Academic literature on the topic 'Kalman filtering with intermittent observations'
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Journal articles on the topic "Kalman filtering with intermittent observations"
Sinopoli, B., L. Schenato, M. Franceschetti, K. Poolla, M. I. Jordan, and S. S. Sastry. "Kalman Filtering With Intermittent Observations." IEEE Transactions on Automatic Control 49, no. 9 (September 2004): 1453–64. http://dx.doi.org/10.1109/tac.2004.834121.
Full textPlarre, Kurt, and Francesco Bullo. "On Kalman Filtering for Detectable Systems With Intermittent Observations." IEEE Transactions on Automatic Control 54, no. 2 (February 2009): 386–90. http://dx.doi.org/10.1109/tac.2008.2008347.
Full textChen, Pengpeng, Honglu Ma, Shouwan Gao, and Yan Huang. "Modified Extended Kalman Filtering for Tracking with Insufficient and Intermittent Observations." Mathematical Problems in Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/981727.
Full textYilin Mo and Bruno Sinopoli. "Kalman Filtering With Intermittent Observations: Tail Distribution and Critical Value." IEEE Transactions on Automatic Control 57, no. 3 (March 2012): 677–89. http://dx.doi.org/10.1109/tac.2011.2166309.
Full textBattilotti, Stefano, Filippo Cacace, Massimiliano d’Angelo, Alfredo Germani, and Bruno Sinopoli. "Kalman-like filtering with intermittent observations and non-Gaussian noise." IFAC-PapersOnLine 52, no. 20 (2019): 61–66. http://dx.doi.org/10.1016/j.ifacol.2019.12.127.
Full textZhong, Yigen, and Yonggui Liu. "Flexible optimal Kalman filtering in wireless sensor networks with intermittent observations." Journal of the Franklin Institute 358, no. 9 (June 2021): 5073–88. http://dx.doi.org/10.1016/j.jfranklin.2021.03.025.
Full textKar, S., B. Sinopoli, and J. M. F. Moura. "Kalman Filtering With Intermittent Observations: Weak Convergence to a Stationary Distribution." IEEE Transactions on Automatic Control 57, no. 2 (February 2012): 405–20. http://dx.doi.org/10.1109/tac.2011.2161834.
Full textMo, Yilin, and Bruno Sinopoli. "Kalman Filtering with Intermittent Observations: Critical Value for Second Order System." IFAC Proceedings Volumes 44, no. 1 (January 2011): 6592–97. http://dx.doi.org/10.3182/20110828-6-it-1002.03731.
Full textRohr, Eduardo Rath, Damian Marelli, and Minyue Fu. "Kalman Filtering With Intermittent Observations: On the Boundedness of the Expected Error Covariance." IEEE Transactions on Automatic Control 59, no. 10 (October 2014): 2724–38. http://dx.doi.org/10.1109/tac.2014.2328183.
Full textCensi, Andrea. "Kalman Filtering With Intermittent Observations: Convergence for Semi-Markov Chains and an Intrinsic Performance Measure." IEEE Transactions on Automatic Control 56, no. 2 (February 2011): 376–81. http://dx.doi.org/10.1109/tac.2010.2097350.
Full textDissertations / Theses on the topic "Kalman filtering with intermittent observations"
Pistorius, Morné. "Measurement correlation in a target tracking system using range and bearing observations /." Link to the onlne version, 2006. http://hdl.handle.net/10019/1162.
Full textPistorius, Morne. "Measurement correlation in a target tracking system using range and bearing observations." Thesis, Stellenbosch : University of Stellenbosch, 2006. http://hdl.handle.net/10019.1/2006.
Full textIn this work we present a novel method to do measurement correlation between target observations made by two ormore radar systems. Some of the most common radar sensors available are those measuring only range (distance to the target) and bearing (azimuth angle). We use these measurements to determine the correlation between two di¤erent sensors observing the same target. As a by-product of the correlation algorithm, we nd a way to estimate the target height for a target observed by at least two radar sensors. The correlation method is expounded upon, where we discuss measurement correlation for moving targets. Targets are tracked using a Kalman Filter, and correlation is done between new observations and existing target tracks. Finally, the correlation algorithm is implemented in an interactive 3D computer simulation. Results obtained indicate a high success rate, with false correlations only obtained where sensor accuracy is the limiting factor.
BenDor, Jonathan, and J. D. Baker. "Processing Real-Time Telemetry with Multiple Embedded Processors." International Foundation for Telemetering, 1994. http://hdl.handle.net/10150/611671.
Full textThis paper describes a system in which multiple embedded processors are used for real-time processing of telemetry streams from satellites and radars. Embedded EPC-5 modules are plugged into VME slots in a Loral System 550. Telemetry streams are acquired and decommutated by the System 550, and selected parameters are packetized and appended to a mailbox which resides in VME memory. A Windows-based program continuously fetches packets from the mailbox, processes the data, writes to log files, displays processing results on screen, and sends messages via a modem connected to a serial port.
Rohr, Eduardo Rath. "State estimation for networked control systems with intermittent data transmission." Thesis, 2012. http://hdl.handle.net/1959.13/936847.
