考虑决策者时序偏好的时域证据融合方法
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摘要
针对时域不确定信息的融合难题,为充分体现时域信息融合的动态性特点和时间因素对融合的影响,在证据理论的基础上,提出一种考虑决策者时序偏好的时域证据融合方法。首先将决策者对时序的偏好融入时域证据融合,通过分析时域证据序列的特点,在定义时序记忆因子的基础上,对决策者的时序偏好进行度量;然后通过构建优化模型求解时序权重,再结合证据信任度的概念,对证据源进行修正;最后利用Dempster组合规则对修正后的证据进行融合。数值算例表明,与没有考虑时间因素的融合方法相比,考虑决策者时序偏好的证据融合方法可以有效处理时域信息序列中的冲突信息,得到合理的融合结果;同时,所提方法充分考虑了时域证据序列的信任度和决策者的主观偏好,可以反映决策者主观因素对时域证据融合的影响,较好地体现了时域证据融合的动态性特点。
Aiming at temporal uncertain information fusion problem, to fully reflect the dynamic characteristic and the influence of time factor on temporal information fusion, a temporal evidence fusion method was proposed with considering decision maker's preference for time sequence based on evidence theory. Firstly, time sequence preference of decision maker was fused to temporal evidence fusion, through the analysis of characteristics of temporal evidence sequence, decision maker's preference for time sequence was measured based on the definition of temporal memory factor. Then, the evidence source was revised by time sequence weight vector obtained by constructing the optimal model and evidence credibility idea. Finally, the revised evidences were fused by Dempster combination rule. Numerical examples show that compared with other fusion methods without considering time factor, the proposed method can deal with conflicting information in temporal information sequence effectively and obtain a reasonable fusion effect; meanwhile, with the consideration of the credibility of temporal evidence sequence and the subjective preference of decision maker, the proposed method can reflect the influence of subjective factors of decision maker on temporal evidence fusion, giving a good expression to the dynamic characteristic of temporal evidence fusion.
Aiming at temporal uncertain information fusion problem, to fully reflect the dynamic characteristic and the influence of time factor on temporal information fusion, a temporal evidence fusion method was proposed with considering decision maker's preference for time sequence based on evidence theory. Firstly, time sequence preference of decision maker was fused to temporal evidence fusion, through the analysis of characteristics of temporal evidence sequence, decision maker's preference for time sequence was measured based on the definition of temporal memory factor. Then, the evidence source was revised by time sequence weight vector obtained by constructing the optimal model and evidence credibility idea. Finally, the revised evidences were fused by Dempster combination rule. Numerical examples show that compared with other fusion methods without considering time factor, the proposed method can deal with conflicting information in temporal information sequence effectively and obtain a reasonable fusion effect; meanwhile, with the consideration of the credibility of temporal evidence sequence and the subjective preference of decision maker, the proposed method can reflect the influence of subjective factors of decision maker on temporal evidence fusion, giving a good expression to the dynamic characteristic of temporal evidence fusion.
引文
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[14]刘永祥,朱玉鹏,黎湘,等.导弹防御系统中的目标综合识别模型[J].电子与信息学报,2006,28(4):638-642.(LIU Y X,ZHU Y P,LI X,et al.Integrated target discrimination model in missile defense system[J].Journal of Electronics&Information Technology,2006,28(4):638-642.)
[15]吴俊,程咏梅,曲圣杰,等.基于三级信息融合结构的多平台多雷达目标识别算法[J].西北工业大学学报,2012,30(3):367-372.(WU J,CHENG Y M,QU S J,et al.An effective multiplatform multi-radar target identification algorithm based on three level fusion hierarchical structure[J].Journal of Northwestern Polytechnical University,2012,30(3):367-372.)
[16]FAN C L,SONG Y F,LEI L,et al.Evidence reasoning for temporal uncertain information based on relative reliability evaluation[J].Expert Systems With Applications,2018,113:264-276.
[17]SMETS P.The combination of evidence in transferable belief model[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1990,12(5):447-458.
[18]宋亚飞,王晓丹,雷蕾,等.基于相关系数的证据冲突度量方法[J].通信学报,2014,35(5):95-100.(SONG Y F,WANG XD,LEI L,et al.Measurement of evidence conflict based on correlation coefficient[J].Journal on Communications,2014,35(5):95-100.)
[19]JOUSSELME A L,GRENIER D,BOSSE E.A new distance between two bodies of evidence[J].Information Fusion,2001,2(2):91-101.
[20]MURPHY C K.Combining belief functions when evidence conflicts[J].Decision Support Systems,2000,29(1):1-9.
[21]DENG Y,SHI W K,ZHU Z F,et al.Combining belief functions based on distance of evidence[J].Decision Support Systems,2004,38(3):489-493.
