Results 1 - 10 of 201

LMMSE Estimation Based on Counting Observations

by Rosa Fernández-alcalá, Jesús Navarro-moreno, Juan Carlos Ruiz-molina, Antonia Oya
"... Abstract — The problem of estimating the intensity process of a doubly stochastic Poisson process is analyzed. Using the knowledge of the first and second-order moments of the intensity process, a recursive linear minimum mean-square error estimate is designed. Moreover, an efficient procedure for t ..."
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Abstract — The problem of estimating the intensity process of a doubly stochastic Poisson process is analyzed. Using the knowledge of the first and second-order moments of the intensity process, a recursive linear minimum mean-square error estimate is designed. Moreover, an efficient procedure

Signal LMMSE estimation from multiple samples

by S Aja-Fernández , C Alberola-López , C.-F Westin - in MRI and DT-MRI,” in Proc. MICCAI
"... Abstract. A method to estimate the magnitude MR data from several noisy samples is presented. It is based on the Linear Minimum Mean Squared Error (LMMSE) estimator for the Rician noise model when several scanning repetitions are available. This method gives a closed-form analytical solution that t ..."
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Abstract. A method to estimate the magnitude MR data from several noisy samples is presented. It is based on the Linear Minimum Mean Squared Error (LMMSE) estimator for the Rician noise model when several scanning repetitions are available. This method gives a closed-form analytical solution

DOUBLE SHRINKAGE CORRECTION IN SAMPLE LMMSE ESTIMATION

by Jordi Serra, Montse Nájary
"... The sample linear minimum mean square error (LMMSE) es-timator undergoes high performance degradation in the small sample size regime. Herein a double shrinkage correction is proposed to alleviate this problem. First, an afne transfor-mation of the sample covariance matrix (SCM) is considered within ..."
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The sample linear minimum mean square error (LMMSE) es-timator undergoes high performance degradation in the small sample size regime. Herein a double shrinkage correction is proposed to alleviate this problem. First, an afne transfor-mation of the sample covariance matrix (SCM) is considered

An Encoder-Embedded Video Denoising Filter Based On The Temporal LMMSE Estimator

by Liwei Guo, Oscarc. Au, Mengyao Ma, Zhiqin Liang - Proc. ICME , 2006
"... Noise not only degrades the visual quality of video contents, but also significantly affects the coding efficiency. Based on the temporal linear minimum mean square error (LMMSE) estimator, an innovative denoising filter is proposed in this paper. The proposed filter only requires simple operations ..."
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Noise not only degrades the visual quality of video contents, but also significantly affects the coding efficiency. Based on the temporal linear minimum mean square error (LMMSE) estimator, an innovative denoising filter is proposed in this paper. The proposed filter only requires simple operations

LMMSE Estimation and Interpolation of Continuous-Time Signals from Discrete-Time Samples Using Factor Graphs

by Lukas Bolliger, Hans-Andrea Loeliger, Christian Vogel , 2013
"... The factor graph approach to discrete-time linear Gaussian state space models is well developed. The paper extends this approach to continuous-time linear systems / filters that are driven by white Gaussian noise. By Gaussian message passing, we then obtain MAP / MMSE / LMMSE estimates of the input ..."
Abstract - Cited by 1 (1 self) - Add to MetaCart
The factor graph approach to discrete-time linear Gaussian state space models is well developed. The paper extends this approach to continuous-time linear systems / filters that are driven by white Gaussian noise. By Gaussian message passing, we then obtain MAP / MMSE / LMMSE estimates

A Central Limit Theorem for the SINR at the LMMSE Estimator Output for Large Dimensional Signals

by Abla Kammoun, Malika Kharouf, Walid Hachem, Jamal Najim , 2008
"... This paper is devoted to the performance study of the Linear Minimum Mean Squared Error estimator for multidimensional signals in the large dimension regime. Such an estimator is frequently encountered in wireless communications and in array processing, and the Signal to Interference and Noise Ratio ..."
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This paper is devoted to the performance study of the Linear Minimum Mean Squared Error estimator for multidimensional signals in the large dimension regime. Such an estimator is frequently encountered in wireless communications and in array processing, and the Signal to Interference and Noise

Modified LMMSE turbo equalization

by Sen Jiang, Li Ping, Hong Sun, Chi Sing Leung - IEEE Commun. Lett , 2004
"... Abstract—This letter presents a modified linear minimum mean square error (LMMSE) turbo equalization scheme that uses an augmented real matrix representation for quadrature modulation systems. In the proposed scheme, the estimates of the two quadra-ture components of the transmitted symbol have both ..."
Abstract - Cited by 5 (0 self) - Add to MetaCart
Abstract—This letter presents a modified linear minimum mean square error (LMMSE) turbo equalization scheme that uses an augmented real matrix representation for quadrature modulation systems. In the proposed scheme, the estimates of the two quadra-ture components of the transmitted symbol have

Novel tap-wise LMMSE channel estimation for MIMO W-CDMA

by Christian Mehlführer - in Proc. 51st Annual IEEE Globecom Conference, 2008 , 2008
"... Abstract—In this paper, a tap-wise LMMSE channel estimator for MIMO W-CDMA is derived. Descrambling operations applied to delayed versions of the received signal whiten the input signal. The descrambling process thus breaks up the full channel autocorrelation matrix (including spatial and temporal c ..."
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Abstract—In this paper, a tap-wise LMMSE channel estimator for MIMO W-CDMA is derived. Descrambling operations applied to delayed versions of the received signal whiten the input signal. The descrambling process thus breaks up the full channel autocorrelation matrix (including spatial and temporal

Reduction of Noise Image Using LMMSE

by Joginder Singh, R. B. Dubey
"... There exist various image de-noising techniques. Amongst them orthogonal wavelet is preferred one. However, the orthogonal wavelet transform is not better technique as proper clustering of wavelet coefficients is not possible in this technique. So a better image de-noising technique is needed to hav ..."
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to have a better SNR and greater image information. In this work, image de-noising by linear minimum mean square-error estimation (LMMSE) scheme is proposed and results show that this method outperforms some of the existing de-noising techniques.

1 On the Robustness of MIMO LMMSE Channel Estimation

by Antonio Assalini, Student Member, Silvano Pupolin, Senior Member
"... Abstract—The robustness of the linear minimum mean square error (LMMSE) channel estimator is studied with respect to the reliability of the estimated channel correlation matrix used for its implementation. The analysis is of interest in practical applications of multiple-input multiple-output (MIMO) ..."
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Abstract—The robustness of the linear minimum mean square error (LMMSE) channel estimator is studied with respect to the reliability of the estimated channel correlation matrix used for its implementation. The analysis is of interest in practical applications of multiple-input multiple-output (MIMO
Results 1 - 10 of 201