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Bộ lọc adaptive

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  • The areas of adaptive filtering and change (fault) detection are quite active fields. both in research and applications . Some central keywords of the book are listed in Table 1.1, and the figures. illustrated in Figure 1.1, give an idea of the relative activity in the different areas . For comparison. the two related and well established areas of adaptive control and system identification are included in the table

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  • The noise is here assumed white with variance X, and will sometimes be restricted to be Gaussian. The last expression is in a polynomial form, whereas G, H are filters. Time-variability is modeled by time-varying parameters Bt. The adaptive filtering problem is to estimate these parametersb y an adaptive filter,

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  • Although today microwave and optical engineering appear to be separate disciplines, there has been a tradition of interchange of ideas between them. In fact, many traditional microwave concepts have been adapted to yield optical counterparts. The laser, as an optical device that plays a key role in optoelectronics and fibre-optic communications, grew from the work of its microwave predecessor, the maser (microwave amplification by stimulated emission of radiation) [1]. The operating principle behind the laser is very similar to that of the microwave oscillator....

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  • The basic assumption in this part. signal estimationis. that them easurements gt consist of a deterministic component Ot ~ the signal ~ and additive white noise et. yt = Ot + et . Adaptive Filtering and Change Detection Fredrik Gustafsson Copyright © 2000 John Wiley & Sons, Ltd ISBNs: 0-471-49287-6 (Hardback); 0-470-84161-3 (Electronic) 58 On-line amroaches For change detection, this will be labeled as a change in the mean model.

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  • Let us start with considering change detection in linear regressions as an offline problem. which will be referred to as segmentation . The goal is to find a Adaptive Filtering and Change Detection Fredrik Gustafsson Copyright © 2000 John Wiley & Sons, Ltd ISBNs: 0-471-49287-6 (Hardback); 0-470-84161-3 (Electronic) 232 Chanae detection b baanskes df il toenr sequence of time indices kn = (kl, k2, .., kn), where both the number n and the locations ki are unknown, such that a linear regression model with piecewise constant parameters,...

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  • Model validation is the problem of deciding whether observed data are consistent with a nominal model . Change detection based on model validation aims at applying a consistency test in one of the following ways:

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  • The goal in this section is to explain the fundamentals of Kalman filter theory by a few illustrative examples. The Kalman filter requires a state space model for describing the signal dynamics. To describe its role, we need a concrete example, so let us return to the target tracking example from Chapter 1. Assume that we want a model with the states z1 = X , x2 = Y, x3 = X och x4 = Y . This is the simplest possible case of state vector used in practice. Before we derive a model in the next section, a few remarks will be given on what role...

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  • Consider a batch of data over a sliding window, collected in a measurement vector Y and input vector U. As in Chapter 6, the idea of a consistency test is to apply a linear transformation to a batch of data, AiY + BiU + ci. The matrices Ai, Bi and vector G are chosen so that the norm of the linear transformation is small when there is no change/fault according to hypothesis Hi, and large when fault Hi has appeared.

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  • The purpose of this section is to get a geometric understanding of linear estimation . First. we outline how projections are computed in linear algebra for finite dimensional vectors . Functional analysis generalizes this procedure to some infinite-dimensional spaces (so-called Hilbert spaces). and finally. we point out that linear estimation is a special case of an infinite-dimensional space

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  • This chapter provides background information and problem descriptions of the applications treated in this book . Most of the applications include real data and many of them are used as case studies examined throughout the book with different algorithms

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  • This chapter surveys off-line formulations of single and multiple change point estimation . Although the problem formulation yields algorithms that process data batch.wise, many important algorithms have natural on-line implementations and recursive approximations . This chapter is basically a projection of the more general results in Chapter 7 to the case of signal estimation . There are, however. some dedicated algorithms for estimating one change point offline that apply to the current case of a scalar signal model . In the literature of mathematical statistics.

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  • This chapter is devoted to the problem of detecting additive abrupt changes in linear state space models . Sensor and actuator faults as a sudden offset or drift can all be modeled as additive changes . In addition. disturbances are traditionally modeled as additive state changes . The likelihood ratio formulation provides a general framework for detecting such changes. and to isolate the fault/disturbance .

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  • This chapter addresses the most general problem formulation of detection in linear systems. Basically, all problem formulations that have been discussed so far are included in the framework considered. The main purpose is to survey multiple model algorithms, and a secondary purpose is to overview and compare the state of the art in different application areas for reducing complexity, where similar algorithms have been developed independently.

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  • It might also be the signal component st of the measurement yt = st +ut. The measurements zt consist of the measured outputs yt and, when appropriate, the inputs ut.

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  • KỸ THUẬT RADIO OVER FIBER AMC AMPS AP BB BPF BPSK Adaptation Modulation and Coding Advanced Mobile Phone Service Access Point Base Band Band Pass Filter Binary Phase Shift Keying Broadband Wireless Access BWAN CDMA CS CSPDN DFB DMOD Network Code division Multiple Access Central Station Circuit Switched Data Network Distributed Feed Back(laser) DeMODdulator Đa truy cập phân chia theo mã Trạm trung tâm Mạng chuyển mạch dữ liệu Laser hồi tiếp phân tán Bộ giải điều chế Bộ điều chế và mã hoá Dịch vụ di động tiên tiến Điểm truy cập Băng tần cơ sở Bộ lọc băng thông...

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  • 4.7 Hệ thống MC-CDMA với băng chọn lọc thích nghi Trong hệ thống MC-CDMA, nhiều sóng mang phụ được dùng để truyền dữ liệu tốc độ cao, mỗi dữ liệu được điều chế bởi sóng mang phụ khác nhau và chịu ảnh hưởng của những kênh truyền khác nhau. Bằng cách truyền dữ liệu chỉ trên những băng tần số được chọn lựa từ toàn bộ các băng tần phụ, mô hình truyền dữ liệu mới ABS (Adaptive Band-Selection) sẽ cải thiện dung lượng hệ thống MCCDMA. Cùng lượng công suất phát và phát cùng lượng dữ liệu thì mô...

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