Sun Yong, Jing Bo, Zhang Jili. Multi-sensor information fusion based on the combination of optimal weight and recursive least square algorithm[J]. Journal of Transcluction Technology, 2004, 156(4): 630-634.
[7] Bogler, Shafer-Dempster R L. Reasoning with application to multi-sensor target identification system[J]. IEEE Transactions on Systems, Man and Cybernetics, 2003, 202(4): 133-136.
[8] Arinivas M S, Andrew H S. Identifying significant features for network forensic analysis using artificial intelligent techniques[J]. International Journal of Digital Evidence, 2003, 189(6): 63-69.
[9] 刘源, 谢维信, 杜文吉, 等. 基于多传感器多目标特征信息的模糊数据 关联算法[J]. 系统工程与电子技术, 1998, 128(12): 18-23. Liu Yuan, Xie Weixin, Du Wenji, et al. Fuzzy data association algorithm based on multisensor and features of targets[J]. Systems Engineering and Electronics, 1998, 128(12): 18-23.
[10] 刘付显, 邢清华. 基于D-S融合证据的决策新方法[J]. 系统工程理论 与实践, 2009, 29(7): 125-131. Liu Fuxian, Xing Qinghua. New decision making method based on D-S fusion evidence[J]. Systems Engineering-Theory & Practice, 2009, 29(7): 125-131.
[11] 李特, 冯琦, 张堃. 基于熵权灰色关联和D-S证据理论的威胁评估[J]. 计算机应用研究, 2013, 1(2): 380-382. Li Te, Feng Qi, Zhang Kun. Threat assessment based on entropy weight grey incidence and D-S theory of evidence[J]. Application Research of Computers, 2013, 1(2): 380-382.
[12] Zhu J M, Yang S B. A grid&p2p trust model based on recommendation evidence reasoning[J]. Journal of Computer Research and Development, 2005, 42(5): 797-803.
[13] Hu R, Liu J X, Liu X F. A trustworthiness fusion model for service cloud platform based on D-S evidence theory[C]//11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, Newport Beach, 2011: 566-571.
[14] 袁禄来, 曾国荪, 王伟. 基于Dempster-Shafer证据理论的信任评估模 型[J]. 武汉大学学报, 2006, 52(5): 627-630. Yuan Lulai, Zeng Guosun, Wang Wei. Trust evaluation model based on Dempster-Shafer evidence theory[J]. Journal of Wuhan University, 2006, 52(5): 627-630.
[15] Wang Yingming, Yang Jianbo. The evidential reasoning approach for multiple attribute decision analysis using interval belief degrees[J]. European Journal of Operational Research, 2006, 175(1): 35-66.
[16] Abdu-l Rahman A, Hailes S. Supporting trust in virtual communit ies[C]//Proceedings of the 33rd Hawaii International Conference on System Sciences. Washington, DC: IEEE Computer Society Press, 2000: 6007-6016.
