Optimal Distributed Filters for Robust Estate Spate Estimation (I)

Resumen: The nature of Wireless sensor networks (WNS) allows the implementation of a distributed estimation process which has proven to be a more robust solution than individual estimation. Furthermore, filters of an unbiased finite impulse response nature have proven themselves as a robust alternative for WSNs applications, which are often deployed in harsh environments, where electromagnetic interference, damaged sensors, or the landscape itself cause the network to suffer from faulty links and missing data. In this paper, we present a distributed unbiased finite impulse response (dUFIR) algorithm for optimal consensus on estimates in WSNs. We compare the performance of dUFIR filter against a distributed Kalman filter (dKF) and prove with simulations that better robustness is achieved by the dUFIR filter against data loss, unknown noise statistics and faulty measurements.

¿Cómo citar?

Miguel Vazquez-Olguin, Yuriy S. Shmaliy, Oscar G. Ibarra-Manzano & Sandra Marquez-Figueroa. Optimal Distributed Filters for Robust Estate Spate Estimation (I). Memorias del Congreso Nacional de Control Automático, pp. 370-375, 2021.

Palabras clave

Robust estimation, Optimal estimation, WSN

• Akyildiz, I., Su, W., Sankarasubramaniam, Y., and Cayirci, E. (2002). Wireless sensor networks: a survey. Computer Networks, 38(4), 393–422. doi: 10.1016/s1389-1286(01)00302-4.
• Bai, X., Wang, Z., Zou, L., and Alsaadi, F.E. (2018). Collaborative fusion estimation over wireless sensor networks for monitoring CO 2 concentration in a greenhouse. Information Fusion, 42, 119–126. doi: 10.1016/j.inffus.2017.11.001.
• Carli, R., Chiuso, A., Schenato, L., and Zampieri, S. (2008). Distributed kalman filtering based on consensus strategies. IEEE Journal on Selected Areas in Communications, 26(4), 622–633. doi: 10.1109/jsac.2008.080505.
• Chen, C., Zhu, S., Guan, X., and Shen, X. (2014). Wireless Sensor Networks. Springer Intern. Publ. doi: 10.1007/978-3-319-12379-0.
• Contreras-Gonzalez, J., Ibarra-Manzano, O., and Shmaliy, Y.S. (2013). Clock state estimation with the kalman-like UFIR algorithm via TIE measurement. Measurement, 46(1), 476–483. doi: 10.1016/j.measurement.2012.08.003.
• Cook, D.J., Augusto, J.C., and Jakkula, V.R. (2009). Ambient intelligence: Technologies, applications, and opportunities. Pervasive and Mobile Computing, 5(4), 277–298. doi:10.1016/j.pmcj.2009.04.001.
• Farina, M., Ferrari-Trecate, G., and Scattolini, R. (2010). Distributed moving horizon estimation for linear constrained systems. IEEE Transactions on Automatic Control, 55(11), 2462–2475. doi: 10.1109/tac.2010.2046058.
• Feng Zhao, L.G. (2004). Wireless Sensor Networks: An Information Processing Approach. MORGAN KAUFMANN PUBL INC.
• Hanley, D., Faustino, A.B., Zelman, S.D., Degenhardt, D.A., and Bretl, T. (2017). MagPIE: A dataset for indoor positioning with magnetic anomalies. In 2017 Int. Conf. Indoor Positioning and Indoor Navigation (IPIN). IEEE. doi:10.1109/ipin.2017.8115961.
• Hu, J., Wang, Z., Gao, H., and Stergioulas, L.K. (2012). Extended kalman filtering with stochastic nonlinearities and multiple missing measurements. Automatica, 48(9), 2007–2015. doi: 10.1016/j.automatica.2012.03.027.
• Jazwinski, A.H. (2007). Stochastic Processes and Filtering Theory (Dover Books on Electrical Engineering). Dover Publications. Li, W., Wang, Z., Wei, G., Ma, L., Hu, J., and Ding, D. (2015). A survey on multisensor fusion and consensus filtering for sensor networks. Discrete Dynamics in Nature and Society, 2015, 1–12. doi:10.1155/2015/683701.
• Mahmoud, M.S. and Xia, Y. (2014). Networked Filtering and Fusion in Wireless Sensor Networks. CRC Press.
• Olfati-Saber, R. (2007). Distributed Kalman filtering for sensor networks. In 2007 46th IEEE Conf. on Decision and Control. IEEE. doi:10.1109/cdc.2007.4434303.
