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Performance Analysis of ML Estimation under Misspecified Numbers of signals

Performance Analysis of ML Estimation under Misspecified Num...

引用

sensor array and multichannel signal processing workshop

作者： Pei-Jung Chung Institute for Digital Communications Joint Research Institute for Signal & Image Processing School of Engineering and Electronics University of Edinburgh UK

ISBN: (纸本)1424403081

The maximum likelihood (ML) approach for estimating direction of arrival (DOA) plays an important role in array processing. Its consistency and efficiency have been well established in literature. A common assumption is that the number of signals is known. In many applications, this information is not available and needs to be estimated. However, the estimated number of signals may not equal the true number of signals. Therefore, it is important to know whether the ML estimator provides any relevant information about the true parameters. In a previous study, the ML estimator was shown to converge to a well defined vector whose components coincide with the true parameters. In this work, we investigate the impact of model mismatch on estimation accuracy. Applying the theory of misspecified nonlinear regression models, we derive a compact formula for the asymptotic covariance matrix. Our analysis and simulation show that the variance increases when the number of signals is incorrectly chosen

关键词： Performance analysis Maximum likelihood estimation Direction of arrival estimation Covariance matrix signal processing array signal processing Digital communication Image processing Analysis of variance signal analysis

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On the Singular Value Manifold and Numerical Stabilization of Algorithms with Orthogonality Constraints

On the Singular Value Manifold and Numerical Stabilization o...

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sensor array and multichannel signal processing workshop

作者： S.C. Douglas Department of Electrical Engineering Southern Methodist University Dallas TX USA

Recently, interest has risen in adaptive algorithms that implicitly impose orthogonality constraints on an adjustable matrix. In practice, parameter deviations from orthogonality can occur due to a chosen algorithm's numerical implementation. This paper introduces the geometry of and adaptive algorithms for the singular value manifold to mitigate these numerical effects. Both gradient and Newton-based methods on the singular value manifold are derived. Applications to single-step and iterative orthogonalization reveal relationships between existing orthogonalization methods as well as novel, fast-converging approximate Newton procedures for this task. Simulations are used to explore their performances

关键词： signal processing algorithms Iterative algorithms Geometry Cost function Adaptive algorithm Computational modeling Geophysics computing Convergence Algorithm design and analysis Manifolds

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A Comparative Performance Investigation of Adaptive Beamformers with Iterative Weight Refinement for GSM signal Detection

A Comparative Performance Investigation of Adaptive Beamform...

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sensor array and multichannel signal processing workshop

作者： Myung-Hoon Yeon J.J. Shynk R.P. Gooch Department of Electrical and Computer Engineering University of California Santa Barbara CA USA Applied Signal Technology Inc. Sunnyvale CA USA

In a multiple-access communication system, many users utilize the same spectral bandwidth by transmitting, for example, during assigned time slots using time-division multiple access (TDMA). The users of a cellular system based on TDMA can encounter cochannel interference because carrier frequencies are reused in nearby cells. In this paper, we investigate adaptive beamforming and equalization algorithms for cochannel signal detection in the Global System for Mobile communications (GSM). In particular, we are interested in iterative weight refinement techniques where the beamformer weights are successively adapted using re-encoded TDMA data to improve signal estimation. The data of a GSM channel are processed in two stages that include the following series of steps: frame synchronization, adaptive beamforming, signal detection, adaptive equalization, and soft-input decoding. The algorithms are evaluated using the number of frames passing the parity check as the performance measure for real GSM data collected with an array of antennas

关键词： GSM signal detection Time division multiple access array signal processing Adaptive equalizers Iterative algorithms Bandwidth Interchannel interference Frequency Estimation

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Structured Covariance Estimation: Theory, Application, and Recent Results

Structured Covariance Estimation: Theory, Application, and R...

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sensor array and multichannel signal processing workshop

作者： D.R. Fuhrmann Electronic Systems and Signals Research Laboratory Department of Electrical and Systems Engineering Washington University of Saint Louis Saint Louis MO USA

The maximum-likelihood approach to structured covariance estimation and spectrum estimation has wide applicability in time series analysis, spectroscopy, adaptive beamforming and detection, remote sensing, radio astronomy, and radar imaging. Standard structured covariance EM algorithm with full model matrices is computationally demanding. Computational requirements drastically reduced when model matrices are sparse. Sparse structure may be achieved through appropriately chosen data preprocessing. We are investigating application in problem of airborne radar imaging from multiple viewpoints, previously computationally unrealistic

关键词： Estimation theory Covariance matrix Sparse matrices Maximum likelihood detection Maximum likelihood estimation Spectral analysis Image analysis Time series analysis Spectroscopy array signal processing

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Throughput of Large Wireless Networks on Square, Hexagonal and Triangular Grids

Throughput of Large Wireless Networks on Square, Hexagonal a...

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sensor array and multichannel signal processing workshop

作者： Kezhu Hong Yingbo Hua Department of Electrical Engineering University of California Riverside CA USA

Capacity analysis of sensor networks or wireless ad hoc networks has attracted a great attention in the past years. But the research in this area has primarily focused on scaling laws of arbitrary or random network instead of the exact capacity of given topologies. While the insight into how the capacity of an arbitrary or random network scales with the number of nodes in a given area is important, the exact capacity of a network depends on the network topology and can be more desirable in practice. In this paper, we compare the throughput of a large network with three possible topologies: square (rectangular), hexagonal and triangular. With a given topology, the network throughput also depends on the choice of routing protocols. We follow a synchronous array method (SAM) that is known so far to yield the highest throughout of a network on a rectangular grid

关键词： Throughput Wireless networks Network topology Mobile ad hoc networks Access protocols signal to noise ratio Capacitive sensors Wireless sensor networks Wireless mesh networks Routing protocols

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Programmable Processor Design for Givens Rotations based Applications

Programmable Processor Design for Givens Rotations based App...