[22]ZHANG Z J,LIU T H,CHEN D,et al.Novel algorithm for identifying and fusing conflicting data in wireless sensor networks[J].Sensors,2014,14(6):9562-9581.
[23]YUAN K J,XIAO F Y,FEI L Q,et al.Conflict management based on belief function entropy in sensor fusion[J].SpringerPlus,2016,5:638-649.
[2]DEMPSTER A P.Upper and lower probabilities induced by a multivalued mapping[J].The Annals of Mathematical Statistics,1967,38(2):325-329.
[3]SHAFER G.A Mathematical Theory of Evidence[M].Princeton:Princeton University Press,1976:19-63.
[4]SARABI-JAMAB A,ARAABI B.How to decide when the sources of evidence are unreliable:a multi-criteria discounting approach in the Dempster-Shafer theory[J].Information Sciences,2018,448/449:233-248.
[5]YANG D,JI H B,GAO Y C.A robust D-S fusion algorithm for multi-target multi-sensor with higher reliability[J].Information Fusion,2019,47:32-44.
[6]PARK T J,CHANG J H.Dempster-Shafer theory for enhanced statistical model-based voice activity detection[J].Computer Speech&Language,2018,47:47-58.
[7]DENG X Y,JIANG W,WANG Z.Zero-sum polymatrix games with link uncertainty:a Dempster-Shafer theory solution[J].Applied Mathematics and Computation,2019,340:101-112.
[8]YAGER R R.Satisfying uncertain targets using measure generalized Dempster-Shafer belief structures[J].Knowledge-Based Systems,2018,142:1-6.
[9]YAGER R R.Fuzzy relations between Dempster-Shafer belief structures[J].Knowledge-Based Systems,2016,105:60-67.
[10]LIU Y T,PAL N R,MARATHE A R,et al.Weighted fuzzy Dempster-Shafer framework for multimodal information integration[J].IEEE Transactions on Fuzzy Systems,2018,26(1):338-352.
[11]宋亚飞,王晓丹,雷蕾.基于直觉模糊集的时域证据组合方法研究[J].自动化学报,2016,42(9):1322-1338.(SONG Y F,WANG X D,LEI L.Combination of temporal evidence sources based on intuitionistic fuzzy sets[J].Acta Automatica Sinica,2016,42(9):1322-1338.)
[12]HONG L,LYNCH A.Recursive temporal-spatial information fusion with applications to target identification[J].IEEE Transactions on Aerospace and Electronic Systems,1993,29(2):435-445.
[13]洪昭艺,高勋章,黎湘.基于DS理论的混合式时空域信息融合模型[J].信号处理,2011,27(1):14-19.(HONG Z Y,GAOX Z,LI X.Research on temporal-spatial information fusion model based on DS theory[J].Signal Processing,2011,27(1):14-19.)
[14]刘永祥,朱玉鹏,黎湘,等.导弹防御系统中的目标综合识别模型[J].电子与信息学报,2006,28(4):638-642.(LIU Y X,ZHU Y P,LI X,et al.Integrated target discrimination model in missile defense system[J].Journal of Electronics&Information Technology,2006,28(4):638-642.)
[15]吴俊,程咏梅,曲圣杰,等.基于三级信息融合结构的多平台多雷达目标识别算法[J].西北工业大学学报,2012,30(3):367-372.(WU J,CHENG Y M,QU S J,et al.An effective multiplatform multi-radar target identification algorithm based on three level fusion hierarchical structure[J].Journal of Northwestern Polytechnical University,2012,30(3):367-372.)
[16]FAN C L,SONG Y F,LEI L,et al.Evidence reasoning for temporal uncertain information based on relative reliability evaluation[J].Expert Systems With Applications,2018,113:264-276.
[17]SMETS P.The combination of evidence in transferable belief model[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1990,12(5):447-458.
[18]宋亚飞,王晓丹,雷蕾,等.基于相关系数的证据冲突度量方法[J].通信学报,2014,35(5):95-100.(SONG Y F,WANG XD,LEI L,et al.Measurement of evidence conflict based on correlation coefficient[J].Journal on Communications,2014,35(5):95-100.)
[19]JOUSSELME A L,GRENIER D,BOSSE E.A new distance between two bodies of evidence[J].Information Fusion,2001,2(2):91-101.
[20]MURPHY C K.Combining belief functions when evidence conflicts[J].Decision Support Systems,2000,29(1):1-9.
[21]DENG Y,SHI W K,ZHU Z F,et al.Combining belief functions based on distance of evidence[J].Decision Support Systems,2004,38(3):489-493.
[22]ZHANG Z J,LIU T H,CHEN D,et al.Novel algorithm for identifying and fusing conflicting data in wireless sensor networks[J].Sensors,2014,14(6):9562-9581.
[23]YUAN K J,XIAO F Y,FEI L Q,et al.Conflict management based on belief function entropy in sensor fusion[J].SpringerPlus,2016,5:638-649.