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Introduction

  • Wen-An Zhang
  • Bo Chen
  • Haiyu Song
  • Li Yu
Chapter
  • 555 Downloads

Abstract

The multisensor fusion estimation has attracted considerable research interest during the past decades and has found applications in a variety of areas, such as target tracking and localization, guidance and navigation, and fault detection [1, 2, 5, 17]. Multisensor fusion is used because of potentially improved estimation accuracy [2, 71] and enhanced reliability and robustness against sensor failures. Many useful fusion estimation methods have been presented in the literature (see, e.g., [8, 12, 14, 20, 25, 36, 41, 46, 58, 69, 70, 75, 77, 80, 86] and the references therein). Recently, the rapid developments of wireless sensor networks (WSNs) have greatly widen applications of the multisensor fusion estimation theory, which in turn, helps the WSNs monitor the environment more accurately and efficiently. Therefore, the WSN-based multisensor fusion estimation and its applications have attracted considerable research interest during the past decade [22, 39, 57, 83].

Keywords

Sensor Network Sensor Node Packet Loss Fusion Center Considerable Research Interest 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Bar-Shalom Y, Li XR (1990) Multitarget-multisensor tracking: advanced applications, vol 1. Artech House, NorwoodGoogle Scholar
  2. 2.
    Bar-Shalom Y, Li XR, Kirubarajan T (2001) Estimation with applications to tracking and navigation. Wilely, New YorkCrossRefGoogle Scholar
  3. 3.
    Cabral FR, Brossier JM (2014) Scalar quantization for estimation: from an asymptotic design to a practical solution. IEEE Trans Signal Process 62(11):2860–2870MathSciNetCrossRefGoogle Scholar
  4. 4.
    Carli R, Fagnani F, Frasca P, Zampieri S (2008) A probabilistic analysis of the average consensus algorithm with quantized communication. In: Proceedings of the 17th IFAC world congress, Seoul, pp 8062–8067Google Scholar
  5. 5.
    Carlson NA (1990) Federated square root filter for decentralized parallel processes. IEEE Trans Aerosp Electron Syst 26(3):517–529CrossRefGoogle Scholar
  6. 6.
    Cattivelli FS, Sayed AH (2010) Diffusion strategies for distributed Kalman filtering and smoothing. IEEE Trans Autom Control 55(9):2069–2084MathSciNetCrossRefGoogle Scholar
  7. 7.
    Cattivelli FS, Sayed AH (2010) Diffusion LMS strategies for distributed estimation. IEEE Trans Signal Process 58(3):1035–1048MathSciNetCrossRefGoogle Scholar
  8. 8.
    Chang KC, Tian Z, Mori S (2004) Performance evaluation for MAP state estimate fusion. IEEE Trans Aerosp Electron Syst 40(2):706–714CrossRefGoogle Scholar
  9. 9.
    Chang LY, Chen PY, Wang TY, Chen CS (2011) A low-cost VLSI architecture for robust distributed estimation in wireless sensor networks. IEEE Trans Circuit Syst-I Regul Pap 58(6):1277–1286MathSciNetCrossRefGoogle Scholar
  10. 10.
    Chen HM, Zhang KS, Li XR (2004) Optimal data compression for multisensor target tracking with communication constraints. In: Proceedings of the 43th IEEE conference on decision and control, Atlantis, pp 8179–8184Google Scholar
  11. 11.
    Chen B, Yu L, Zhang WA (2011) Robust Kalman filtering for uncertain state delay systems with random observation delays and missing measurements. IET Control Theory Appl 5(17):1945–1954MathSciNetCrossRefGoogle Scholar
  12. 12.
    Chen B, Yu L, Zhang WA, Liu AD (2013) Robust information fusion estimator for multiple delay-tolerant sensors with different failure rates. IEEE Trans Circuit Syst-I Regul Pap 60(2):401–414MathSciNetCrossRefGoogle Scholar
  13. 13.
