White noise

About: White noise is a research topic. Over the lifetime, 16496 publications have been published within this topic receiving 318633 citations.

Extended theory of early-late code tracking for a bandlimited gps receiver

Abstract: Code-tracking accuracy, an important attribute of GPS receivers, depends both on characteristics of the signal being tracked and on the design of the receiver. A simple expression has been available to predict code-tracking accuracy for early-late processing of signals with sinc-squared spectra in white noise for an infinite front-end bandwidth receiver. However, the literature has not indicated when this approximation holds. This paper provides a more exact analysis of code-tracking accuracy for early-late discriminators processing conven-tional binary phase shift keyed signals in white noise. New analytical expressions apply for various front-end bandwidths, discriminator spacings, and code-tracking loop bandwidths, while using linear models based on a small-error assumption. A theoretical lower bound is also supplied, indicating inherent limits on accuracy for given conditions. While evaluation of the exact expressions requires numerical integrations, new algebraic approximations are also provided. Numerical results compare the various expressions and approximations.

Gaussian Sum Filters for Space Surveillance: Theory and Simulations

Abstract: While standard Kalman-based filters, Gaussian assumptions, and covariance-weighted metrics are very effective in data-rich tracking environments, their use in the data-sparse environment of space surveillance ismore limited. To properly characterize non-Gaussian density functions arising in the problem of long-term propagation of state uncertainties, a Gaussian sum filter adapted to the two-body problem in space surveillance is proposed and demonstrated to achieve uncertainty consistency. The proposed filter is made efficient by using only a onedimensional Gaussian sum in equinoctial orbital elements, thereby avoiding the expensive representation of a full six-dimensional mixture and hence the “curse of dimensionality.” Additionally, an alternate set of equinoctial elements is proposed and is shown to provide enhanced uncertainty consistently over the traditional element set. Simulation studies illustrate the improvements in theGaussian sumapproach over the traditional unscentedKalman filter and the impact of correct uncertainty representation in the problems of data association (correlation) and anomaly (maneuver) detection.

Variational Algorithms to Remove Stationary Noise: Applications to Microscopy Imaging

Abstract: A framework and an algorithm are presented in order to remove stationary noise from images. This algorithm is called variational stationary noise remover. It can be interpreted both as a restoration method in a Bayesian framework and as a cartoon+texture decomposition method. In numerous denoising applications, the white noise assumption fails. For example, structured patterns such as stripes appear in the images. The model described here addresses these cases. Applications are presented with images acquired using different modalities: scanning electron microscope, FIB-nanotomography, and an emerging fluorescence microscopy technique called selective plane illumination microscopy.

Optimal linear-quadratic systems for detection and estimation

Abstract: The problem of linear-quadratic systems for detection has long been solved by assuming the deflection criterion and Gaussian noise. It is shown here that the Gaussian assumption can be removed, and a complete solution is presented for an arbitrary probability distribution with finite fourth-order moments. The optimal solution can always be obtained by solving a linear system of equations. Some properties of the optimal systems are developed for particular examples of nonGaussian noise. It is shown that there is a strong relationship between linear-quadratic optimal detection and optimal estimation, which extends results known for the purely linear case. >

A stochastic model of neural response

Abstract: We propose a model of a passive nerve cylinder undergoing random stimulus along its length. It is shown that this model is approximated by the solution of a stochastic partial differential equation. Numerous properties of the sample paths are derived, such as their modulus of continuity, quadratic and quartic variation, and it is shown that the solution exhibits the phenomenon of flicker noise. The first-passage problem is studied, and it is shown to be connected with a first-hitting time for an infinite-dimensional diffusion.