摘要
无线传感器网络集成了微机电技术、传感器技术、无线通信技术和分布式信息处理技术,已成为当前研究的热点之一。目标定位跟踪作为无线传感器网络的典型应用之一,可广泛地应用于国防建设、环境监测、智能交通、医疗卫生和工业自动化等众多领域,特别是在生化危险环境的探测、特殊地域或特殊工作环境的监测以及军事侦察与跟踪等方面。
基于无线传感器网络的目标跟踪系统具有稳健性强和跟踪精度高等优势,但同时也受到严格的能量与带宽约束。因此,在保证跟踪精度条件下如何减少对通信能量和通信带宽的需求,或者在满足能量和带宽约束的条件下如何有效提高跟踪精度是无线传感器网络目标跟踪的关键问题。本文分别针对信息融合的不同结构形式,深入研究了无线传感器网络中基于量化信息的目标状态估计与融合问题。具体来说,主要包括如下创新性成果:
1.微分代数系统的降阶鲁棒滤波器的设计与分析。针对广义系统进行能量-峰值滤波器的设计,考虑了全阶和降阶两种情况。基于线性矩阵不等式(LMI)技术,给出了滤波器存在的充分必要条件以及滤波器增益的解析表达式。可以证明,给出的结果将常规状态空间系统的相关结论推广到了微分代数系统。然后,基于统一的信息融合模型及其最优解,给出了系统含有不确定项时的鲁棒融合估计算法,基此对鲁棒滤波器的估计性能进行对比分析。
2.分布式量化航迹融合。考虑到无线传感器网络中的通信带宽和系统能量约束,先对局部状态估计的方差阵进行压缩处理,再对压缩后的方差阵和状态估计向量进行矢量量化、传输。在融合中心层,针对局部估计的未知或者不完整相关性,提出了不依赖于局部相关性的稳健航迹融合方法—内椭球逼近法。
3.目标导向的节点动态分簇策略。考虑到基于树和基于静态分簇的目标跟踪系统存在路由代价高、冗余信息多等缺陷,提出了目标导向的动态传感器分簇策略。当传感器节点监测到目标时,它们交换监测报告,具有更高残余能量和更小平均通信距离的节点竞争成为簇首节点;其它节点加入簇首成为成员节点并对目标进行测量/状态估计。仿真结果证明相比于随机选取激活节点策略,目标导向的动态分簇策略节省可达42%的能量。
4.基于自适应量化测量的目标状态估计与融合。将原始测量信息通过自适应量化处理后传输给融合中心。融合中心根据量化的测量信息,融合各激活子传感器信息进行目标状态融合估计。重点考虑了自适应带宽分配和自适应量化阈值调整两种策略,并对基于自适应量化测量的目标状态融合估计的性能进行分析,给出了其后验克拉美-罗下界(CRLB)。
5.信道感知目标跟踪及跨层优化。无线传感器网络目标跟踪系统中考虑无线通信信道不确定性的信息融合结果目前还很少。针对数字通信的常用不确定信道模型—二元对称信道—进行信道感知的目标跟踪策略研究;并对其性能进行分析,给出了信道感知目标跟踪的后验CRLB。基此,对传感器调度问题进行跨层设计与优化,并给出了节点调度的一种启发式方法。
6.对等传感器网络中分布式协同目标跟踪。首先,针对对等自组织传感器网络,提出两种基于动态协同策略的完全分布式滤波算法:分布式鲁棒滤波器与分布式Sigma点卡尔曼滤波器。基于动态协同策略的分布式滤波算法的优势在于每个节点仅需与邻节点进行信息交换就能对状态估计融合达到网络范围的全局一致性。这使得所提出的算法是规模可扩展的,适用于大规模传感器网络。其次,提出一种新的动态协同算法,并对其收敛性和稳定性进行分析。最后,给出一种分层结构的可扩展融合框架,它对网络链接故障、节点失效以及变拓扑结构具有较强的稳健性。
Recent advances in micro-electro-mechanical systems (MEMS), sensing technology, wireless communications, and distributed signal processing have enabled the development of low-cost, low-power wireless sensor networks (WSNs). Target tracking is one of the fascinating application scenarios for WSNs, which has been studied and widely applied to national defense, environmental monitoring, intelligent transportation, and industrial automation etc.
Target tracking in a WSN has the advantages such as better robustness and higher accuracy, while each node in the network has limited energy supply and communication bandwidth. Therefore, quantized information based state estimation and fusion for target tracking in WSNs is investigated in this dissertation. Specifically, the contributions can be stated as follows.