    Chiuso A, Schenato L (2011) Information fusion strategies and performance bounds in packet-drop networks. Automatica 47:1304–1316MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Deng ZL, Gao Y, Mao L et al (2005) New approach to information fusion steady-state Kalman filtering. Automatica 41(10):1695–1707MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Dong H, Wang Z, Ho DWC, Gao H (2010) Variance-constrainted H filtering for a class of nonlinear time-varying systems with multiple missing measurements: the finite-horizon case. IEEE Trans Signal Process 58(5):2534–2543MathSciNetCrossRefGoogle Scholar
  16. 16.
    Dong H, Wang Z, Gao H (2010) Robust H filtering for a class of nonlinear networked systems with multiple stochastic communication delays and packet dropouts. IEEE Trans Signal Process 58(4):1957–1966MathSciNetCrossRefGoogle Scholar
  17. 17.
    Dong HL, Wang ZD, Gao HJ (2012) Fault detection for Markovian jump systems with sensor saturations and randomly varying nonlinearities. IEEE Trans Circuit Syst-I Regul Pap 59(10):2354–2362MathSciNetCrossRefGoogle Scholar
  18. 18.
    Dong H, Wang Z, Gao H (2012) Distributed filtering for a class of time-varying systems over sensor networks with quantization errors and successive packet dropouts. IEEE Trans Signal Process 60(6):3164–3173MathSciNetCrossRefGoogle Scholar
  19. 19.
    Dimakis AG, Kar S, Moura JMF, Rabbat MG, Scaglione A (2010) Gossip algorithms for distributed signal processing. Proc IEEE 98(11):1847–1864CrossRefGoogle Scholar
  20. 20.
    Duan Z, Li XR (2013) Lossless linear transformation of sensor data for distributed estimation fusion. IEEE Trans Signal Process 59(1):362–372MathSciNetCrossRefGoogle Scholar
  21. 21.
    Epstein M, Shi L, Tiwari A, Murry R (2008) Probabilistic performance of state estimation across a lossy network. Automatica 44(12):3046–3053MathSciNetCrossRefzbMATHGoogle Scholar
  22. 22.
    Fang J, Li H (2008) Distributed adaptive quantization for wireless sensor networks: from delta modulation to maximum likelihood. IEEE Trans Signal Process 56(10):5246–5257MathSciNetCrossRefGoogle Scholar
  23. 23.
    Fang J, Li H (2009) Hyperplane-based vector quantization for distributed estimation in wireless sensor networks. IEEE Trans Inf Theory 55:5682–5699MathSciNetCrossRefGoogle Scholar
  24. 24.
    Fu MY, de Souza CE (2009) State estimation for linear discrete-time systems using quantized measurements. Automatica 45(12):2937–2945MathSciNetCrossRefzbMATHGoogle Scholar
  25. 25.
    Gan Q, Harris CJ (2001) Comparison of two measurement fusion methods for Kalman-filter-based multisensor data fusion. IEEE Trans Aerosp Electron Syst 37(1):273–280CrossRefGoogle Scholar
  26. 26.
    Ghasemi N, Dey S (2008) Power-efficient dynamic quantization for multisensor HMM state estimation over fading channels. In: The 3rd international symposium on communications, control and signal processing, St Julians, pp 1553–1558Google Scholar
  27. 27.
    Giridhar A, Kumar PR (2006) Towards a theory of in-network computation in wireless sensor networks. IEEE Commun Mag 44(4):98–107CrossRefGoogle Scholar
  28. 28.
    He LD, Han DF, Wang XF, Shi L (2013) Optimal linear state estimation over a packet-dropping network using linear temporal coding. Automatica 49(4):1075–1082MathSciNetCrossRefzbMATHGoogle Scholar
  29. 29.
    Hounkpevi FO, Yaz EE (2007) Robust minimum variance linear state estimators for multiple sensors with different failure rates. Automatica 43(7):1274–1280MathSciNetCrossRefzbMATHGoogle Scholar
  30. 30.
    Hu L, Wang Z, Liu X (2015) Dynamic state estimation of power systems with quantization effects: a recursive filter approach. IEEE Trans Neural Netw Learn Syst. doi:10.1109/TNNLS.2014.2381853Google Scholar
  31. 31.
