State estimation for asynchronous multirate multisensor nonlinear dynamic systems with missing measurements. (English) Zbl 1263.93218
Summary: This paper is concerned with the state estimation for a kind of nonlinear multirate multisensor asynchronous sampling dynamic system. There are \(N\) sensors observing a single target independently at multiple sampling rates, and the dynamic system is formulated at the highest sampling rate. Observations are obtained asynchronously, and each sensor may lose data randomly at a certain probability. The fused state estimate is generated using multiscale system theory and the modified sigma point Kalman filter. It is shown that our main results improve and extend the existing sigma point Kalman filter for which the samples are obtained multirate nonuniformly. Measurements missing Bernoulli distribution could also be allowed in this paper. Finally, the feasibility and efficiency of the presented algorithm is illustrated by a numerical simulation example.
MSC:
| 93E10 | Estimation and detection in stochastic control theory |
| 93E11 | Filtering in stochastic control theory |
| 93C41 | Control/observation systems with incomplete information |
| 93C55 | Discrete-time control/observation systems |
| 93C57 | Sampled-data control/observation systems |
| 93C10 | Nonlinear systems in control theory |
Keywords:
state estimation; data fusion; nonlinear system; asynchronous; multirate; nonlinear multirate multisensor asynchronous sampling; sensor network; sigma point Kalman filter; multiscale system theory; highest sampling rate; Bernoulli distributionReferences:
| [1] | Zhang, Delay-dependent state estimation for time-varying delayed neural networks, International Journal of Innovative Computing, Information and Control 5 (6) pp 1711– (2009) |
| [2] | Liao, A delay-dependent approach to design state estimator for discrete stochastic recurrent neural network with interval time-varying delays, ICIC Express Letters 3 (3A) pp 465– (2009) |
| [3] | Liu, Asynchronously-coupled consensus of second-order dynamic agents with communication delay, International Journal of Innovative Computing, Information and Control 6 (11) pp 5035– (2010) |
| [4] | Wu, State estimation and sliding mode control of Markovian jump singular systems, IEEE Transactions on Automatic Control 55 (5) pp 1213– (2010) · Zbl 1368.93696 · doi:10.1109/TAC.2010.2042234 |
| [5] | Zhang, Stability, l2-gain and asynchronous Hcontrol of discrete-time switched systems with average dwell time, IEEE Transaction on Automatic Control 54 (9) pp 2193– (2009) |
| [6] | Wu, Adaptive variable structure state estimation for uncertain systems with persistently bounded disturbances, International Journal of Robust and Nonlinear Control 20 (17) pp 2003– (2010) · Zbl 1202.93016 · doi:10.1002/rnc.1567 |
| [7] | Fu, Kalman Filter Theory and its Application in Navigation System (2010) |
| [8] | Xiang L Nonlinear filtering method and its application in navigation system Dissertation for the Doctoral Degree in Engineering 2009 |
| [9] | Cui, A comparison of nonlinear filtering approaches with an application to ground target tracking, Signal Processing 85 (8) pp 1469– (2005) · Zbl 1160.94325 · doi:10.1016/j.sigpro.2005.01.010 |
| [10] | Daum, Nonlinear filters: beyond the Kalman filter, IEEE A&E Systems Magazine 20 (8) pp 57– (2005) · doi:10.1109/MAES.2005.1499276 |
| [11] | Kalman, A new approach to linear filtering and prediction theory, Journal of Basic Engineering 82D pp 35– (1960) · doi:10.1115/1.3662552 |
| [12] | Kalman, New results in linear filtering and prediction theory, Journal of Basic Engineering 83D pp 55– (1961) |
| [13] | Teixeira, Flight path reconstruction - A comparison of nonlinear Kalman filter and smoother algorithms, Aerospace Science and Technology 15 (1) pp 60– (2011) · doi:10.1016/j.ast.2010.07.005 |
