The Experts below are selected from a list of 45 Experts worldwide ranked by ideXlab platform
Saleh Al-shebeilli - One of the best experts on this subject based on the ideXlab platform.
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ISSPIT - Finite-precision analysis: Fast QR-decomposition algorithm
The 10th IEEE International Symposium on Signal Processing and Information Technology, 2010Co-Authors: Mobien Mohammad, Saleh Al-shebeilliAbstract:The Fast QR-Decomposition based recursive least-squares (FQRD-RLS) algorithms offer RLS like convergence and misadjustment, at a lower computational cost, and therefore are desirable for implementation on fixed point digital signal processors (DSPs). Furthermore, the FQRD-RLS algorithms are derived from QR-decomposition based RLS algorithm that are well-known for their numerical stability in finite-precision, therefore these algorithms are also assumed to be numerically stable. However, no formal proof has been provided till now for the stability of the FQRD-RLS algorithms in finite precision. The objective here is to prove the sufficient condition for stability by deriving the steady-state values of the quantization error of the internal variables of the FQRD-RLS algorithm in presence of a zero mean and Unity Variance white Gaussian noise. The mean-squared quantization error values of all the variables of the FQRD-RLS algorithm are derived and compared with a fixed-point simulation for verification.
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Finite-precision analysis: Fast QR-decomposition algorithm
The 10th IEEE International Symposium on Signal Processing and Information Technology, 2010Co-Authors: Mobien Mohammad, Saleh Al-shebeilliAbstract:The Fast QR-Decomposition based recursive least-squares (FQRD-RLS) algorithms offer RLS like convergence and misadjustment, at a lower computational cost, and therefore are desirable for implementation on fixed point digital signal processors (DSPs). Furthermore, the FQRD-RLS algorithms are derived from QR-decomposition based RLS algorithm that are well-known for their numerical stability in finite-precision, therefore these algorithms are also assumed to be numerically stable. However, no formal proof has been provided till now for the stability of the FQRD-RLS algorithms in finite precision. The objective here is to prove the sufficient condition for stability by deriving the steady-state values of the quantization error of the internal variables of the FQRD-RLS algorithm in presence of a zero mean and Unity Variance white Gaussian noise. The mean-squared quantization error values of all the variables of the FQRD-RLS algorithm are derived and compared with a fixed-point simulation for verification.
Mobien Mohammad - One of the best experts on this subject based on the ideXlab platform.
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ISSPIT - Finite-precision analysis: Fast QR-decomposition algorithm
The 10th IEEE International Symposium on Signal Processing and Information Technology, 2010Co-Authors: Mobien Mohammad, Saleh Al-shebeilliAbstract:The Fast QR-Decomposition based recursive least-squares (FQRD-RLS) algorithms offer RLS like convergence and misadjustment, at a lower computational cost, and therefore are desirable for implementation on fixed point digital signal processors (DSPs). Furthermore, the FQRD-RLS algorithms are derived from QR-decomposition based RLS algorithm that are well-known for their numerical stability in finite-precision, therefore these algorithms are also assumed to be numerically stable. However, no formal proof has been provided till now for the stability of the FQRD-RLS algorithms in finite precision. The objective here is to prove the sufficient condition for stability by deriving the steady-state values of the quantization error of the internal variables of the FQRD-RLS algorithm in presence of a zero mean and Unity Variance white Gaussian noise. The mean-squared quantization error values of all the variables of the FQRD-RLS algorithm are derived and compared with a fixed-point simulation for verification.
