Model-Based Signal Processing by James V. Candy

By James V. Candy

A different therapy of sign processing utilizing a model-based viewpoint sign processing is essentially aimed toward extracting precious details, whereas rejecting the extraneous from noisy information. If sign degrees are excessive, then easy innovations should be utilized. even though, low sign degrees require utilizing the underlying physics to right the matter inflicting those low degrees and extracting the specified details. Model-based sign processing accommodates the actual phenomena, measurements, and noise within the kind of mathematical versions to unravel this challenge. not just does the strategy allow sign processors to paintings without delay by way of the problem's physics, instrumentation, and uncertainties, however it presents a ways better functionality over the normal strategies. Model-based sign processing is either a modeler's in addition to a sign processor's software. Model-Based sign Processing develops the model-based process in a unified demeanour and follows it in the course of the textual content within the algorithms, examples, functions, and case reports. The process, coupled with the hierarchy of physics-based versions that the writer develops, together with linear in addition to nonlinear representations, makes it a special contribution to the sphere of sign processing. The textual content comprises parametric (e.g., autoregressive or all-pole), sinusoidal, wave-based, and state-space versions as a few of the version units with its specialise in how they're used to resolve sign processing difficulties. certain gains are only if help readers in figuring out the fabric and studying the best way to practice their new wisdom to fixing real-life difficulties. * Unified remedy of recognized sign processing versions together with physics-based version units * easy functions exhibit how the model-based procedure works, whereas specific case experiences show challenge options of their entirety from inspiration to version improvement, via simulation, software to genuine info, and unique functionality research * Summaries supplied with each one bankruptcy make sure that readers comprehend the major issues had to stream ahead within the textual content in addition to MATLAB(r) Notes that describe the main instructions and toolboxes on hand to accomplish the algorithms mentioned * References bring about extra in-depth insurance of specialised issues * challenge units try out readers' wisdom and support them positioned their new talents into perform the writer demonstrates how the fundamental inspiration of model-based sign processing is a powerful and ordinary method to clear up either easy in addition to advanced processing difficulties. Designed as a graduate-level textual content, this e-book can be crucial interpreting for training signal-processing pros and scientists, who will locate the range of case stories to be useful.

An Instructor's handbook providing special ideas to the entire difficulties within the ebook is obtainable from the Wiley editorial division

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Plane wave impinging on a two-element sensor array–frequency and bearing estimation problem: (a ) Classical spectral (temporal and spatial) estimation approach. (b) Model-based approach using parametric adaptive (nonlinear) processor to estimate bearing angle, temporal frequency, and the corresponding residual or innovations sequence. (ωo = 50 Hz, θo = 45◦ ). The MBP also produces a “residual sequence” (shown in the figure) that is used to determine its performance. We summarize the classical and model-based solutions to the temporal frequency and bearing angle estimation problem.

FIND the best estimates of the plane wave bearing angle (θo ) and temporal frequency (ωo ) parameters, θˆo and ωˆ o . 1 We use the notation, “N(m, v)” to define a gaussian or normal probability distribution with mean, m, and variance, v. 6. Plane wave—signal enhancement problem: (a ) Classical bandpass filter (50 Hz, 1 Hz BW) approach. (b) Model-based processor using 50 Hz, 45◦ , plane wave model impinging on a two-element sensor array. The classical approach to this problem is to first take one of the sensor channels and perform spectral analysis on the filtered time series to estimate the temporal frequency, ωo .

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