Robust global motion estimation for video security based on improved k-means clustering

Adaptive Control & Signal

Yang

2019

230

117

This paper considers the state estimation problem of bilinear systems in the presence of disturbances. The standard Kalman filter is recognized as the best state estimator for linear systems, but it is not applicable for bilinear systems.It is well known that the extended Kalman filter (EKF) is proposed based on the Taylor expansion to linearize the nonlinear model. In this paper, we show that the EKF method is not suitable for bilinear systems because the linearization method for bilinear systems cannot describe the behavior of the considered system. Therefore, this paper proposes a state filtering method for the single-input-single-output bilinear systems by minimizing the covariance matrix of the state estimation errors. Moreover, the state estimation algorithm is extended to multiple-input-multiple-output bilinear systems. The performance analysis indicates that the state estimates can track the true states. Finally, the numerical examples illustrate the specific performance of the proposed method.

Section: Resultsmentioning

confidence: 99%

“…The steps of computing the state estimateR k in (20)- (22) and (52)- (53) are listed in the following. The steps of computing the state estimateR k in (20)- (22) and (52)- (53) are listed in the following.…”

Section: Theorem 1 For the Bilinear System In (1)-(2) And The Bilinementioning

confidence: 99%

State estimation for bilinear systems through minimizing the covariance matrix of the state estimation errors

Xiao

Adaptive Control & Signal

Yang

2019

230

117

“…the forgetting factor BSO-HMISG algorithm (BSO-FF-HMISG) algorithm is formulated to improve the convergence speed and the parameter estimation accuracy of the BSO-HMISG algorithm. The proposed state and parameter estimation algorithms for bilinear systems can combine other estimation algorithms 45,46 and the mathematical tools [47][48][49][50][51] and strategies [52][53][54][55][56] to explore new identification methods of other linear, bilinear, and nonlinear systems with colored noises [57][58][59][60][61] and can be applied to other fields such as information processing [62][63][64][65][66] and communication. [67][68][69][70][71] Remark 6.…”

Section: The Hmisg Algorithmmentioning

confidence: 99%

Recursive parameter estimation methods and convergence analysis for a special class of nonlinear systems

Xiao

Intl J Robust & Nonlinear

2019

120

Summary This paper is concerned with the joint estimation of states and parameters of a special class of nonlinear systems, ie, bilinear systems. The key is to investigate new estimation methods for interactive state and parameter estimation of the considered system based on the interactive estimation theory. Because the system states are unknown, a bilinear state observer is established based on the Kalman filtering principle. Then, the unavailable states are updated by the state observer outputs recursively. Once the state estimates are obtained, the bilinear state observer–based hierarchical stochastic gradient algorithm is developed by using the gradient search. For the purpose of improving the convergence rate and the parameter estimation accuracy, a bilinear state observer–based hierarchical multi‐innovation stochastic gradient algorithm is proposed by expanding a scalar innovation to an innovation vector. The convergence analysis indicates that the parameter estimates can converge to their true values. The numerical example illustrates the effectiveness of the proposed algorithms.

“…principle is an effective method to improve the accuracy of the identification algorithms. [58][59][60][61][62][63][64][65][66][67] [58][59][60][61][62][63][64][65][66][67] …”

Section: M(z)y(l)mentioning

confidence: 99%

Maximum likelihood gradient identification for multivariate equation‐error moving average systems using the multi‐innovation theory

Liu

Adaptive Control & Signal

Hayat

2019

For the multivariate equation-error moving average system, a multivariate maximum likelihood multi-innovation extended stochastic gradient (M-ML-MIESG) algorithm is delivered. The key is to decompose the system into several regressive identification subsystems according to the number of the system outputs. Then, a multivariate maximum likelihood extended stochastic gradient algorithm is presented to estimate the parameters of these subsystems. The M-ML-MIESG algorithm has higher parameter estimation accuracy than the multivariate extended stochastic gradient algorithm. The simulation examples indicate that the proposed methods work well.