1. Design of Reduced-Order Filters for Complex Systems
Based on a brief overview of state estimation and fusion, the energy-to-peak filtering that has attracted less attention is considered, including full-order and reduced-order ones. Based on linear matrix inequalities (LMIs) technique, both the necessary and sufficient condition and an explicit solution of the filter are given. Besides, a unified information fusion model is set up with the optimal solution given. Then, the robust estimation fusion algorithm for systems with uncertainties is presented.
2. Distributed Quantized Track Fusion
Considering the limited energy and bandwidth, local covariance matrices are compressed and vector quantized with the state estimates. Then, considering the unknown or incomplete correlation of local estimation, the fusion center (i.e. the cluster head) fuses the local quantized messages through a novel robust approach, i.e. internal ellipsoidal approximation.
3. Target-Oriented Sensor Clustering Strategy
The tree-based and static clustering-based target tracking systems have such shortages as expensive routing, high redundancy etc. Hence, a target-oriented dynamic clustering strategy is proposed: Nodes around the target are activated with monitoring reports exchanged; the node with more residue energy and less average communicational distance is competed as cluster head; other activated ones participate this cluster as member nodes and make observation on the target. Simulation results show that comparing with the randomly selection, the target-oriented scheduling strategy save energy more that 42%.
4. Adaptive Quantized Measurement based Target Tracking
The local measurements are quantized adaptively and transmitted to the fusion center; the fusion center estimates the target state according to the received messages. Attentions are focused on adaptive bandwidth allocation and adaptive quantization thresholds selection. The posterior Cramer-Rao lower bounds (CRLBs) for target tracking using adaptive quantized measurements are also given.
5. Channel-Aware Target Tracking and Cross-Layer Optimization
There is little result on target tracking in WSNs with uncertain channels. According the typical uncertain model for digital channels, binary symmetric channels (BSC), the problem of channel-aware target tracking is investigated with the posterior CRLBs derived. Based on the posterior CRLBs of channel-aware target tracking, the problem of cross-layer design and optimization for sensor scheduling is considered. Furthermore, a heuristic approach to sensor scheduling is given to surround the optimization complexity.
6. Scalable Distributed Target Tracking for Peer-to-Peer (P2P) Sensor Networks
First, based on dynamic consensus strategy, two scalable distributed filters are proposed for P2P sensor networks, one is distributed robust filtering, and the other is distributed sigma-point Kalman filtering. Both strategies have the advantage that only information exchanges between neighboring nodes are necessary, and network-wide agreement can be achieved for tareget state estimation. This makes both strategies scalable for large-scale sensor networks. Besides, a novel dynamic consensus is proposed with its convergence and stability discussed. Finally, a scalable hybrid estimation fusion framework is presented, which is robust against link failure and varying topology.
引文
[1] Akyildiz, I.F., Melodia, T., Chowdury, K.R. Wireless multimedia sensor networks: A survey. IEEE Wireless Communications. 2007, 14(6):32– 39.
[2] Yick, J., Mukherjee, B., Ghosal, D. Wireless sensor network survey. Computer Networks. 2008, 52(12):2292-2330.
[3] Schizas, I.D., Ribeiro, A., Giannakis, G.B. Dimensionality Reduction, Compression and Quantization for Distributed Estimation with Wireless Sensor Networks. Wireless Communications. 2006, 143: 259--296.
[4] Hua, Y. Urbashi M. Special section– Signal processing for wireless Ad Hoc communication networks. IEEE Signal Processing magazine. 2006, 23(5): 1135-1123.
[5] Swank, R.G. Implementation guidance for industrial-level systems using radio frequency alarm links. Westinghouse Hanford Company Technical Security Document WHC-SD-SEC-DGS-002. Springfield, VA: National Technical Information Service, 1996.
[6] Mike, H., Alan, B., Mike, G., et al. Deployment ready multimode micropower wireless sensor networks for intrusion detection, classification, and tracking. Sensors and Command, Control, Communications, and Intelligence (C3i) Technologies for Homeland Defense and Law enforcement. Edward, M. Carapezza, Ed. Proc. SPIE, 2002: 290-295.
[7] Mainwaring, A., Polastre, J., Szewczyk, R., et al. Wireless sensor networks for habitat monitoring. In: Proc. 1st ACM intl. Workshop Wireless Sensor Networks & Applications. Atlanta, USA, 2002: 143-151.
[8] Gilman, T., Joseph, P., Robert, S., et al. A macroscope in the redwooks. In: Proc. 3rd Intl. Conf. Embeeded Networked Sensor System. San Diego, USA, 2005: 51-63.
[9] Jia, X.Y., Wang, C. The Security routing research for WSN in the application of intelligent transport system. In: Proc. IEEE Intl. Conf. Mechatronics & Automation, 2006: 2318-2323.