    Huang M, Dey S (2007) Stability of Kalman filtering with Markovian packet losses. Automatica 43(4):598–607MathSciNetCrossRefzbMATHGoogle Scholar
  32. 32.
    Jin ZP, Gupta V, Murray RM (2006) State estimation over packet dropping networks using multiple description coding. Automatica 42(9):1441–1452MathSciNetCrossRefzbMATHGoogle Scholar
  33. 33.
    Kar S, Moura JMF (2011) Gossip and distributed Kalman filtering: weak consensus under weak detectability. IEEE Trans Signal Process 59(4):1766–1784MathSciNetCrossRefGoogle Scholar
  34. 34.
    Kar S, Sinopoli B, Moura JMF (2012) Kalman filtering with intermittent observations: weak convergence to a stationary distribution. IEEE Trans Autom Control 57(2):405–420MathSciNetCrossRefGoogle Scholar
  35. 35.
    Khan UA, Moura JMF (2008) Distributed the Kalamn filter for large-scale systems. IEEE Trans Signal Process 56(10):4919–4935MathSciNetCrossRefGoogle Scholar
  36. 36.
    Kim KH (1994) Development of track to track fusion algorithm. In: Proceedings of the American control conference, Maryland, pp 1037–1041Google Scholar
  37. 37.
    Krishnamurthy V (1995) Estimation of quantized linear errors-in-variables models original research. Automatica 31(10):1459–1464MathSciNetCrossRefzbMATHGoogle Scholar
  38. 38.
    Lerdsuwanakij K, Chugg KM, Polydoros A (1999) Quantization-based estimation. In: Conference record of the thirty-third asilomar conference on signals, systems, and computers, Pacific Grove, pp 37–41Google Scholar
  39. 39.
    Li JL, AlRegib G (2009) Distributed estimation in energy-constrained wireless sensor networks. IEEE Trans Signal Process 57(10):3746–3758MathSciNetCrossRefGoogle Scholar
  40. 40.
    Li HB, Fang J (2007) Distributed adaptive quantization and estimation for wireless sensor networks. IEEE Signal Process Lett 14(10):669–672CrossRefGoogle Scholar
  41. 41.
    Li XR, Zhu YM, Wang J, Han CZ (2003) Optimal linear estimation fusion-part I: unified fusion rules. IEEE Trans Inf Theory 49(9):2192–2208CrossRefzbMATHGoogle Scholar
  42. 42.
    Liang Y, Chen TW, Pan Q (2010) Optimal linear state estimator with multiple packet dropouts. IEEE Trans Autom Control 55(6):1428–1433MathSciNetCrossRefGoogle Scholar
  43. 43.
    Lv N, Sun SL (2009) Scalar-weighted fusion estimators for systems with multiple sensors and multiple delayed measurements. In: Proceedings of IEEE conference on decision and control, Shanghai, pp 7599–7602Google Scholar
  44. 44.
    Ma J, Sun S (2011) Information fusion estimators for systems with multiple sensors of different packet dropout rates. Inf Fusion 12(3):213–222CrossRefGoogle Scholar
  45. 45.
    Ma L, Da F, Zhang KJ (2011) Exponential H filter design for discrete time-delay stochastic systems with markovian jump parameters and missing measurements. IEEE Trans Circuit Syst-I Regul Pap 58(5):994–1007MathSciNetCrossRefGoogle Scholar
  46. 46.
    Manyika J, Durrant-Whyte H (1994) Data fusion and sensor management: a decentralized information-theoretic approach. Ellis Horwood, New YorkGoogle Scholar
  47. 47.
    Marano S, Matta V, Willett P (2005) Some approaches to quantization for distributed estimation with data association. IEEE Trans Signal Process 53(3):885–895MathSciNetCrossRefGoogle Scholar
  48. 48.
    Matveev AS, Savkin AV (2003) The problem of state estimation via asynchronous communication channels with irregular transmission times. IEEE Trans Autom Control 48(4):670–676MathSciNetCrossRefGoogle Scholar
  49. 49.
    Moayedi M, Foo YK, Soh YC (2010) Adaptive Kalman filtering in networked systems with random sensor delays, multiple packet dropouts and missing measurements. IEEE Trans Signal Process 58(3):1577–1588MathSciNetCrossRefGoogle Scholar
  50. 50.
