A simple algorithm is presented to correct the scanning horizon sensor measurement for Earth oblateness in satellite attitude estimation. An interative correction algorithm is presented by solving a scalar equation of...
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A simple algorithm is presented to correct the scanning horizon sensor measurement for Earth oblateness in satellite attitude estimation. An interative correction algorithm is presented by solving a scalar equation of the phase angle of the horizon crossing point. A first-order correction algorithm is also derived, based on the fact that the flattening coefficient of the oblate Earth is small. Compared with the methods in the open literature, the algorithms presented here achieve relatively high accuracy with simple computation. Simulation results show that the accuracy of the first-order correction algorithm is better than 0.01 deg for a LEO satellite. (AIAA)
In this paper, we study a class of random nonlinear variational inequalities in Banach spaces, By applying a random minimax inequality obtained by Tarafdar and Yuan. some existence uniqueness theorems of random soluti...
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In this paper, we study a class of random nonlinear variational inequalities in Banach spaces, By applying a random minimax inequality obtained by Tarafdar and Yuan. some existence uniqueness theorems of random solutions for the random nonlinear variational inequalities are proved. Next, by applying the random auxiliary problem technique, we suggest an innovative iterative algorithm to compute the random approximate solutions of the random nonlinear variational inequality. Finally, the convergence criteria is also discussed.
Discusses a method of combining adaptive grid refinement and coarsening with the multi-grid algorithm for efficient calculation of the steady-state solution for compressible flow. Application of the method to the Eule...
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Discusses a method of combining adaptive grid refinement and coarsening with the multi-grid algorithm for efficient calculation of the steady-state solution for compressible flow. Application of the method to the Euler equation; Numerical solution and adaptation; Multi-grid iteration.
The adaptive two-state filter (ATSF) for a class of nonlinear systems made up of a linear dynamic model and a nonlinear measurement model is shown to be capable of determining the mean and covariance of measurement no...
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The adaptive two-state filter (ATSF) for a class of nonlinear systems made up of a linear dynamic model and a nonlinear measurement model is shown to be capable of determining the mean and covariance of measurement noise online, rendering it able to perform well in cases where the statistical properties are unknown a priori. Application of the ATSF to the bearings-only measurement problem shows it to dramatically outperform the extended and extended-adaptive Kalman filters. (AIAA)
In this paper, we propose a new class of iterative methods for solving generalized monotone mixed variational inequalities using the resolvent operator technique. (C) 1999 Elsevier Science Ltd. All rights reserved.
In this paper, we propose a new class of iterative methods for solving generalized monotone mixed variational inequalities using the resolvent operator technique. (C) 1999 Elsevier Science Ltd. All rights reserved.
In this paper we construct a new iterative algorithm for solving a new class of nonlinear variational inequalities with set-valued mapping, and give some convergence analysis of iterative sequences generated by the al...
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In this paper we construct a new iterative algorithm for solving a new class of nonlinear variational inequalities with set-valued mapping, and give some convergence analysis of iterative sequences generated by the algorithm.
The critical issue in extending the iterative algorithm widely used for single-channel restoration to the multichannel case is the formidable computational requirements due to the huge matrix operations in the restora...
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The critical issue in extending the iterative algorithm widely used for single-channel restoration to the multichannel case is the formidable computational requirements due to the huge matrix operations in the restoration algorithm. This paper explores the possibility of combining the multichannel constrained-least-square (CLS) algorithm with the iterative algorithm for restoring multichannel images. A detailed analysis of the properties of the matrices in the formula and an effective method implemented in space domain are presented. This makes the iterative CLS algorithm feasible for multichannel restoration problems. Experiments are performed to evaluate the proposed iterative multichannel CLS algorithm. The results of the related independent single-channel algorithm are also presented for comparison.
The estimation algorithm developed offers an alternative to standard recursive nonlinear estimators such as the extended Kalman filter and the iterated extended Kalman filter. The algorithm, which is developed from a ...
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The estimation algorithm developed offers an alternative to standard recursive nonlinear estimators such as the extended Kalman filter and the iterated extended Kalman filter. The algorithm, which is developed from a quadratic cost function basis, splits the problem of cost function minimization into a linear first step and a nonlinear second step by defining new first-step states that are nonlinear combinations of the unknown states. Estimates of the first-step states are obtained by minimizing the first-step cost function using a Kalman filter formulation. Estimates of the unknown, or second-step, states are obtained by minimizing the second-step cost function using an iterative Gauss-Newton algorithm. The two-step estimator is shown to be optimal for static problems in which the time variation of the measurement equation can be separated from the unknowns. This method is then generalized by approximating the nonlinearity as a perturbation of the dynamic update, while keeping the measurement cost function the same, In contrast, the extended Kalman filter and the iterated extended Kalman filter linearize the measurement cost function, resulting in suboptimal estimates. Two example applications confirm these analytical results.
作者：
Lee, SJLee, CWSK Telecom
Cent R&D Ctr Core Technol Dev Lab Taejon 305348 South Korea Seoul Natl Univ
Sch Elect Engn Inst New Media & Commun Seoul 151 South Korea
For optimal entropy coding of multiple sources, the encoder and the decoder have to retain a separate Huffman table for each source. In many practical cases, however, available memory is usually restricted and therefo...
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For optimal entropy coding of multiple sources, the encoder and the decoder have to retain a separate Huffman table for each source. In many practical cases, however, available memory is usually restricted and therefore it is necessary for some sources to share a Huffman table. Recently, we developed an iterative algorithm, which leads to locally optimal sharing of Huffman tables (Lee et al., 1995). In this paper, we examine the iterative algorithm in detail and present some modification methods which improve the sharing performance and reduce computational complexity. First, considering that the iterative algorithm provides only locally optimum, pie introduce an unused table processing method and two initialization methods, splitting and merging, for the iterative algorithm.,And fixed-length coding is introduced for the encoding of some sources, which provides considerable performance improvement in case only a small number of Huffman tables are used. In addition, we present a simple approximation method of codelengths, which reduces the computational complexity of the sharing algorithm with little performance degradation. Simulations show that performance can be further improved with these modifications. For practical applications, we also present a search method for fast determining the number of Huffman tables and the Huffman table size, which, under a given memory constraint, minimize average bit-rate. The proposed sharing method can be widely applied to many high-order entropy coding systems with memory constraints. (C) 1998 Elsevier Science B.V. All rights reserved.
This correspondence is concerned with the problem of estimating the parameters of constant amplitude chirp signals embedded in noise. An estimation algorithm based on a simple iterative approach is proposed whose main...
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This correspondence is concerned with the problem of estimating the parameters of constant amplitude chirp signals embedded in noise. An estimation algorithm based on a simple iterative approach is proposed whose main characteristics include accuracy, reduction in error propagation effect, and operation over a wide range of phase parameter values. The estimation is quite robust against additive noise and is shown to achieve the Cramer-Rao lower bound even at relatively low signal-to-noise ratios. Simulation results are presented that justify the viability of the new algorithm and demonstrate its better performance as compared with the existing techniques.
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