[10] Tia, G., Greenspan, D., Welsh, M., et al. Vital signs monitoring and patient tracking over a wireless sensor network. In: Proc. 27th Annual Intl. Conf. Engineering in Medicine and Biology Society. Shanghai, China, 2005: 102-105.
[11] Bhargava, A., Zoltowski, M. Sensors and wireless communication for medical care. In: Proc. 14th Intl. Workshop on Database and Expert Systems Applications. 2003: 956-960.
[12] Shamim, N.P., Sukun, K., Gregory, L.F. et al. Multi-purpose wireless accelerometers for civil infrastructure monitoring. In: Proc. 5th Intl. Workshop on Structural Health Monitoring, Stanford, CA, 2005.
[13] Xu, N., Rangwala, S., Kant, K.C. et al. A wireless sensor network for strucutreal monitoring. In: Proc. 2nd Intl. Conf. Embedded Networked Sensor Systems. Baltimore, MD, USA, 2004: 13-24.
[14] Petriu, E.M., Georganas, N.D., Petriu, D.C., et al. Sensor-based information appliances. IEEE Instrumentation & Measurement Magazine. 2000, 3(4): 31-35.
[15] Eduardo, F., Nakamura, C., Figueiredo, M.S. et al. Information fusion Algorithm for wireless sensors networks. Handbook of Algorithms for Wireless Networking and Mobile Computing, 2006.
[16] Zhao, F., Liu, J., Liu, J., et al. Collaborative signal and information processing: an information directed approach. Proceedings of IEEE. 2003, 11(3): 1199–1209.
[17] Long, C., Zhang, Q., Li, B., Yang H., Guan X. Non-cooperative power control for wireless ad hoc networks with repeated games. IEEE Journal on Selected Areas in Communications. 2007, 25(6): 1101-1111.
[18] Xiao, J.J., Cui, S., Luo, Z.Q., Goldsmith, A.J. Power scheduling of universal decentralized estimation in sensor networks. IEEE Trans. Signal Processing.2006, 54(2): 413-421.
[19] Zhou, P., Yao, J.H., Pei, J.L. Implementation of an energy-efficient scheduling scheme based on pipeline flux monitoring networks. Sci China Ser F- Information Sciences. 2009, 52(9): 1632-1639.
[20] Ribeiro, A., Giannakis, G.B., Rounmeliotis, S.E. SOI-KF: Distributed Kalman filtering with low-cost communications using the sign of innovations. IEEE Trans. Signal Processing. 2006, 54(12): 4782-4795.
[21] Djuric, P.M., Vemula, M., Bugallo, M.F., Target tracking by particle filtering in binary sensor networks. IEEE Trans. Signal Processing, 2008, 56(6): 2229-2238.
[22] Lacoss, R., Walton. R. Strawman design of a DSN (Distributed Sensor Networks) to detect and track low flying aircraft. In: Proc. Distributed Sensor Nets Conference. Pittsburgh, PA, 1978: 41-52.
[23] Tubaishat, M., Madria, S. Sensor networks: an overview. IEEE Potentials. 2003, 22(2): 20-23.
[24]宋文,王兵,周应宾等.无线传感器网络技术与应用.北京:电子工业出版社,2007.
[25] Sohraby, K., Minoli. D., Znati, T. Wireless sensor networks: Technology, protocols, and applications. Wiley-interscience, New Jersey, 2006.
[26]唐剑,史浩山,韩忠祥.无线传感器网络中的目标跟踪算法.空军工程大学学报. 2006, 7(5): 25-29.
[27] Guo, W.H., Liu, Z.Y., Wu, G.B. An energy-balanced transmission scheme for sensor networks. In: Proc. 1st Intl. Conf. Embedded Networked Sensor Systems. Los Angeles, CA,USA, 2003: 300-301.
[28] Kung, H.T., Vlah, D. Efficient location tracking using sensor networks. Wireless Communications and Networking. Cambridge, MA, USA, 2003: 1954-1961.
[29] Zhang, W.S., Cao, G.H. DCTC: Dynamic convoy tree-based collaboration for target tracking in sensor networks. IEEE Trans. Wireless Communications. 2004, 3(5): 1689-1701.
[30]周鸣争,楚宁,周涛,强俊.一种基于能量约束的传感器网络动态数据融合算法.仪器仪表学报. 2007, 28(1): 173-176.
[31] Burrell, J., Brooke, T., Beckwith, R. Vineyard computing: Sensor networks in agricultural production, IEEE Pervasive Computing, 2004, 3(1): 38-45.
[32] Gupta R, Das S.R. Tracking moving targets in a smart sensor network. The VTC Fall 2003 Symposium, 2003.