    Msechu EJ, Roumeliotis SI, Ribeiro A, Giannakis GB (2008) Decentralized quantized Kalman filtering with scalable communication cost. IEEE Trans Signal Process 56(8):3727–3741MathSciNetCrossRefGoogle Scholar
  51. 51.
    Olfati-Saber R (2005) Distributed Kalman filter with embedded consensus filters. In: Proceedings of the 44th IEEE conference decision and control, Sevilla, pp 8179–8184Google Scholar
  52. 52.
    Olfati-Saber R (2007) Distributed Kalman filtering for sensor networks. In: Proceedings of the 46th IEEE conference on decision and control, New Orleans, pp 5492–5498Google Scholar
  53. 53.
    Penarrocha I, Sanchis R, Albertos P (2012) Estimation in multisensor networked systems with scarce measurements and time varying delays. Syst Control Lett 61:555–562MathSciNetCrossRefzbMATHGoogle Scholar
  54. 54.
    Ribeiro A, Giannakis GB (2006) Bandwidth-constrained distributed estimation for wireless sensor networks-part I: gaussian case. IEEE Trans Signal Process 54(3):1131–1143CrossRefGoogle Scholar
  55. 55.
    Ribeiro A, Giannakis GB (2006) Bandwidth-constrained distributed estimation for wireless sensor networks-part II: unknown probability density function. IEEE Trans Signal Process 54(7):2784–2796CrossRefGoogle Scholar
  56. 56.
    Ribeiro A, Giannakis GB, Roumeliotis SI (2006) SOI-KF: distributed Kalman filtering with low-cost communications using the sign of innovations. IEEE Trans Signal Process 54(12):4782–4795CrossRefGoogle Scholar
  57. 57.
    Ribeiro A, Schizas ID, Roumeliotis SI, Giannakis GB (2010) Kalman filtering in wireless sensor networks. IEEE Control Syst Mag 30(2):66–86MathSciNetCrossRefGoogle Scholar
  58. 58.
    Roecker JA, McGillem CD (1988) Comparison of two-sensor tracking methods based on state vector fusion and measurement fusion. IEEE Trans Aerosp Electron Syst 24(4):447–449CrossRefGoogle Scholar
  59. 59.
    Sahebsara M, Chen TW, Shah SL (2007) Optimal H 2 filtering in networked control systems with multiple packet dropouts. IEEE Trans Autom Control 52(8):1508–1513MathSciNetCrossRefGoogle Scholar
  60. 60.
    Schenato L (2008) Optimal estimation in networked control systems subject to random delay and packet drop. IEEE Trans Autom Control 53(5):1311–1317MathSciNetCrossRefGoogle Scholar
  61. 61.
    Schizas ID, Giannakis GB, Luo ZQ (2007) Distributed estimation using reduced-dimensionality sensor observations. IEEE Trans Signal Process 55(8):4284–4299MathSciNetCrossRefGoogle Scholar
  62. 62.
    Schizas I, Ribeiro A, Giannakis G (2007) Consensus in ad hoc WSNs with noisy links-part I: distributed estimation of deterministic signals. IEEE Trans Signal Process 56(1):350–364MathSciNetCrossRefGoogle Scholar
  63. 63.
    Shen XJ, Varshney PK, Zhu YM (2007) Robust distributed maximum likelihood estimation with dependent quantized data. Automatica 43(6):1117–1123MathSciNetCrossRefzbMATHGoogle Scholar
  64. 64.
    Shen B, Wang ZD, Hung YS (2010) Distributed H -consensus filtering in sensor networks with multiple missing measurements: the finite-horizon case. Automatica 46(10):1682–1688MathSciNetCrossRefzbMATHGoogle Scholar
  65. 65.
    Shen XJ, Zhu YM, You ZS (2010) A sensor quantization algorithm for best linear estimation fusion in bandwidth-constrained systems. In: The 2010 international conference on intelligent control and information processing, Dalian, pp 433–438Google Scholar
  66. 66.