Full textClassic control theory relies on the assumption that sensors, estimators, controllers, and actuators communicate through transparent links. When the communication between these elements is subject to effects like quantisation, delay, and loss of packets, the links can no longer be considered transparent and the basic theory must be revisited. These effects are typically present in systems that use digital wireless communication to share information amongst the different components of the system. This thesis helps to answer the question of how the loss of packets containing measurements from a system affects the performance of its state estimator. In particular, due to its wide use and optimality, the Kalman filter is the state estimator studied. The natural way to evaluate the performance of an estimator is to study the covariance of its estimation error. When measurements are subject to random packet loss, the estimation error covariance does not converge to a steady state, but becomes a random variable whose statistical properties are studied in this thesis. The main contribution of this thesis is the development of a necessary condition and a sufficient condition, having only a trivial gap between them, to determine whether the asymptotic expected value of the estimation error covariance is bounded or not. The conditions apply for a fairly general class of systems and packet drop models, extending previous results and creating a unified approach. Other contributions include bounds on the expected value and bounds on the cumulative distribution function of the estimation error covariance. In both cases, numeric methods to obtain a sequence of upper and lower bounds that can be made arbitrarily tight at the expense of increased computational effort are presented. A detailed example showing an important application of such bounds is also presented. The theoretical tools developed here contribute to the area known as networked control systems, which in turn provides guidance for the design and implementation of network protocols, devices, and integrated systems benefiting from wireless technologies.
Kuo, Chin-Ting, and 郭晉廷. "Green Kalman Filtering with Opportunistically Scheduled Observations." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/33428391621876853132.
Full text國立交通大學
電控工程研究所
104
In this thesis, we study the state estimation problem of a wireless linear dynamic system in which the Kalman filter receives the observation signals transmitted by sensors over wireless fading channels. Since the transmitted observations would be severely weakened while the wireless channels are in deep fade, we propose a threshold-based opportunistic scheduling scheme for the sensors in order to make the signals received by the Kalman filter not fairly weak. Under the assumption of perfect channel state information (CSI) on the sensor side, the stability conditions of Kalman filtering with opportunistically scheduled observations are first derived in the single-sensor and multi-sensor cases, which characterize the fundamental limits between the scheduling threshold and the system matrices of the linear dynamical system. Then we derive the stability conditions for imperfect intermittent CSI on the sensor side and they can be significantly improved by exploiting the statistics of the channel gain. Finally the energy efficiency of the proposed opportunistic scheduling scheme is studied and we show that the maximum energy efficiency can be achieved by utilizing the maximum scheduling threshold which satisfies the stability condition.
Book chapters on the topic "Kalman filtering with intermittent observations"
"4. Continuous States and Observations and Kalman Filtering." In Hidden Markov Models and Dynamical Systems, 59–72. Society for Industrial and Applied Mathematics, 2008. http://dx.doi.org/10.1137/1.9780898717747.ch4.
Full textConference papers on the topic "Kalman filtering with intermittent observations"
Shen, Xiaotong, Daniela Rus, and Marcelo H. Ang. "Bounds for Kalman filtering with intermittent observations." In 2015 European Control Conference (ECC). IEEE, 2015. http://dx.doi.org/10.1109/ecc.2015.7330968.
Full textSui, Tianju, Keyou You, and Minyue Fu. "Kalman filtering with intermittent observations using measurements coding." In 2013 10th IEEE International Conference on Control and Automation (ICCA). IEEE, 2013. http://dx.doi.org/10.1109/icca.2013.6565001.
Full textWu, Junfeng, Guodong Shi, and Karl Henrik Johansson. "Probabilistic convergence of Kalman filtering with nonstationary intermittent observations." In 2014 IEEE 53rd Annual Conference on Decision and Control (CDC). IEEE, 2014. http://dx.doi.org/10.1109/cdc.2014.7039978.
Full textRohr, Eduardo, Damian Marelli, and Minyue Fu. "Kalman filtering with intermittent observations: Bounds on the error covariance distribution." In 2011 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC 2011). IEEE, 2011. http://dx.doi.org/10.1109/cdc.2011.6160565.
Full textRohr, Eduardo, Damian Marelli, and Minyue Fu. "Kalman filtering for a class of degenerate systems with intermittent observations." In 2011 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC 2011). IEEE, 2011. http://dx.doi.org/10.1109/cdc.2011.6161467.
Full textMo, Yilin, and Bruno Sinopoli. "A characterization of the critical value for Kalman filtering with intermittent observations." In 2008 47th IEEE Conference on Decision and Control. IEEE, 2008. http://dx.doi.org/10.1109/cdc.2008.4739119.
Full textVakili, Ali, and Babak Hassibi. "On the steady-state performance of Kalman filtering with intermittent observations for stable systems." In 2009 Joint 48th IEEE Conference on Decision and Control (CDC) and 28th Chinese Control Conference (CCC). IEEE, 2009. http://dx.doi.org/10.1109/cdc.2009.5400386.
Full textCensi, Andrea. "On the performance of Kalman filtering with intermittent observations: A geometric approach with fractals." In 2009 American Control Conference. IEEE, 2009. http://dx.doi.org/10.1109/acc.2009.5159869.
Full textElamin, Khalid Abd El Mageed Hag, and Mirghani Fath Elrahman Taha. "On the steady-state error covariance matrix of Kalman filtering with intermittent observations in the presence of correlated noises at the same time." In 2013 International Conference on Computing, Electrical and Electronics Engineering (ICCEEE). IEEE, 2013. http://dx.doi.org/10.1109/icceee.2013.6633901.
Full textRohr, Eduardo, Damian Marelli, and Minyue Fu. "Stability of Kalman filters subject to intermittent observations." In 2013 IEEE 52nd Annual Conference on Decision and Control (CDC). IEEE, 2013. http://dx.doi.org/10.1109/cdc.2013.6761129.
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