[33] Guo D, Wang X. Dynamic sensor collaboration via sequential Monte Carlo. IEEE Journal on Selected Areas in Communication. 2004, 22 (6): 1037-1047.
[34] Zhao, F., Shin, J., Reich, J. Information-driven dynamic sensor collaboration for tracking applications. IEEE Signal Processing Magazine. 2002, 19(2): 61-72.
[35] Chu, M., Hussecker, H., Zhao, F. Scalable information-driven sensor querying and routing for ad hoc heterogeneous sensor networks. Intl. Journal on High Performance Computing Application. 2002, 16(3): 293-313.
[36] Ramanathan, P. Location-centric approach for collaborative target detection, classification, and tracking. IEEE CAS workshop. 2002.
[37] Brook, R., Ramanathan, P., Sayeed, A. Distributed target classification and tracking in sensor networks. Proceeding of IEEE. 2003, 91(8): 1163-1171.
[38]李迅,李洪峻.基于簇结构的无线传感器网络移动目标精确跟踪.传感技术学报. 2009, 22(12): 1813-1817.
[39]杨小军,邢科义,施坤林,潘泉.传感器网络下机动目标动态协同跟踪算法.自动化学报. 2007, 33(10): 1029-1036.
[40] Chen, W.P., Hou, J.C., Sha, L. Dynamic clustering for acoustic target tracking in wireless sensor networks. IEEE Transactions on Mobile Computing, 2004, 3(3): 258-271.
[41] Phoha, S., Javobson, N., Friedlander, D. Sensor network based localization and target tracking hybridization in the operational domains of beamforming and dynamic space-time clustering. Global Telecommunications Conference. Michigan, USA, 2003: 2952- 2956.
[42] Phoha, S., Jacobson, N., Friedlander, D., Brooks, R. Sensor network based localization and tracking through hybridization in the operational domains of beamforming and dynamic space-time clustering. In: Proc. Conf. Global Telecommunications, 2003.
[43]魏雪云,廖惜春.基于卡尔曼的无线传感器网络时空融合研究.传感器与微系统, 2007,26(9): 72-75.
[44] Cucker, F., Smale, S. Emergent behavior in flocks. IEEE Trans. Automatic Control. 2007, 52 (5): 852-862.
[45] Gazi, V., Passino, K.M. Stability analysis of swarms. IEEE Trans. Automatic Control. 2003, 48 (4): 692-697.
[46] Cortes, J., Martinez, S., Bullo, F. Robust redezvous for mobile autonomous agents via proximity graphs in arbitrary dimensions. IEEE Trans. Automatic Control. 2006, 51 (8): 1289-1298.
[47] Cortes, J. Distributed Kriged Kalman filter for spatial estimation. IEEE Trans. Automatic Control. 2009, 54 (12): 2816-2827.
[48] Olfati-Saber, R. Distributed Kalman filter with embedded consensus filters. 44th IEEE Conf. on Decision and Control and European Control. 2005: 8179-84.
[49] Olfati-Saber, R. Distributed Kalman filtering for sensor networks. 46th IEEE Conf. on Decision and Control. New Orleans, UAS, Dec., 2007.
[50] Carli, R., Chiuso, A., Schenato, L., Zampieri, S. Distributed Kalman filtering based on consensus strategies. IEEE J. Selected Areas in Communications, 2006, 26(4): 622-632.
[51] Schiza, I.D., Giannakis, G.B., Roumeliotis, S.I., Ribeiro, A.R. Consensus in ad hoc WSNs with noisy links– Part 2: Distributed estimation and smoothing of random signals. IEEE Trans. Signal Processing. 2008, 56 (4): 1650-1656.
[52] Schiza, I.D., Mateos, G., Giannakis, G.B. Distributed LMS for consensus-based in-network adaptive processing. IEEE Trans. Signal Processing. 2009, 57 (6): 2365-2383.
[53] Mechitov, K., Sundresh, S., Kwon, Y., Agha, G. Cooperative tracking with binary-detection sensor networks. In: Proc. Embedded Networked Sensor System. Los Angeles, CA, 2003.
[54] Kim, W.Y., Kirill, M. On target tracking with binary proximity sensors. In: Proc. 4th Intl. Conf. Information Processing Sensor Networks. Los Angeles, California, USA, 2005: 310-308.
[55] Aslam, J., Butler, Z., Constantin, F., et al. Tracking a moving object with a binary sensor network. In: Proc. 1st Intl. Conf. Embedded Networked Sensor Systems. 2003: 150-161.
[56] Vemula, M., Bugallo, M.F., Djuri?, P.M. Particle filtering-based target tracking in binary sensor networks using adaptive thresholds. In Proc. 2nd IEEE Intl. Workshop Comput. Advan. In Multi-Sensor Adaptive Processing. 2007: 165-168.
