Identification algorithms based on H/sub infinity / state-space filtering techinques

Grimble, M.J.; Hashim, Rizwan; Shaked, Uri

doi:10.1109/cdc.1992.371383

Cited by 14 publications

(4 citation statements)

References 1 publication

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“…These results were compared with a Least Square Estimator [4] and an H ∞ identifier [5,6] as benchmark algorithms.…”

Section: Comparisonmentioning

confidence: 99%

LMS-based enhancement for IR images with applications to surveillance

Qidwai

2005

SPIE Proceedings

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In this paper, a new strategy is presented to map the pixel space from infrared (IR) image to the pixel space of optical images. This is done by utilizing the usual technique of system identification using the input-output measurements and trying to fit a parametric model under a given fitness criterion. In this case, the IR image is taken as the input data and a similar image taken simultaneously by a normal CCD camera is taken as the output data. The acquired data are then fitted with an autoregressive moving average (ARMA) model in 1D under the H ∞ criterion which is the most robust and statistically less-dependent fitness criterion. The well known LMS algorithm can reach the H ∞ bounds under certain impositions. By structuring the given data to suite these impositions, the LMS algorithm is used to reach the optimal bound. This implies a faster convergence with better performance. The results have shown remarkable enhancement in the images and appears to be promising for the real-time applications.

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“…These results were compared with a Least Square Estimator [4] and an H ∞ identifier [5,6] as benchmark algorithms.…”

Section: Comparisonmentioning

confidence: 99%

LMS-based enhancement for IR images with applications to surveillance

Qidwai

2005

SPIE Proceedings

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“…If y is the measured output, a Kalman filter for one-step-ahead prediction of Xk+1 has the form 14) and the corresponding one-step-ahead prediction of 8 k+1 is If the probabilistic error-covariance operator for ij is G, then Thus the ARX coefficients that minimize the least-squares one-step-ahead prediction error in Problem 3.3 are the coefficients in the probabilistic minimum-variance prediction of iN+l based on the data yl, y2, ... YN and Ul, U2,... UN and the assumption that the initial state vector zx has zero mean and covariance G, given by (3.22). This G is indeed correct for the steady-state statistics of the state vector zx in (5.11) when T has spectral radius less than 1 and uk and wk are zero-mean stationary white noise sequences (in the probalistic sense) with covariance operators Ru and RIO, respectively.…”

Section: Minimax Estimation and Predictionmentioning

confidence: 99%

“…In [4,14,15], we introduced a new class of parameter estimation problems, in which the estimated parameters are minimizing solutions to minimax problems for quadratic fit-to-data criteria. Whereas the asymptotic parameter estimates produced by least-squares methods are Markov parameters of Kalman filters, the asymptotic parameter estimates produced by the order-recursive minimax problem in [4] are Markov parameters of discrete-time H. filters.…”

Section: Adaptive Minimax Estimation and Filteringmentioning

confidence: 99%

Digital Adaptive and Optimal Control of Distributed Systems

Gibson¹

1993

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“…These results were compared with a Least Square Estimator [5] and an H∞ identifier [6] as benchmark algorithms.…”

Section: Comparisonmentioning

confidence: 99%

Infrared Image Enhancement using Recursive Weighted Least Squares Algorithm based pixel mapping for Surveillance Applications

Qidwai

Qidwai²

Student Conference on Engineering, Sciences and Technology

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In this paper, an algorithmic approach is presented that utilizes the system modeling approach to map the infrared (IR) pixel space to real optical space. This mapping has been performed using the parametric approach with Recursive Weighted Least Squares (RWLS) estimation algorithm. Using the autoregressive (ARX) model structure, the image pixels are mapped from the IR pixel space into normal optical image space, thus enhancing the IR image for improved visual quality.

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Identification algorithms based on H/sub infinity / state-space filtering techinques

Cited by 14 publications

References 1 publication

LMS-based enhancement for IR images with applications to surveillance

LMS-based enhancement for IR images with applications to surveillance

Digital Adaptive and Optimal Control of Distributed Systems

Infrared Image Enhancement using Recursive Weighted Least Squares Algorithm based pixel mapping for Surveillance Applications

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