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sensor array and multichannel signal processing workshop

作者： K. Dickson Z. Liu J.V. McCanny The Institute of Electronics Communications and Information Technology ECIT Queen''s University Belfast UK

A wide range of applications involve matrix decompositions which can be computed using givens rotations. A programmable processor for givens rotations offers more flexibility than highly tailored systolic array hardware implementations. The programmable processor design presented in this paper employs an approximate rotations method to compute givens rotations. Instantiations of this programmable processor can then be arranged/configured (i.e. in arrays of varying size and dimension) according to the requirements of the target application. Another advantage of this approach is that the processor can be programmed to compute different algorithms (e.g. QR decomposition, singular-value decomposition or a combination of both)

关键词： Process design Systolic arrays Matrix decomposition Hardware array signal processing MIMO Radar antennas Adaptive arrays Maximum likelihood detection Computer architecture

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Probabilistically-constrained Estimation of Random Parameters with Unknown Distribution

Probabilistically-constrained Estimation of Random Parameter...

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sensor array and multichannel signal processing workshop

作者： S.A. Vorobyov Y.C. Eldar A.B. Gershman Darmstadt University of Technology Darmstadt Germany Department of Electrical Engineering Technion Israel Institute of Technology-Technion Haifa Israel

The problem of estimating random unknown signal parameters in a noisy linear model is considered. It is assumed that the covariance matrices of the unknown signal parameter and noise vectors are known and that the noise is Gaussian, while the distribution of the random signal parameter vector is unknown. Instead of the traditional minimum mean squared error (MMSE) approach, where the average is taken over both the random signal parameters and noise realizations, we propose a linear estimator that minimizes the MSE which is averaged over the noise only. To make our design pragmatic, the minimization is performed for signal parameter realizations whose probability is sufficiently large, while "discarding" low-probability realizations. It is shown that the obtained linear estimator can be viewed as a generalization of the classical Wiener filter

关键词： Parameter estimation Vectors Covariance matrix Wiener filter Communication systems Gaussian noise Radar signal processing Noise robustness signal design Estimation theory

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Large Phased array Radar using Networked Small Parabolic Reflectors

Large Phased Array Radar using Networked Small Parabolic Ref...

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sensor array and multichannel signal processing workshop

作者： F. Amoozegar Jet Propulsion Laboratory California Institute of Technology Pasadena CA USA

Multifunction phased array systems with radar, telecom, and imaging applications have already been established for flat plate phased arrays of dipoles, or waveguides. In this paper the design trades and candidate options for combining the radar and telecom functions of the deep space network (DSN) into a single large transmit array of small parabolic reflectors will be discussed. In particular the effect of combing the radar and telecom functions on the sizes of individual antenna apertures and the corresponding spacing between the antenna elements of the array will be analyzed. A heterogeneous architecture for the DSN large transmit array is proposed to meet the radar and telecom requirements while considering the budget, scheduling, and strategic planning constrains

关键词： Phased arrays Radar antennas Telecommunications Antenna arrays Radar imaging Aperture antennas Dipole antennas Solar system Calibration Propulsion

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The Strong Uncorrelating Projection Approximation (SUPA) Algorithm for Decorrelating Complex Non-Circular Data

The Strong Uncorrelating Projection Approximation (SUPA) Alg...

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sensor array and multichannel signal processing workshop

作者： S.C. Douglas J. Eriksson V. Koivunen Department of Electrical Engineering Southern Methodist University Dallas TX USA Signal Processing Laboratory SMARAD CoE Helsinki University of Technology Espoo Finland

The second order statistics of stationary complex random vector signals are not completely specified by the data covariance matrix. Hence, conventional decorrelation methods may not suffice in removing dependencies within complex-valued signals; instead, strong-uncorrelation is required (J. Eriksson and V. Koivunen, 2006). In this paper, we develop a procedure for adaptively estimating the strong uncorrelating transform (SUT) by recasting this task as a series of one-dimensional signal approximation problems using a novel prewhitened cost and applying a modified version of the projection approximation subspace tracking with deflation (PASTd) algorithm to solve each problem. The algorithm updates with prewhitening are of O(n 2 ) complexity per n-element snapshot, and unlike other SUT algorithms, the technique can be used in a sequential fashion to identify any portion of the complex signal subspace by degree of signal non-circularity

关键词： Approximation algorithms Decorrelation signal processing algorithms Covariance matrix Statistics signal processing Symmetric matrices Multidimensional signal processing Wireless communication Independent component analysis

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The Akaike Information Criterion with Parameter Uncertainty

The Akaike Information Criterion with Parameter Uncertainty

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sensor array and multichannel signal processing workshop

作者： A. Seghouane National ICT Australia Canberra Research Laboratory Research School of Information Sciences and Engineering Australian National University Canberra ACT Australia

An instance crucial to most problems in signal processing is the selection of the order of a candidate model. Among the different exciting criteria, the two most popular model selection criteria in the signal processing literature have been the Akaike's criterion AIC and the Bayesian information criterion BIC. These criteria are similar in form in that they consist of data and penalty terms. Different approaches have been used to derive these criteria. However, none of them take into account the prior information concerning the parameters of the model. In this paper, an new approach for model selection, that takes into account the prior information on the model parameters, is proposed. Using the proposed approach and depending on the nature of the prior on the model parameters, two new information criteria are proposed for univariate linear regression model selection. We use the term "information criteria" because their derivation is based on the Kullback-Leibler divergence.

关键词： Uncertain systems signal processing Bayesian methods Australia Parameter estimation Least squares approximation Probability Uncertainty Laboratories Electronic mail

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