    Shen X, Zhu Y, You Z (2011) An efficient sensor quantization algorithm for decentralized estimation fusion. Automatica 47:1053–1059MathSciNetCrossRefzbMATHGoogle Scholar
  67. 67.
    Silva EI, Solis MA (2013) An alternative look at the constant-gain Kalman filter for state estimation over erasure channels. IEEE Trans Autom Control 58(12):3259–3265CrossRefGoogle Scholar
  68. 68.
    Sinopoli B, Schenato L, Franceschetti M, Poolla K, Jordan MI, Sastry SS (2004) Kalman filtering with intermittent observations. IEEE Trans Autom Control 49(9):1453–1464MathSciNetCrossRefGoogle Scholar
  69. 69.
    Song EB, Zhu YM, Zhou J, You ZS (2007) Optimal Kalman filtering fusion with cross-correlated sensor noises. Automatica 43(8):1450–1456MathSciNetCrossRefzbMATHGoogle Scholar
  70. 70.
    Song E, Xu J, Zhu Y (2014) Optimal distributed Kalman filtering fusion with singular covariances of filtering errors and measurement noises. IEEE Trans Autom Control 59(5):1271–1282MathSciNetCrossRefGoogle Scholar
  71. 71.
    Sun SL, Deng ZL (2004) Multi-sensor optimal information fusion Kalman filter. Automatica 40(6):1017–1023MathSciNetCrossRefzbMATHGoogle Scholar
  72. 72.
    Sun XJ, Deng ZL (2009) Information fusion wiener filter for the multisensor multichannel ARMA signals with time-delayed measurements. IET Signal Process 3(5):403–415MathSciNetCrossRefGoogle Scholar
  73. 73.
    Sun SL, Lin JY, Xie LH, Xiao WD (2007) Quantized Kalman filtering. In: The 22nd IEEE international symposium on intelligent control, Singapore, pp 7–12Google Scholar
  74. 74.
    Sun SL, Xie LH, Xiao WD, Soh YC (2008) Optimal linear estimation for systems with multiple packet dropouts. Automatica 44(7):1333–1342MathSciNetCrossRefzbMATHGoogle Scholar
  75. 75.
    Tian X, Bar-Shalom Y (2009) Exact algorithm for four track-to-track fusion configurations: all you wanted to know but were afraid to ask. In: Proceedings of the 12th international conference on information fusion, Seattle, pp 537–544Google Scholar
  76. 76.
    Varshney RK, Varshney PK (1986) Recrusive estiamtion with uncertain observations in a multisensor environment. IEE Proc F Commun Radar Signal Process 133(6):527–523CrossRefGoogle Scholar
  77. 77.
    Wang YM, Li XR (2012) Distributed estimation fusion with unavailable cross-correlation. IEEE Trans Aerosp Electron Syst 48(1):259–278CrossRefGoogle Scholar
  78. 78.
    Wang ZD, Ho DWC, Liu XH (2003) Variance-constrained filtering for uncertain stochastic systems with missing measurements. IEEE Trans Autom Control 48(7):1254–1258MathSciNetCrossRefGoogle Scholar
  79. 79.
    Wang ZD, Yang FW, Ho DWC, Liu XH (2005) Robust finite-horizon filtering for stochastic systems with missing measurements. IEEE Signal Process Lett 12(6):437–440CrossRefGoogle Scholar
  80. 80.
    Willner D, Chang CB, Dunn KP (1976) Kalman filter algorithm for a multisensor system. In: Proceedings of the IEEE conference on decision and control, Clearwater, pp 570–574Google Scholar
  81. 81.
    Xia Y, Shang J, Chen J, Liu GP (2009) Networked data fusion with packet losses and variable delays. IEEE Trans Syst Man Cybern Part B Cybern 39(5):1107–1120CrossRefGoogle Scholar
  82. 82.
    Xiao JJ, Cui SG, Luo ZQ, Goldsmith AJ (2006) Power scheduling of universal decentralized estimation in sensor networks. IEEE Trans Signal Process 54(2):413–422MathSciNetCrossRefGoogle Scholar
  83. 83.