| [14] | Merwe, AIAA Guidance, Navigation, and Control Conference and Exhibit pp 1– (2004) |
| [15] | Julier, Proceedings of the SPIE - The International Society for Optical Engineering, Signal Processing, Sensor Fusion, and Target Recognition 3068 pp 182– (1997) |
| [16] | Julier, Unscented filtering and nonlinear estimation, Proceedings of the IEEE 92 (3) pp 401– (2004) · doi:10.1109/JPROC.2003.823141 |
| [17] | Henk APB Edwin AB Particle filtering for stochastic hybrid systems 43rd IEEE Conference on Decision and Control 2004 3221 3226 |
| [18] | Chowdhary, Aerodynamic parameter estimation from flight data applying extended and unscented Kalman filter, Aerospace Science and Technology 14 (2) pp 106– (2010) · doi:10.1016/j.ast.2009.10.003 |
| [19] | Lee, Adaptive sigma point filtering for state and parameter estimation, AIAA 5101 pp 1– (2004) |
| [20] | Xiong, Performance evaluation of UKF-based nonlinear filtering, Automatica 42 (2) pp 261– (2006) · Zbl 1103.93045 · doi:10.1016/j.automatica.2005.10.004 |
| [21] | Hao YL Xiong ZL Sun F Wang XG Comparison of unscented Kalman filters Proceedings of the 2007 IEEE International Conference on Mechatronics and Automation 2007 895 899 |
| [22] | Wu, Comments on performance evaluation of UKF-based nonlinear filtering, Automatica 43 (3) pp 567– (2007) · Zbl 1137.93417 · doi:10.1016/j.automatica.2006.08.029 |
| [23] | Lee, Nonlinear estimation and multiple sensor fusion using unscented information filtering, IEEE Signal Processing Letters 15 pp 861– (2008) · doi:10.1109/LSP.2008.2005447 |
| [24] | Rambabu, Applying the unscented Kalman filter for nonlinear state estimation, Journal of Process Control 18 (7-8) pp 753– (2008) · doi:10.1016/j.jprocont.2007.11.004 |
| [25] | Hassan, State estimation of constrained nonlinear discrete-time dynamical systems, International Journal of Innovative Computing, Information and Control 6 (10) pp 4449– (2010) |
| [26] | Liu, Fuzzy Hfiltering for nonlinear stochastic systems with missing measurements, ICIC Express Letters 3 (3B) pp 739– (2009) |
| [27] | Yan, An asynchronous multirate multisensor information fusion algorithm, IEEE Transactions on Aerospace and Electronic Systems 43 (3) pp 1135– (2007) · doi:10.1109/TAES.2007.4383603 |
| [28] | Gao, Multi-sensor optimal data fusion for INS/GPS/SAR integrated navigation system, Aerospace Science and Technology 13 (4-5) pp 232– (2009) · doi:10.1016/j.ast.2009.04.006 |
| [29] | Hong, Multi-resolutional distributed filtering, IEEE Transactions on Automatic Control 39 (4) pp 853– (1994) · Zbl 0807.93063 · doi:10.1109/9.286269 |
| [30] | Yan, State estimation for asynchronous multirate multisensor dynamic systems with missing measurements, IET Signal Processing 4 (6) pp 728– (2010) · doi:10.1049/iet-spr.2009.0215 |
| [31] | Censi, Kalman filtering with intermittent observations: convergence for Semi-Markov chains and an intrinsic performance measure, IEEE Transactions on Automatic Control 56 (2) pp 376– (2011) · Zbl 1368.93707 · doi:10.1109/TAC.2010.2097350 |
| [32] | Kar, Kalman filtering with intermittent observations: weak convergence to a stationary distribution, IEEE Transactions on Automatic Control PP (99) pp 1– (2011) |
| [33] | Kluge, Stochastic stability of the extended Kalman filter with intermittent observations, IEEE Transactions on Automatic Control 55 (2) pp 514– (2010) · Zbl 1368.93717 · doi:10.1109/TAC.2009.2037467 |
| [34] | Lee JW Lee SD Data fusion based state estimation of nonlinear discrete systems Proceedings of the 39th IEEE Conference on Decision and Control 2000 310 315 |
| [35] | Yan, Study of asynchronous multirate data fusion estimation algorithm based on nonlinear systems, Acta Electronica Sinica 37 (12) pp 2735– (2009) |
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