Cubature Kalman Filtering for Continuous-Discrete Systems: Theory and Simulations

Arasaratnam, Ienkaran; Haykin, S.; Hurd, T. R.

doi:10.1109/tsp.2010.2056923

Cited by 510 publications

(432 citation statements)

References 28 publications

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“…The extension of the cubature Kalman filter [33] to continuous-discrete filtering problems-using Itô-Taylor series based approximations-has also been recently studied in [4], and its relationship to the classical approach to continuous-discrete filtering was analyzed in [5].…”

Section: Fokker-planck-kolmogorov (Fpk) Partial Differential Equationmentioning

confidence: 99%

“…To compare the performance of the different methods, we use a modified version of the simulation scenario used in [4]. In the original simulation, the dynamic model was a three-dimensional coordinated turn model which can be written in the form:…”

Section: Radar Tracked Coordinate Turn With Multiplicative Noise In Dmentioning

confidence: 99%

“…As discussed in [4], this is a suitable scenario for testing nonlinear estimators, because it has nonlinear dynamic and measurement models, and it is relatively high-dimensional.…”

Section: Radar Tracked Coordinate Turn With Multiplicative Noise In Dmentioning

confidence: 99%

“…Here we consider the particularly difficult case of non-additive noise which is intractable to many existing methods in the field (cf. [4,5]). …”

Section: Introductionmentioning

confidence: 99%

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Gaussian filtering and smoothing for continuous-discrete dynamic systems

2013

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Section: Fokker-planck-kolmogorov (Fpk) Partial Differential Equationmentioning

confidence: 99%

Section: Radar Tracked Coordinate Turn With Multiplicative Noise In Dmentioning

confidence: 99%

“…As discussed in [4], this is a suitable scenario for testing nonlinear estimators, because it has nonlinear dynamic and measurement models, and it is relatively high-dimensional.…”

Section: Radar Tracked Coordinate Turn With Multiplicative Noise In Dmentioning

confidence: 99%

“…Here we consider the particularly difficult case of non-additive noise which is intractable to many existing methods in the field (cf. [4,5]). …”

Section: Introductionmentioning

confidence: 99%

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Gaussian filtering and smoothing for continuous-discrete dynamic systems

2013

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“…Based on the radial-spherical cubature rule, the CKF uses a set of 2n points, where n represents the state-vector dimension, to capture the mean and covariance of the states of a nonlinear system with additive Gaussian noise. It is considered to be more efficient and stable than the UKF [39][40][41]. The CKF has been successfully applied in many fields, such as mobile-station locating [42], moving-target tracking [43] and spacecraft attitude estimation [44].…”

Section: Introductionmentioning

confidence: 99%

State of Charge Estimation of Lithium-Ion Batteries Using an Adaptive Cubature Kalman Filter

et al. 2015

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Accurate state of charge (SOC) estimation is of great significance for a lithium-ion battery to ensure its safe operation and to prevent it from over-charging or over-discharging. However, it is difficult to get an accurate value of SOC since it is an inner sate of a battery cell, which cannot be directly measured. This paper presents an Adaptive Cubature Kalman filter (ACKF)-based SOC estimation algorithm for lithium-ion batteries in electric vehicles. Firstly, the lithium-ion battery is modeled using the second-order resistor-capacitor (RC) equivalent circuit and parameters of the battery model are determined by the forgetting factor least-squares method. Then, the Adaptive Cubature Kalman filter for battery SOC estimation is introduced and the estimated process is presented. Finally, two typical driving cycles, including the Dynamic Stress Test (DST) and New European Driving Cycle (NEDC) are applied to evaluate the performance of the proposed method by comparing with the traditional extended Kalman filter (EKF) and cubature Kalman filter (CKF) algorithms. Experimental results show that the ACKF algorithm has better performance in terms of SOC estimation accuracy, convergence to different initial SOC errors and robustness against voltage measurement noise as compared with the traditional EKF and CKF algorithms. OPEN ACCESSEnergies 2015, 8 5917

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Adaptive control and signal processing literature survey (No. 22)

2011

Adaptive Control & Signal

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In order to keep readers up-to-date, the journal will contain a list of recently published journal articles relevant to the fields of adaptive control and signal processing six times a year. This list is compiled from a wide range of journals. Please note that even though the list has been broadly categorized these classifications are by no means strict, and are there to assist the reader. Please also note that inclusion in the list is not an endorsement of a paper's quality. If you have any suggestions or comments, please e-mail: Erfu Yang at erfu.yang@eee.strath.ac.uk 1. ADAPTIVE SYSTEMS Battistelli G, Mosca E, Safonov M, Tesi P. Stability of unfalsified adaptive switching control in noisy environments. IEEE Transactions on Automatic Control 2010; 55(10):2424-2429. Chen B, Liu X, Liu K, Lin C. Fuzzy-approximationbased adaptive control of strict-feedback nonlinear systems with time delays. IEEE Transactions on Fuzzy Systems 2010; 18(5):883-892. Chen X, Hisayama T, Su C-Y. Adaptive control for uncertain continuous-time systems using implicit inversion of Prandtl-Ishlinskii hysteresis representation. IEEE Transactions on Automatic Control 2010; 55(10):2357-2363. das Sharma K, Chatterjee A, Rakshit A. Design of a hybrid stable adaptive fuzzy controller employing Lyapunov theory and harmony search algorithm. H-S, Yin B-Q. Adaptive optimisation of timeout policy for dynamic power management based on semi-Markov control processes. IET Control Theory and Applications 2010; 4(10):1945-1958. Mirkin B, Gutman P-O. Robust adaptive outputfeedback tracking for a class of nonlinear timedelayed plants. IEEE Transactions on Automatic Control 2010; 55(10):2418-2424. Montazeri A, Poshtan J. A computationally efficient adaptive IIR solution to active noise and vibration control systems. IEEE Transactions on Automatic Control 2010; 55(11):2671-2676. Tenno R. Dual adaptive controls for linear system with unknown constant parameters. International Journal of Control 2010; 83(11):2232-2240. Tong S, Liu C, Li Y. Fuzzy-adaptive decentralized output-feedback control for large-scale nonlinear systems with dynamical uncertainties. IEEE Transactions on Fuzzy Systems 2010; 18(5):845-861. Wang C, Lin Y. Adaptive dynamic surface control for linear multivariable systems. Automatica 2010; 46(10):1703-1711. Wang L, de Lamare R. Adaptive constrained constant modulus algorithm based on auxiliary vector filtering for beamforming. IEEE Transactions on Signal Processing 2010; 58(10):5410-5415. Wang M, Ge S, Hong K-S. Approximation-based adaptive tracking control of pure-feedback nonlinear systems with multiple unknown time-varying delays. robust adaptive beamformer based on worst-case semi-definite programming. IEEE Transactions on Signal Processing 2010; 58(11):5914-5919. Zhang Z, Xu S, Chu Y. Adaptive stabilisation for a class of non-linear state time-varying delay systems with unknown time-delay bound. adaptive cooperation diversity scheme with bestrelay selection in cognitive radio networks. IEEE Transactions on Signal Processing 2010; 58(10):5438...

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Cubature Kalman Filtering for Continuous-Discrete Systems: Theory and Simulations

Cited by 510 publications

References 28 publications

Gaussian filtering and smoothing for continuous-discrete dynamic systems

Gaussian filtering and smoothing for continuous-discrete dynamic systems

State of Charge Estimation of Lithium-Ion Batteries Using an Adaptive Cubature Kalman Filter

Adaptive control and signal processing literature survey (No. 22)

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