[57]李煜,程远国,杨露青.一种二元探测传感器网络目标跟踪算法.传感技术学报. 2008, 21(11): 1900-1904.
[58] Ruan, Y., Willett, P., Marrs, A., et al. Practical fusion of quantized measurements via particle filtering. IEEE Trans. Aerosp. Electron. System. 2008, 44(1): 15-29.
[59]骆吉安,柴利,王智.无线传感器网络中基于多比特量化数据的滚动时域状态估计.电子与信息学报. 2009, 31(12): 2819-2923.
[60] Ozdemir O., Niu, R., Varshney, P.K. Tracking in wireless sensor networks using particle filtering: Physical layer considerations. IEEE Transactions on Signal Processing. 2009, 57(5): 1987-1999.
[61]关小杰,陈军勇.无线传感器网络中基于量化观测的粒子滤波状态估计.传感技术学报. 2009, 22(9): 1338-1342.
[62] You, K., Xie, L., Sun, S., Xiao, W. Multiple-level quantized innovation Kalman filter. in Proc. IFAC World Congress. Seoul, Korea, 2008.
[63] Sukhavasi, R.T., Hassibi, B. Particle filtering for quantized innovations. in Proc. IEEE Int. Conf. Acout., Speech and Signal Process., Taiwan, China, 2009.
[64] Hill, J., Szewczyk, R., Hollar, S., et al. System architecture directions for networked sensors. In Proceedings of ACM ASPLOS IX. Nov. 2000.
[65] Bar-Shalom, Y., Li, X. R. Multitarget-Multisensor Tracking: Principles and Techniques [J]. YBS Publishing, Storrs, CT, 1995.
[66] Kalman, R.E. A new approach to linear filtering and prediction problems. J. basic Eng. - T. ASME. 1960, 82: 35-45.
[67] Simon, D. Optimal State Estimation, Kalman, H-inf and Nonlinear Approaches. New Jersey: John Wiley & Sons, 2006.
[68] Julier, S.J., Uhlmann, J.K., Durrant-Whyte, H.F. A new method for the nonlinear transformations of means and covariances in filters and estimators. IEEE Trans. Autom. Control. 2000, 45(3): 477-482.
[69] Arulampalam, M. S., Maskell, S., Gordon, N., Clapp, T. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans. Signal Processing. 2002, 50(2): 174-188.
[70] Ristic, B., Arulampalam, S., Gordon, N. Beyond the Kalman Filtering: Particle Filtering for Tracking Application, Artech House, 2004
[71] Anderson, B.D.O., Moore, J.B. Optimal filtering. Prentice-Hall, NJ, USA, 1979.
[72] Brown, R.G., Hwang, P.Y.C. Introduction to random signals and applied Kalman filtering. Wiley, NY, USA, 1992.
[73] Palhares, R.M., Pedro, L.D.P. Robust filtering with guaranteed energy-to-peak performance- an LMI approach. Automatica. 2000, 36: 851-858.
[74] Gao, H., Lam, J., Wang, C. Robust energy-to-peak filter design for stochastic time-delay systems. Syst. Control Letters. 2006, 55: 101-111.
[75] Nagpal, K.M., Khargonekar, P.P. Filtering and smoothing in an H∞setting. IEEE Trans.Automatic Control. 1991, 36: 152-166.
[76] Yaesh, I., Shaked, U. A transfer function approach to the problem of discrete-time systems H∞–optimal linear control and filtering. IEEE Trans. Automatic Control. 1991, 36: 1272-1276.
[77] de Souza, C.E., Fu, U., Xie, L. H∞estimation for discrete-time linear uncertain systems. International Journal of Robust Nonlinear Control. 1991, 1: 11-23.
[78] Grimble, M.J., Ho, D., EI Sayed A. Solution of the H∞optimal linear filtering problem for discrete-time systems. IEEE Trans. Acoustics, Speech and Signal Processing.1990, 38: 1092-1104.
[79] Boyd, S., Ghaoui, El., Feron, E., Balakrishnan, V. Linear matrix inequality in systems and control theory. SIAM Frontier Series, April 1994.
[80] Nemirovskii, A. Several NP-hard problems arising in robust stability analysis. Math. Control Signals Systems. 1993, 6: 99-105.
[81] de Oliveira, M.C., Bernussou, J., Geromel, C. A new discrete-time robust stability condition. Systems & Control Letters. 1999, 37: 261-265.
[82] Tuan, H.D., Apkarian, P., Nauyen, T.Q. Robust and reduced-order filtering: New LMI-based characterizations and methods. IEEE Trans. Signal Processing. 2001, 49: 2975-2984.
