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JACIII Vol.20 No.7 pp. 1060-1064
doi: 10.20965/jaciii.2016.p1060
(2016)

Short Paper:

Exponential-Weighting-Based Maximum Likelihood for Determining Measurement Random Latency Probability in Network Systems

Xiaoxu Wang and Quan Pan

School of Automation, Northwestern Polytechnical University
No. 127, Youyi West Road, Xi’an, Shaanxi 710072, China

Received:
June 7, 2016
Accepted:
September 5, 2016
Online released:
December 20, 2016
Published:
December 20, 2016
Keywords:
nonlinear parameter determination, filter, exponential weighting, maximum multi-step likelihood
Abstract

The standard nonlinear Gaussian approximation (GA) filter used for randomly delayed measurement in network systems, usually assume that the measurement random latency probability (MRLP) is known a priori. However, practically, the MRLP may be unknown or even be time-varying, causing the standard nonlinear GA filter to certainly fail. Motivated by the above situation, this paper is concerned with the application of maximum likelihood based on exponential weighting for adaptively determining the MRLP. Furthermore, an adaptive version of the nonlinear GA filter is proposed for joint state estimation and MRLP determination. Finally, simulation results demonstrate the performance of the new adaptive GA filter compared with the standard one.

References
  1. [1] E. Masazade, M. Fardad, and P. K. Varshney, “Sparsity-Promoting Extended Kalman Filtering for Target Tracking in Wireless Sensor Networks,” IEEE Signal Proc. Letters, Vol.19, No.12, pp. 845-848, 2012.
  2. [2] B. Jia, M. Xin, and Y. Cheng, “Sparse-grid quadrature nonlinear filtering,” Automatica, Vol.48, No.2, pp. 327-341, 2012.
  3. [3] J. T. Horwood and A. B. Poore, “Adaptive Gaussian Sum Filters for Space Surveillance,” IEEE Trans. on Automatic Control, Vol.56, No.8, pp. 1777-1790, 2011.
  4. [4] R. R. Chen, B. Liu, and X. L. Cheng, “Pricing the term structure of inflation risk premia: Theory and evidence from TIPS,” J. of Empirical Finance, Vol.17, No.4, pp. 704-721, 2011.
  5. [5] J. F. Liu, X. Z. Chen, D. M. Peña, and P. D. Christofides, “Iterative distributed model predictive control of nonlinear systems: Handling asynchronous, delayed measurements,” IEEE Trans. on Automatic Control, Vol.57, No.2, pp. 528-534, 2012.
  6. [6] A. Hermoso-Carazo and J. Linares-Pérez, “Extended and unscented filtering algorithms using one-step randomly delayed measurements,” Applied Mathematics and Computation, Vol.190, No.2, pp. 1375-1393, 2007.
  7. [7] A. Hermoso-Carazo and J. Linares-Pérez, “Unscented filtering algorithm using two-step randomly delayed measurements in nonlinear systems,” Applied Mathematical Modelling, Vol.33, No.9, pp. 3705-3717, 2009.
  8. [8] A. Hermoso-Carazo and J. Linares-Pérez, “Unscented filtering from delayed observations with correlated noises,” Mathematical Problems in Engineering, Vol.1, 2009.
  9. [9] X. X. Wang, Y. Liang, Q. Pan, and C. H. Zhao, “Gaussian filter for nonlinear systems with the one-step randomly delayed measurement,” Automatica, Vol.49, No.4, pp. 976-986, 2013.
  10. [10] X. X. Wang, Y. Liang, Q. Pan, and F. Yang, “Gaussian smoothers for nonlinear systems with the one-step randomly delayed measurement,” IEEE Trans. on Automatic Control, Vol.58, No.7, pp. 1828-1835, 2013.
  11. [11] I. Arasaratnam and S. Haykin, “Cubature Kalman filter,” IEEE Trans. on Automatic Control, Vol.54, No.8, pp. 1254-1269, 2009.

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Last updated on May. 26, 2017