Approximate optimum nonlinear filtering, detection and demodulation of nongaussian signals.
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Approximate optimum nonlinear filtering, detection and demodulation of nongaussian signals.

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Published in [New York] .
Written in English


  • Signal theory (Telecommunication),
  • Markov processes.,
  • Digital filters (Mathematics),
  • Nonlinear theories.

Book details:

LC ClassificationsTK5102.5 .A66
The Physical Object
Pagination235 l.
Number of Pages235
ID Numbers
Open LibraryOL5764255M
LC Control Number71297551

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  Part II focuses on the problem of finding the optimum estimate of a waveform which is embedded in a signal in a nonlinear manner. The following topics are covered in detail: Bayesian Cramér-Rao bound on the mean-square estimation error; Optimum demodulators for frequency-modulation systems; Phase estimation: the synchronization problemReviews: 1. A simple optimum nonlinear filter for stochastic-resonance-based signal detection Conference Paper (PDF Available) in Acoustics, Speech, and Signal Processing, Approximate Optimum Nonlinear FiJtering, Detection and Demodulation of NonGaussian Signals, (). Diffusion Processes and Optimal Advertising Policy," (). Formal Algorithms for Continuous-Time Non-Linear Filtering and Smoothing," Nonlinear Filtering Theory,"Author: Charles Tapiero. Application of Optimum Filter Xˆ • The solution θ∗ is the least squares estimate, – Linear filters work poorly with non-Gaussian noise. • Nonlinear filters can be designed using the same method-ologies. C. A. Bouman: Digital Image Processing - January 7,

When dealing with nonlinear filtering problems, the Extended Kalman Filter (EKF) approximates the problem to apply the KF solution. Some contributions show the use of EKF for carrier phase. Figure makes evident that the performance of the detection strategy is deter­ mined entirely by the ratio E/(σ √ E), or equivalently by the signal-to-noise ratio E/σ2, i.e. the 2ratio of the signal energy E to the noise variance σ. Matched Filtering Since the correlation sum in () constitutes a linear operation on the. The Alpha/BT indicates the filter roll-off (or excess bandwidth) of the selected filter which occurs due to the practical inability of filter technology to build a perfectly square (brick-wall) filter which would have an alpha of 0 (no excess bandwidth). For example, a typical filter with an alpha of has a bandwidth 30% greater than the. Signal Detection in Non-Gaussian Noise S.A. Kassam An Introduction to Signal Detection and Estimation, 2nd Edition VI1.D Nonlinear Filtering .. VII.D.l Basic Equations of Nonlinear Filtering .. VII.D.2 A Derivation of the Nonlinear Filtering Equations. VXI.D.3 Practical Approximations to Optimum Nonlinear Filters.

  A Continuous Wave Narrow-Band Interference (CW-NBI) can reduce the effective signal-to-noise ratio (SNR), and the quality of the Signals-of-Interest (SoI) in any wireless transmission such as in a Digital Video Broadcasting (DVB-S2) receiver. Therefore, this paper proposes a novel low-complexity anti-jamming filter to mitigate unknown CW-NBI. Nonlinear Filtering • Linear filters – Tend to blur edges and other image detail. – Perform poorly with non-Gaussian noise. – Result from Gaussian image and noise assumptions. – Images are not Gaussian. • Nonlinear filter – Can preserve edges – Very effective at removing impulsive noise – Result from non-Gaussian image and. detection filter for commercial aircrafts is designed. [5] A detection filter is applied for nonlinear systems by means of geometric view on inversion-based model. [6] Generally, in order to make the nonlinear problem formulations tractable, some form of model reduction, approximation and/or process simplification is often. The combination of Eqs.(9)– is known as the Stratonovich-Kushner nonlinear equations (SKE), and they have an appealing physical sense: the first term in represents the dynamics of the a priori data of x(t).For the second term, the analysis of observations is used to drive the innovation of the a priori data. Using any optimization criteria, one can get x ̂ t (the optimum estimation of x(t.