[83] Gao, H., Wang, C. A delay-dependent approach to robust H∞filtering for uncertain discrete-time state-delayed systems. IEEE Tran. Sign. Processing. 2004, 52:1631-1640.
[84] Wilson, D.A. Convolution and Hankel operator norms for linear systems. IEEE Trans, Automat. Control. 1989, 34: 94-97.
[85] Scherer, C. Mixed H2/ H∞control. In Isidori, A. Trends in control. Springer, Berlin, German, 1995: 173-216.
[86] Skelton, R.E., Iwasaki, T. Grigoriadis, K.M. A unified algebraic approach to linear control design. Taylor & Fancis, London, UK, 1997.
[87] Grigoriadis, K.M., Watson, J.T. Reduced-order H∞and L2 - L∞filtering via linear matrix inequalities. IEEE Trans. Aero. Elec. System. 1997, 33: 1326-1338.
[88] Takaba, K. Robust H2 control of descriptor system with time-varying uncertainty. Int. J. Control. 1998, 71: 559-579.
[89] Dai, L. Singular control systems. Springer-Verlag, Berlin, Germany, 1989.
[90] Xu, S., Lam, J. H∞model reduction for discrete-time singular systems. Syst. & Control Letters. 2003, 48: 121-133.
[91] Xu S., Lam J. Robust control and filtering of singular systems. Springer-Verlag, Berlin, German, 2006.
[92] Hsiung, K.J., Lee, L. Lyapunov inequality and bounded real lemma for discrete-timedescriptor systems. IEE proc.- Control Theory Applications. 1999, 146: 327-331.
[93] Xu, S., Dooren, P.V., Stefan, R., Lam, J. Robust stability and stabilization for singular systems with state delay and parameter uncertainty. IEEE Trans. Automat. Control. 2002, 47: 1122-1128.
[94] Boyd, S., Ghaoui, L.E., Feron, E., Balakrishnan, V. Linear matrix inequalities in system and control theory [M]. Philadelphia, PA: SIAM. 1994.
[95] Xu, S., Yang, C. Stabilization of discrete-time singular systems: a matrix inequalities approach. Automatica. 1999, 35: 1613-1617.
[96] Lee, C.M., Fong, I.K. H∞optimal singular and normal filter design for uncertain singular systems. IET Control Theory Applications. 2007, 1: 119-126.
[97] Lee, C.M., Fong, I.K. H∞filter design for uncertain discrete-time singular systems via normal transformation. Circ., Syst. & Signal Processing. 2006, 25: 525-538.
[98]何友,王国宏,彭应宁等.多传感器信息融合及应用[M].北京:电子工业出版社,2000.
[99]韩崇昭,朱洪艳,段战胜等.多源信息融合[M].北京:清华大学出版社,2006.
[100]王志胜,姜斌,甄子洋.融合估计与融合控制[M].北京:科学出版社,2009.
[101] Jazwinski A.H. Stochastic processes and filtering theory [J]. Academic Press, New York, 1970.
[102] Chen, L., Arambel, P.O., Mehra, R.K. Estimation under unknown correlation: Covariance Intersection revisited [J]. IEEE Transactions on Automatic Control. 2002, 47(3): 1879-1882.
[103] Uhlmann, J.K. Covariance consistency methods for fault-tolerant distributed data fusion [J]. Information Fusion. 2003, 4: 201-215.
[104] Sun, S.L., Deng, Z.L. Multi-sensor optimal information fusion Kalman filter [J]. Automatica. 2004, 40(6): 1017-1023.
[105] Julier, S.J., Uhlmann, J.K. A non-divergent algorithm in the presence of unknown correlation [J]. in Proc. Am. Control Confernce. Albuquerque, New Mexico, 1997: 2369-2373.
[106] Horn, R.A, Johson, C.A. Matrix Analysis. Campgridge University Press, 1985.
[107] Tse, D., Viswanath, P. Fundamentals of wireless communication. Cambridge, U.K.: Cambridge University Press, 2005.
[108]孙圣和.矢量量化技术及应用[J].北京:科学出版社, 2002.
[109] Bhardwaj, M., Chandrakasan, A.P. Bounding the lifetime of sensor networks via optimal role assignments [J]. IEEE INFOCOM, 2002.
[110] Duda, R.O., Har, P.E., Stork, D.G. Pattern Classification. Wiley India Pvt. Ltd., 2007.
[111] Hurley, M.B. An information theoretic justification for covariance intersection and itsgeneralization [C]. Proc. Fifth Int’l Conf. Information Fusion. 2002: 505-511.