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Finite-precision analysis: Fast QR-decomposition algorithm
The 10th IEEE International Symposium on Signal Processing and Information Technology, 2010Co-Authors: Mobien Mohammad, Saleh Al-shebeilliAbstract:The Fast QR-Decomposition based recursive least-squares (FQRD-RLS) algorithms offer RLS like convergence and misadjustment, at a lower computational cost, and therefore are desirable for implementation on fixed point digital signal processors (DSPs). Furthermore, the FQRD-RLS algorithms are derived from QR-decomposition based RLS algorithm that are well-known for their numerical stability in finite-precision, therefore these algorithms are also assumed to be numerically stable. However, no formal proof has been provided till now for the stability of the FQRD-RLS algorithms in finite precision. The objective here is to prove the sufficient condition for stability by deriving the steady-state values of the quantization error of the internal variables of the FQRD-RLS algorithm in presence of a zero mean and Unity Variance white Gaussian noise. The mean-squared quantization error values of all the variables of the FQRD-RLS algorithm are derived and compared with a fixed-point simulation for verification.
Pak-chung Ching - One of the best experts on this subject based on the ideXlab platform.
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Time-of-arrival based localization under NLOS conditions
IEEE Transactions on Vehicular Technology, 2006Co-Authors: Yiu-tong Chan, Wing-yue Tsui, Hing-cheung So, Pak-chung ChingAbstract:Three or more base stations (BS) making time-of-arrival measurements of a signal from a mobile station (MS) can locate the MS. However, when some of the measurements are from non-line-of-sight (NLOS) paths, the location errors can be very large. This paper proposes a residual test (RT) that can simultaneously determine the number of line-of-sight (LOS) BS and identify them. Then, localization can proceed with only those LOS BS. The RT works on the principle that when all measurements are LOS, the normalized residuals have a central Chi-Square distribution, versus a noncentral distribution when there is NLOS. The residuals are the squared differences between the estimates and the true position. Normalization by their Variances gives a Unity Variance to the resultant random variables. In simulation studies, for the chosen geometry and NLOS and measurement noise errors, the RT can determine the correct number of LOS-BS over 90% of the time. For four or more BS, where there are at least three LOS-BS, the estimator has Variances that are near the Cramer--Rao lower bound.
Tod Luginbuhl - One of the best experts on this subject based on the ideXlab platform.
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Characteristic Functions of the Product of Two Gaussian Random Variables and the Product of a Gaussian and a Gamma Random Variable
IEEE Signal Processing Letters, 2016Co-Authors: Steven Schoenecker, Tod LuginbuhlAbstract:We derive the characteristic function (CF) for two product distributions-first for the product of two Gaussian random variables (RVs), where one has zero mean and Unity Variance, and the other has arbitrary mean and Variance. Next, we develop the characteristic function for the product of a gamma RV and a zero mean, Unity Variance Gaussian RV. The underlying rationale for this is to develop a model for a “quasi-Gaussian” RV-an RV that is nominally Gaussian, but with mean and Variance parameters that are not constant, but instead, are RVs themselves. Due to the central limit theorem, many “real-world” processes are modeled as being Gaussian distributed. However, this implicitly assumes that the processes being modeled are perfectly stationary, which is often a poor assumption. The quasi-Gaussian model could be used as a more conservative description of many of these processes.
Yiu-tong Chan - One of the best experts on this subject based on the ideXlab platform.
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Time-of-arrival based localization under NLOS conditions
IEEE Transactions on Vehicular Technology, 2006Co-Authors: Yiu-tong Chan, Wing-yue Tsui, Hing-cheung So, Pak-chung ChingAbstract:Three or more base stations (BS) making time-of-arrival measurements of a signal from a mobile station (MS) can locate the MS. However, when some of the measurements are from non-line-of-sight (NLOS) paths, the location errors can be very large. This paper proposes a residual test (RT) that can simultaneously determine the number of line-of-sight (LOS) BS and identify them. Then, localization can proceed with only those LOS BS. The RT works on the principle that when all measurements are LOS, the normalized residuals have a central Chi-Square distribution, versus a noncentral distribution when there is NLOS. The residuals are the squared differences between the estimates and the true position. Normalization by their Variances gives a Unity Variance to the resultant random variables. In simulation studies, for the chosen geometry and NLOS and measurement noise errors, the RT can determine the correct number of LOS-BS over 90% of the time. For four or more BS, where there are at least three LOS-BS, the estimator has Variances that are near the Cramer--Rao lower bound.
