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由于传感器网络的自身特征和节点的资源受限，使得对观测信号的处理必须考虑量化和能耗等因素，而引入的量化噪声同时增加了系统整体噪声的复杂性.针对传感器网络中整体噪声统计特性难以准确数学建模的特点，提出了一种基于代价参考粒子滤波的混沌信号重构算法.算法采用容积点变换以获得相对准确的更新粒子，并将局部重构信号的代价增量构建为广义似然比函数，用来选择传感器网络中的有效工作节点.仿真结果表明：所提算法可实现混沌信号的有效重构，且在噪声统计特性未知时性能要优于容积卡尔曼粒子滤波算法；算法同时能够通过选择不同的广义似然比阈值，实现网络能耗和重构精度的折中.Blind signal reconstruction in sensor arrays is usually a highly nonlinear and non-Gaussian problem, and nonlinear filtering is an effective way to realize state estimation from available observations. Developing the processing problem of blind signal in wireless sensor networks (WSNs) will greatly extend the application scope. Meanwhile, it also meets great challenges such as energy and bandwidth constrained. For solving the constrained problem in WSNs, the observed signals must be quantified before sending to the fusion center, which makes the overall noise unable to be modeled accurately by simple probabilistic model. To study the reconstruction issue of chaotic signal with unknown statistics in WSNs, a reconstructed method of chaotic signal based on a cost reference particle filter (CRPF) is proposed in this paper. The cost recerence cubature particle filter (CRCPF) algorithm adopts cubature-point transformation to enhance the accuracy of prediction particles, and cost-risk functions are defined to complete particle propagation. The effectiveness of proposed CRCPF algorithm is verified in the sensor network with a fusion center. Moreover, a generalized likelihood ratio functionis obtained by the cost increment of local reconstructed signals in the cluster-based sensor network topology model, which is used to reduce the network energy consumption by selecting working nodes. Simulation results show that compared with CPF and CRPF, the proposed algorithm CRCPF attains good performance in a WSN with unknown noise statistics. Meanwhile, the CRCPF algorithm realizes the compromise between energy consumption and reconstruction accuracy simultaneously, which indicates that the proposed CRCPF algorithm has the potential to extend other application scope.
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Keywords:
- cost reference /
- chaotic signal reconstruction /
- energy consumption /
- generalized likelihood ratio
[1] Rawat P, Singh K D, Chaouchi H, Bonnin J M 2014 J. Supercomput. 68 1
[2] Qi H, Wang F B, Deng H 2013 Acta Phys. Sin. 62 270 (in Chinese)[祁浩, 王福豹, 邓宏2013 62 270]
[3] Sun B, Ahmed F, Sun F, Qian Q, Xiao Y 2016 Int. J. Sensor Networks 20 26
[4] Galka A, Wong K K F, Stephani U, Ozaki T 2013 Int. J. Bifurcat. Chaos 23 1350165
[5] Hao X C, Liu W J, Xin M J, Yao N, Ru X Y 2015 Acta Phys. Sin. 64 080101 (in Chinese)[郝晓辰, 刘伟静, 辛敏洁, 姚宁, 汝小月 2015 64 080101]
[6] Chen H B, Tse C K, Feng J C 2008 IEEE Trans. Circ. Syst. Ⅱ:Express Briefs 55 947
[7] Wang S Y, Feng J C 2008 J. Electron. Inform. Technol. 30 89 (in Chinese)[王世元, 冯久超 2008电子与信息学报 30 89]
[8] Hu Z H, Feng J C 2010 J. Southwest Univ. (Natural Science Edition) 32 146 (in Chinese)[胡志辉, 冯久超 2010 西南大学学报(自然科学版) 32 146]
[9] Wang S Y, Feng J C 2012 Acta Phys. Sin. 61 170508 (in Chinese)[王世元, 冯久超 2012 61 170508]