[112] Benaskeur, A.R. Consistent fusion of correlated data sources [C]. in Proc Decision Support Syst. Sect., Defence Res. & Devices. Val-Blair, Que., Canada, 2002: 2652-2656.
[113] Vazhentsev, A.Y. On internal ellipsoidal approximation for problems of control synthesis with bounded coordinates. Journal of Computer and System Sciences International. 2000, 39: 399-406.
[114] Kurzhanski, A.B. Ellipsoidal techniques for dynamic systems: The problem of control synthesis. Dynamics and Control, 1991, 1: 357-378.
[115] Ben-Tal, A., Nemirovski, A. Lectures on modern convex optimization. Philadelphia, PA: SIAM. 2001.
[116] Intanagonwiwat, C., Estrin, D., Govindan, R., et al. Impact of network density on data aggregation in wireless sensor networks. In Technical Report 01-750, University of Southern California, Nov. 2001.
[117] Bhaskar, K., Deborh, E., Stephen, B.W. The impact of data aggregation in wireless sensor networks. In Proc. 22nd Intl. Conf. Distributed Computing Systems. 2002: 575-578.
[118] Zhang, W., Cao, G. DCTC: Dynamic convoy tree-based collaboration for target tracking in sensor networks. IEEE Transactions on Wireless Communications. 2004.
[119]潘峰,秦丽,孟令军,具有声定位功能的无线传感器网络节点设计.计算机工程. 2008,34(23): 107-109.
[120] Ruan, Y., Willett, P. A quantization architecture for track fusion [J]. IEEE Trans. Aerospace & Electr. Systems. 2005, 41 (2): 671-681.
[121] Gubner, J. Distributed estimation and quantization. IEEE Trans. Information Theory. 1993, 39: 1456-1459.
[122] Lam, W., Reibman, A. Quantizer design for decentralized systems with communication constrains. IEEE Trans. Communications. 1993, 41: 1602-1605.
[123] Wang, Q.X., Chen, W.P., Zheng, R., et al. Acoustic target tracking using tiny wireless sensor devices. in Proc. Int. Workshop on Infor. Proces. Sensor Network, 2003.
[124] Gan, Q., Harris, C.J. Comparison of two measurement fusion methods for Kalman-filter-based multisensory data fusion. IEEE Trans. Aerospace & Elec. Systems.2001, 37(1): 273-280.
[125] Li, X.R., Zhu, Y., Han, C. Unified optimal linear estimation fusion. in Proc. 39th IEEE Conf. On Decision and Control. Sydney, Australia, 2005: 10-25.
[126] Cui, S., Goldsmith, A., Bahai, A. Energy-constrained modulation optimization. IEEE Trans. Wireless Communication. 2005, 4(5): 2349-2360.
[127] Boyd, S., Vandenberghe, L. Convex Optimization. Cambridge, U.K.: Cambridge Univ.Press, 2003.
[128] Sheng, X., Hu, Y. H. Maximum likelihood multiple-source localization using acoustic energy measurements with wireless sensor networks. IEEE Trans. Signal Processing. 2005, 53(1): 44-53.
[129] Pham, T., Sadler, B.M., Papadopulos, H. Energy-based source localization via ad-hoc acoustic sensor network. In Proc. IEEE Workshop on Statistics Signal Processing. St. Louis, MO, Sep. 2003, 5: 387-390.
[130] Kinsler, L.E., Frey, A.R. Fundamentals of Acoustics. New York: Wiley, 1962.
[131] Levanon, N. Radar Principles. New York: Wiley, 1988.
[132] Gordon N.J., Salmond, D.J., Smith, A.F.M. Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proc.–F. 1993, 140(2): 107-113.
[133] Tickavsky, P., Muravchik, C.H., Nehorai, A. Posterior Cramér-Rao bounds for discrete-time nonlinear filtering. IEEE Trans. Signal Processing. 1998, 46(5): 1386-1396.
[134] VanTree, H.L. Detection, Estimation and Modulation Theory. New York: John Wiley & Sons, 1968.
[135] Bar-Shalom, Y., Li, X.R., Kirubarajan, T. Estimation with Applications to Tracking and Navigation. New York: Wiley, 2001.
[136] Proakis, J.G. Digital Communications. McGraw-Hill, Columbus, Ohio, USA, 4th edition, 2001.
[137] Luo, X., Giannakis, G.B. Energy-constrained optimal quantization for wireless sensor networks. EURASIP Journal on Advances in Signal Processing, 2008: 1-12.
[138] Srivastava, V., Motani, M. Cross-layer design: a survey and the road ahead. IEEE Communication Magazine. Dec. 2005.
[139] Srivastava, V., Motani, M. Cross-layer design: A survey and the road ahead. IEEE Communications Magazine. 2005, 43(12): 112-119.