• Olfati-Saber, R. and Murray, R. (2004). Consensus problems in networks of agents with switching topology and time-delays. IEEE Transactions on Automatic Control, 49(9), 1520–1533. doi:10.1109/tac.2004.834113.
• Pomarico-Franquiz, J.J. and Shmaliy, Y.S. (2014). Accurate self-localization in RFID tag information grids using FIR filtering. IEEE Trans. Ind. Informat., 10(2), 1317–1326. doi:10.1109/tii.2014.2310952.
• Ramirez-Echeverria, F., Sarr, A., and Shmaliy, Y.S. (2014). Optimal memory for discrete-time FIR filters in state-space. IEEE Transactions on Signal Processing, 62(3), 557–561. doi:10.1109/tsp.2013.2290504.
• Rao, B. and Durrant-Whyte, H. (1991). Fully decentralised algorithm for multisensor Kalman filtering. IEE Proc. D Control Theory and Applic., 138(5), 413. doi:10.1049/ip-d.1991.0057.
• Shmaliy, Y.S. (2012). Suboptimal FIR filtering of nonlinear models in additive white gaussian noise. IEEE Transactions on Signal Processing, 60(10), 5519–5527. doi:10.1109/tsp.2012.2205569.
• Shmaliy, Y.S., Khan, S., and Zhao, S. (2016). Ultimate iterative UFIR filtering algorithm. Measurement, 92, 236–242. doi:10.1016/j.measurement.2016.06.029.
• Shmaliy, Y.S., Khan, S.H., Zhao, S., and Ibarra-Manzano, O. (2017a). General unbiased FIR filter with applications to GPS-based steering of oscillator frequency. IEEE Transactions on Control Systems Technology, 25(3), 1141–1148. doi:10.1109/tcst.2016.2583961.
• Shmaliy, Y.S., Zhao, S., and Ahn, C.K. (2017b). Unbiased finite impluse response filtering: An iterative alternative to kalman filtering ignoring noise and initial conditions. IEEE Control Systems, 37(5), 70–89. doi: 10.1109/mcs.2017.2718830.
• Sinopoli, B., Schenato, L., Franceschetti, M., Poolla, K., Jordan, M., and Sastry, S. (2004). Kalman filtering with intermittent observations. IEEE Transactions on Automatic Control, 49(9), 1453–1464. doi: 10.1109/tac.2004.834121.
• Stankovi´c, S.S., Stankovi´c, M.S., and Stipanovi´c, D.M. (2009). Consensus based overlapping decentralized estimation with missing observations and communication faults. Automatica, 45(6), 1397–1406. doi: 10.1016/j.automatica.2009.02.014.
• Uribe-Murcia, K., Shmaliy, Y.S., and Andrade-Lucio, J.A. (2018). UFIR filtering for GPS-based tracking over WSNs with delayed and missing data. Journal of Electrical and Computer Engineering, 2018, 1–9. doi: 10.1155/2018/7456010.
• Vazquez-Olguin, M., Shmaliy, Y.S., Ahn, C.K., and Ibarra-Manzano, O.G. (2017a). Blind robust estimation with missing data for smart sensors using UFIR filtering. IEEE Sensors Journal, 17(6), 1819–1827. doi: 10.1109/jsen.2017.2654306.
• Vazquez-Olguin, M., Shmaliy, Y.S., and Ibarra-Manzano, O. (2018). Developing UFIR Filtering with Consensus on Estimates for Distributed Wireless Sensor Networks. WSEAS Trans. Circuits Syst.
• Vazquez-Olguin, M., Shmaliy, Y.S., Ibarra-Manzano, O., and Marquez-Figueroa, S. (2021). Distributed UFIR filtering with applications to environmental monitoring. International Journal of Circuits, Systems and Signal Processing, 15, 349–355. doi:10.46300/9106.2021.15.38.
• Vazquez-Olguin, M., Shmaliy, Y.S., Ibarra-Manzano, O., Munoz-Minjares, J., and Lastre-Dominguez, C. (2019). Object tracking over distributed WSNs with consensus on estimates and missing data. IEEE Access, 7, 39448– 39458. doi:10.1109/access.2019.2905514.
• Vazquez-Olguin, M., Shmaliy, Y.S., and Ibarra-Manzano, O.G. (2017b). Distributed unbiased FIR filtering with average consensus on measurements for WSNs. IEEE Trans. Ind. Informat., 13(3), 1440–1447. doi: 10.1109/tii.2017.2653814.
• Vazquez-Olguin, M., Shmaliy, Y.S., and Ibarra-Manzano, O.G. (2020). Distributed UFIR filtering over WSNs with consensus on estimates. IEEE Transactions on Industrial Informatics, 16(3), 1645–1654. doi: 10.1109/tii.2019.2930649.
• Zhao, S., Shmaliy, Y.S., and Liu, F. (2016). Fast kalmanlike optimal unbiased FIR filtering with applications. IEEE Transactions on Signal Processing, 64(9), 2284– 2297. doi:10.1109/tsp.2016.2516960.