[10] Naqvi S M, Yu M, Chambers J A 2010 IEEE J. Select. Topics in Signal Process. 4 895
[11] Chen H B, Feng J C 2010 J. Southwest Univ. (Natural Science Edition) 32 124 (in Chinese)[陈宏滨, 冯久超 2010 西南大学学报(自然科学版) 32 124]
[12] Huang J W, Feng J C 2014 Chin. Phys. B 23 070504
[13] Chen H B, Tse C K, Feng J C 2009 IEEE Trans. Parall. Distrib. Syst. 20 886
[14] Míguez J, Bugallo M F, Djurić P M 2004 EURASIP J. Adv. Signal Process. 2004 2278
[15] Míguez J 2007 Signal Process. 87 3155
[16] Míguez J 2007 Digit. Signal Process. 17 787
[17] Lu J, Shui P L, Su H T 2014 IET Signal Process. 8 85
[18] Hu Z T, Pan Q, Yang F, Cheng Y M 2009 Systems Engineer. Electron. 31 3022 (in Chinese)[胡振涛, 潘泉, 杨峰, 程咏梅 2009 系统工程与电子技术 31 3022]
[19] Shui P L, Shi S N, Lu J, Jiang X W 2016 Digit. Signal Process. 48 104
[20] Lu J, Su H T, Shui P L, Zhou Z G 2013 J. Xi'an Jiaotong Univ. 47 93 (in Chinese)[卢锦, 苏洪涛, 水鹏朗, 周忠根 2013西安交通大学学报 47 93]
[21] Arasaratnam I, Haykin S 2009 IEEE Trans. Autom. Control 54 1254
[22] Heinzelman W B, Chandrakasan A P, Balakrishnan H 2002 IEEE Trans. Wireless Commun. 1 660
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[1] Rawat P, Singh K D, Chaouchi H, Bonnin J M 2014 J. Supercomput. 68 1
[2] Qi H, Wang F B, Deng H 2013 Acta Phys. Sin. 62 270 (in Chinese)[祁浩, 王福豹, 邓宏2013 62 270]
[3] Sun B, Ahmed F, Sun F, Qian Q, Xiao Y 2016 Int. J. Sensor Networks 20 26
[4] Galka A, Wong K K F, Stephani U, Ozaki T 2013 Int. J. Bifurcat. Chaos 23 1350165
[5] Hao X C, Liu W J, Xin M J, Yao N, Ru X Y 2015 Acta Phys. Sin. 64 080101 (in Chinese)[郝晓辰, 刘伟静, 辛敏洁, 姚宁, 汝小月 2015 64 080101]
[6] Chen H B, Tse C K, Feng J C 2008 IEEE Trans. Circ. Syst. Ⅱ:Express Briefs 55 947
[7] Wang S Y, Feng J C 2008 J. Electron. Inform. Technol. 30 89 (in Chinese)[王世元, 冯久超 2008电子与信息学报 30 89]
[8] Hu Z H, Feng J C 2010 J. Southwest Univ. (Natural Science Edition) 32 146 (in Chinese)[胡志辉, 冯久超 2010 西南大学学报(自然科学版) 32 146]
[9] Wang S Y, Feng J C 2012 Acta Phys. Sin. 61 170508 (in Chinese)[王世元, 冯久超 2012 61 170508]
[10] Naqvi S M, Yu M, Chambers J A 2010 IEEE J. Select. Topics in Signal Process. 4 895
[11] Chen H B, Feng J C 2010 J. Southwest Univ. (Natural Science Edition) 32 124 (in Chinese)[陈宏滨, 冯久超 2010 西南大学学报(自然科学版) 32 124]
[12] Huang J W, Feng J C 2014 Chin. Phys. B 23 070504
[13] Chen H B, Tse C K, Feng J C 2009 IEEE Trans. Parall. Distrib. Syst. 20 886
[14] Míguez J, Bugallo M F, Djurić P M 2004 EURASIP J. Adv. Signal Process. 2004 2278
[15] Míguez J 2007 Signal Process. 87 3155
[16] Míguez J 2007 Digit. Signal Process. 17 787
[17] Lu J, Shui P L, Su H T 2014 IET Signal Process. 8 85
[18] Hu Z T, Pan Q, Yang F, Cheng Y M 2009 Systems Engineer. Electron. 31 3022 (in Chinese)[胡振涛, 潘泉, 杨峰, 程咏梅 2009 系统工程与电子技术 31 3022]
[19] Shui P L, Shi S N, Lu J, Jiang X W 2016 Digit. Signal Process. 48 104
[20] Lu J, Su H T, Shui P L, Zhou Z G 2013 J. Xi'an Jiaotong Univ. 47 93 (in Chinese)[卢锦, 苏洪涛, 水鹏朗, 周忠根 2013西安交通大学学报 47 93]
[21] Arasaratnam I, Haykin S 2009 IEEE Trans. Autom. Control 54 1254
[22] Heinzelman W B, Chandrakasan A P, Balakrishnan H 2002 IEEE Trans. Wireless Commun. 1 660
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