[140] Melodia, T., Vuran, M.C., Pompili, D. The state of art in cross-layer design for wireless sensor networks. In Cesana, M., Fratta, L. Network Architect. In Next Generation Internet. Berlin Heidelberg: Springer-Verlag, 2006.
[141] Kwon, H., Kim, T.H., Choi, S., Lee, B.G. A cross-layer strategy for energy-efficient reliable delivery in wireless sensor networks. IEEE Trans. on Wireless Communications. 2006, 5(12): 3689-3699.
[142] Wang, H., Yang, Y., Ma, M., He, J., Wang, X. Network lifetime maximization with cross-layer design in wireless sensor networks. IEEE Trans. on Wireless Communications. 2008, 7(10): 3759-3768.
[143] Song, L., Hatzinakos, D. A cross-layer architecture of wireless sensor networks for target tracking. IEEE/ACM Transactions on networking. 2007, 15(1): 145-158.
[144] Raman, B., Bhagwat, P., Seshan, S. Arguments for cross-layer optimizations in Bluetoothscatternets. In: Proc. Symp. Applications & the Internet. 2001: 176-184.
[145] Kapan, L.M. Global node selection for target localization in a distributed network of bearings-only sensors. IEEE Trans. Aerospace & Electronics Systems. 2006, 42(1): 113-135.
[146] Gu, D. Distributed EM algorithm for Gaussian mixtures in sensor networks. IEEE Trans. Neural Networks. 2008, 19(7): 1311-1319.
[147] Mutambara, A.G.O. Decentralized estimation and control for multisensory systems. Boca Raton, FL: CRC Press, 1998.
[148] Vercauteren, T., Wang, X. Decentralized Sigma-point information filters for target tracking in collaborative sensor networks. IEEE T. Signal Processing. 2005, 53(8): 2997-3009
[149] Godsil, C., Royle, G. Algebraic graph theory. Springer-Verlag, 2001.
[150] Lynch, N.A. Distributed Algorithms. Morgan Kaufmann Publishers, Inc., 1997.
[151] DeGroot, M.H. Reaching a consensus. Journal of American Statistical Association, 1974, 69(345): 118-121.
[152] Benediktsson, J.A., Swain, P.H. Consensus theoretic classification methods. IEEE Trans. Systems, Man and Cybernetics, 1992, 22(4): 688-704.
[153] Hatano, Y., Mesbahi, M. Agreement over random networks. IEEE Trans. Automatic Control. 2005, 50 (11): 1867-1872.
[154] Yang, W., Wang X.F. Consensus filters on small world networks. Dynamics of Continous, Discrete, and Impulsive Systems -B. 2006, 13(3/4): 379-386.
[155] Tanner, H.G., Pappas, G.J., Kumar, V. Leader-to-formation stability. IEEE Trans. Automatic Control. 2004, 49 (3): 443-455.
[156] Saber, R.O., Murray, R.M. Consensus protocols for networks of dynamic agents. Proc. American Control Conference. 2003, 2: 951-956.
[157] Saber, R.O., Murray, R.M. Consensus protocols in networks of agents with switching topology and time-delay. IEEE Trans. Automat. Control. 2004, 49(9): 1520-1533.
[158] Saber, R.O. Ultrafast consensus in small-world networks. Proc. The 2005 American Control Conference. pp. 2371-2378, 2005.
[159] Spanos, D., Olfati-Saber, R. Murray, R.M. Consensus tracking in mobile networks. The 16th IFAC World Congress, Prague, Czech, 2005.
[160] Mangoubi, R. Roubst estimation and failure detection: a concise treatment. Technology& Engineering, 1998.
[161] Zhang, Y., Soh, Y.C., Chen, W. Robust information filter for decentralized estimation. Automatica. 2005, 41: 2141-2146.
[162] Lefebvre, T., Bruynickx, H., Schutter, J. Comment on“a new method for the nonlineartransformation of means and covariances in filters and estimation. IEEE Trans. Automat. Control. 2002, 50: 655-61.
[163] Merwe, V.R., Wan, E. Sigma-point Kalman filters for probabilistic inference in dynamic state-space models. Proc. Workshop Adv. In Machine Learning. Montreal, QC, Canada, 2003.
[164] Estrin, D., Govindan, R., Heidemann, J., Kumar, S. Next contrary challenges: Scalable coordination in sensor networks. Mobicom’99. Seattle Washington, UAS, 1999.
[165] Msechu, E.J., Roumeliotis, S.I., Ribeiro, A., Giannakes, G.B. Decentralized quantized Kalman filtering with scalable communication cost. IEEE Trans. Signal Processing, 2008, 56: 3727-41.