A Consensus Data Fusion Algorithm for Dynamic Multi-sensors
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摘要: 针对现有一致性融合算法在处理时变系统的状态估计时, 不能准确度量传感器的一致性和可靠性, 且传感器一致性均值和可靠性度量存在“数据饱和”和“历史信息浪费”等问题, 将一致性均值和方差的计算转化为时变参数估计问题, 引入一致性衰减因子和方差衰减因子, 更为客观地度量传感器的一致性和可靠性, 实现传感器融合权重的动态调整, 从而将一致性融合算法推广应用到时变系统。仿真结果表明, 该方法可更为合理地分配各传感器的融合权重, 改善一致性融合算法的性能。Abstract: Existing consensus fusion algorithms can not accurately measure the consensus and reliability of sensor in state estimation of time-varying system, and ‘data saturation’ and ‘waste of history information’ exist in measurement of sensor′s consensus mean value and reliability. In this paper, the calculation of consensus mean value and variance is transformed into the estimation of time-varying parameters, the consensus attenuation factors and variance attenuation factors are employed to measure the consensus and reliability of sensor more objectively, and the dynamic adjustment of sensor fusion weight is realized. Simulation results show that the present approach can reasonably distribute the fusion weights of sensors to optimize the fusion algorithm.
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Key words:
- data fusion /
- consensus /
- reliability /
- attenuation factor
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[1] 王婷杰, 施惠昌.一种基于模糊理论的一致性数据融合方法[J].传感器技术, 1999, 18(6): 50-53.Wang Ting-Jie, Shi Hui-Chang. Consensus Data Fusion Me- thod Based on Fuzzy Theory[J]. Sensor Technology, 1999, 18(6): 50-53. [2] 段战胜, 韩崇昭, 陶唐飞. 基于最近统计距离的多传感器一致性数据融合[J]. 仪器仪表学报, 2005, 26(5): 478-481.Duan Zhan-sheng, Han Chong-zhao, Tao Tang-fei. Consis-tent Multi-sensor Data Fusion Based on Nearest Statistical Distance[J]. Chinese Journal of Scientific Instrument, 2005, 26(5): 478-481. [3] 危遂薏, 刘桂雄.一种同质的多传感器融合新算法[J].传感器技术, 2004, 23(8): 61-62.Wei Sui-Yi, Liu Gui-xiong. Novel Arithmetic of Similar Multi-sensor Data Fusion[J]. Journal of Transducer Tech-nology, 2004, 23(8): 61-62. [4] Luo R C. Multi-sensor Fusion and Integration: Approaches, Applications, and Future Research Directions[J].IEEE Sen- sors Journal, 2002, 2(2): 107-111. [5] 蒲书缙.复杂环境下目标识别的智能数据融合技术研究[D].哈尔滨: 哈尔滨工程大学, 2006. [6] 刁联旺, 王常武, 商建云, 等.多传感器一致性数据融合方法的改进与推广[J].系统工程与电子技术, 2002, 24(9): 60-63.Diao Lian-wang, Wang Chang-wu, Shang Jian-yun, et al. Im-proved and Generalized Consensus Data Fusion Method [J]. Systems Engineering and Electronics, 2002, 24(9): 60- 63. [7] 张鹏, 张建业, 王占磊, 等.基于邻近量测认知信息的多传感器融合估计[J]. 计算机工程, 2012, 38(8): 1-3.Zhang Peng, Zhang Jian-ye, Wang Zhan-lei, et al. Multi- sen- sor Fusion Estimation Based on Adjacent Measurement Cogni-tive Information[J]. Computer Engineering, 2012, 38 (8): 1-3. [8] 杨宝强, 孙勇, 徐明.基于支持度的多传感器信息融合算法[J]. 空军工程大学学报(自然科学版), 2007, 8(2): 33- 35. [9] 刘敏华, 萧德云. 基于相似度的多传感器数据融合[J].控制与决策, 2004, 19(5): 534-537.Liu Min-hua, Xiao De-yun. Multi-sensor Data Fusion Based on Similitude Degree[J]. Control and Decision, 2004, 19(5): 534- 537. [10] 程建兴, 史仪凯.动态加权的一致性多传感器数据融合算法[J].火力与指挥控制, 2008, 33(8): 75-78.Cheng Jian-xing, Shi Yi-kai. A Novel Consensus Multi-sen-sor Data Fusion Algorithm Based on Dynamic Weighted[J]. Fire Control and Command Control, 2008, 33(8): 75-78. [11] 王昕, 张合.一致性多传感器数据融合技术在引信信息融合过程中的应用[J]. 兵工学报, 2004, 23(8): 61-62. [12] 胡振涛, 刘先省. 一种改进的一致性数据融合算法[J]. 传感器技术, 2005, 24(8): 65-67.Hu Zhen-Tao, Liu Xian-xing. Improved Consensus Data Fusion Algorithm[J]. Journal of Transducer Technology, 2005, 24(8): 65-67. [13] 王华, 邓军, 王连华, 等.改进的一致性数据融合算法及其应用[J]. 中国矿业大学学报, 2009, 38(4): 590-594.Wang Hua, Deng Jun, Wang Lian-hua, et al. Improved Con-sensus Data Fusion Algorithm and Its Application[J]. Jour-nal of China University of Mining & Technology, 2009, 38(4): 590-594. [14] Benediktsson J A, Swain P H. Consensus Theoretic Classi-fication Methods[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1992(22): 688-704. [15] Benediktsson J A, Sveinsson J R, Swain P H.Hybrid Con-sensus Theoretic Classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 1997(35): 833-843.
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