The Experts below are selected from a list of 264 Experts worldwide ranked by ideXlab platform
T.e. Posch - One of the best experts on this subject based on the ideXlab platform.
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Time-frequency distribution for a wide sense stationary Random Signal
IEEE International Symposium on Circuits and Systems, 1Co-Authors: T.e. PoschAbstract:The time-frequency distribution for a wide sense stationary Random Signal is considered. The author derives a simple criterion for when a bilinear time-frequency distribution gives the power spectrum of the Signal. >
Wenchang Sun - One of the best experts on this subject based on the ideXlab platform.
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Nonuniform Sampling for Random Signals Bandlimited in the Linear Canonical Transform Domain
arXiv: Signal Processing, 2018Co-Authors: Haiye Huo, Wenchang SunAbstract:In this paper, we mainly investigate the nonuniform sampling for Random Signals which are bandlimited in the linear canonical transform (LCT) domain. We show that the nonuniform sampling for a Random Signal bandlimited in the LCT domain is equal to the uniform sampling in the sense of second order statistic characters after a pre-filter in the LCT domain. Moreover, we propose an approximate recovery approach for nonuniform sampling of Random Signals bandlimited in the LCT domain. Furthermore, we study the mean square error of the nonuniform sampling. Finally, we do some simulations to verify the correctness of our theoretical results.
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Sampling theorems and error estimates for Random Signals in the linear canonical transform domain
Signal Processing, 2015Co-Authors: Haiye Huo, Wenchang SunAbstract:The linear canonical transform (LCT) plays an important role in optical and digital Signal processing. Over the past few decades, there has been a vast amount of research on sampling theorems for a deterministic Signal bandlimited in the LCT domain. However, Signals are usually Random in practical situations. Hence in this paper, we study sampling theorems for a Random Signal bandlimited in the LCT domain. We first construct a Random Signal theoretic framework in the LCT domain, such as the LCT power spectral density and the LCT auto-correction function. Then, we formulate uniform sampling theorem and multi-channel sampling theorem for a Random Signal bandlimited in the LCT domain. Finally, we analyze two kinds of reconstruction error estimates for uniformly sampling a Random Signal in the LCT domain: aliasing error and truncation error. HighlightsA uniform sampling theorem for a Random Signal bandlimited in the LCT domain is studied in this paper.A multi-channel sampling theorem for a Random Signal bandlimited in the LCT domain is also formulated.Two kinds of reconstruction error estimates (aliasing error and truncation error) for uniformly sampling a Random Signal in the LCT domain are analyzed.
W. Martin - One of the best experts on this subject based on the ideXlab platform.
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ICASSP - Time-frequency analysis of Random Signals
ICASSP '82. IEEE International Conference on Acoustics Speech and Signal Processing, 1Co-Authors: W. MartinAbstract:A conjoint time-frequency representation of harmonizable Random Signals is defined as a generalization of the Wigner distribution of finite energy Signals. It is shown that this conjoint time-frequency representation possesses properties analogous to those of finite energy Signals. Furthermore, we state a necessary and sufficient condition for the existence of a Random Wigner distribution as a stochastic integral in quadratic mean. Then, we can define a Random instantaneous frequency and a Random group delay, and give expressions of their expectation and variance. This is done without assuming narrow band conditions or stationarity of the Random Signal.
Haiye Huo - One of the best experts on this subject based on the ideXlab platform.
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Nonuniform Sampling for Random Signals Bandlimited in the Linear Canonical Transform Domain
arXiv: Signal Processing, 2018Co-Authors: Haiye Huo, Wenchang SunAbstract:In this paper, we mainly investigate the nonuniform sampling for Random Signals which are bandlimited in the linear canonical transform (LCT) domain. We show that the nonuniform sampling for a Random Signal bandlimited in the LCT domain is equal to the uniform sampling in the sense of second order statistic characters after a pre-filter in the LCT domain. Moreover, we propose an approximate recovery approach for nonuniform sampling of Random Signals bandlimited in the LCT domain. Furthermore, we study the mean square error of the nonuniform sampling. Finally, we do some simulations to verify the correctness of our theoretical results.
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Sampling theorems and error estimates for Random Signals in the linear canonical transform domain
Signal Processing, 2015Co-Authors: Haiye Huo, Wenchang SunAbstract:The linear canonical transform (LCT) plays an important role in optical and digital Signal processing. Over the past few decades, there has been a vast amount of research on sampling theorems for a deterministic Signal bandlimited in the LCT domain. However, Signals are usually Random in practical situations. Hence in this paper, we study sampling theorems for a Random Signal bandlimited in the LCT domain. We first construct a Random Signal theoretic framework in the LCT domain, such as the LCT power spectral density and the LCT auto-correction function. Then, we formulate uniform sampling theorem and multi-channel sampling theorem for a Random Signal bandlimited in the LCT domain. Finally, we analyze two kinds of reconstruction error estimates for uniformly sampling a Random Signal in the LCT domain: aliasing error and truncation error. HighlightsA uniform sampling theorem for a Random Signal bandlimited in the LCT domain is studied in this paper.A multi-channel sampling theorem for a Random Signal bandlimited in the LCT domain is also formulated.Two kinds of reconstruction error estimates (aliasing error and truncation error) for uniformly sampling a Random Signal in the LCT domain are analyzed.
Pramod K Varshney - One of the best experts on this subject based on the ideXlab platform.
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location estimation of a Random Signal source based on correlated sensor observations
IEEE Transactions on Signal Processing, 2011Co-Authors: Ashok Sundaresan, Pramod K VarshneyAbstract:The problem of location estimation of a source of Random Signals using a network of sensors is considered. A novel maximum-likelihood estimation (MLE) based approach using copula functions is proposed. The measurements received at the sensors are often spatially correlated and characterized by a multivariate distribution. Using the theory of copulas, the joint parametric density of sensor observations (joint likelihood) is approximated assuming only the knowledge of the marginal likelihood functions of the sensor observations. The problem of selecting the best copula function to model the joint likelihood is approached as one of model selection and a model fusion strategy is used to reduce the effect of selection bias. An example involving source localization of a Poisson source is presented to illustrate the proposed approach and demonstrate its performance.