    Xiao JJ, Ribeiro A, Luo ZQ, Giannakis GB (2006) Distributed compression estimation using wireless sensor networks. IEEE Signal Process Mag 7:27–41CrossRefGoogle Scholar
  84. 84.
    Xiao L, Boyd S, Kim SJ (2007) Distributed average consensus with least-mean-square deviation. J Parallel Distrib Comput 67(1):33–46CrossRefzbMATHGoogle Scholar
  85. 85.
    Xiao N, Xie LH, Fu MY (2009) Kalman filtering over unreliable communication networks with bounded Markovian packet dropouts. Int J Robust Nonlinear Control 19(16):1770–1786MathSciNetCrossRefzbMATHGoogle Scholar
  86. 86.
    Xu J, Song E, Luo Y, Zhu Y (2012) Optimal distributed Kalman filtering fusion algorithm without invertibility of estimation error and sensor noise covariances. IEEE Signal Process Lett 19(1):55–58CrossRefGoogle Scholar
  87. 87.
    Yang F, Wang Z, Feng G, Liu X (2009) Robust filtering with randomly varying sensor delay: the finite-horizon case. IEEE Trans Circuit Syst-I Regul Pap 56(3):664–672MathSciNetCrossRefGoogle Scholar
  88. 88.
    You KY, Fu MY, Xie LH (2011) Mean square stability for Kalman filtering with Markovian packet losses. Automatica 47(12):2647–2657MathSciNetCrossRefzbMATHGoogle Scholar
  89. 89.
    Zhang KS, Li XR (2004) Optimal sensor data quantization for best linear unbiased estimation fusion. In: The 43rd IEEE conference on decision and control, Nassau, pp 2656–2661Google Scholar
  90. 90.
    Zhang H, Xie L, Zhang D, Soh YC (2004) A reorganized innovation approach to linear estimation. IEEE Trans Autom Control 49(10):1810–1814MathSciNetCrossRefGoogle Scholar
  91. 91.
    Zhang H, Feng G, Duan G, Lu X (2006) H filtering for multiple-time-delay measurements. IEEE Trans Signal Process 54(5):1681–1688CrossRefGoogle Scholar
  92. 92.
    Zhang WA, Yu L, Song HB (2009) H filtering of networked discrete-time systems with random packet losses. Inf Sci 179(22):3944–3955MathSciNetCrossRefzbMATHGoogle Scholar
  93. 93.
    Zhang H, Feng G, Han C (2011) Linear estimation for random delay systems. Syst Control Lett 60(7):450–459MathSciNetCrossRefzbMATHGoogle Scholar
  94. 94.
    Zhang HS, Song X, Shi L (2012) Convergence and mean square stability of suboptimal estimator for systems with measurement packet dropping. IEEE Trans Autom Control 57(5):1248–1253MathSciNetCrossRefGoogle Scholar
  95. 95.
    Zhou Y, Li JX (2009) Quantized measurement fusion for target tracking in wireless sensor networks. In: In the joint 48th IEEE conference on decision and control and the 28th Chinese control conference, Shanghai, pp 6835–6840Google Scholar
  96. 96.
    Zhu Y, Song E, Zhou J, You Z (2005) Optimal dimensionality reduction of sensor data in multisensor estimation fusion. IEEE Trans Signal Process 53(3):1631–1639MathSciNetGoogle Scholar
  97. 97.
    Zhu H, Schizas ID, Giannakis GB (2009) Power-efficient dimensionality reduction for distributed channel aware Kalman tracking using WSNs. IEEE Trans Signal Process 57(8):3193–3207MathSciNetCrossRefGoogle Scholar

Copyright information

© Science Press, Beijing and Springer Science+Business Media Singapore 2016

Authors and Affiliations

  • Wen-An Zhang
    • 1
  • Bo Chen
    • 1
  • Haiyu Song
    • 2
  • Li Yu
    • 3
  1. 1.Department of AutomationZhejiang University of TechnologyHangzhouChina
  2. 2.Zhejiang Uni. of Finance & EconomicsHangzhouChina
  3. 3.Zhejiang University of TechnologyHangzhouChina

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