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Finite sample identifiability of multiple constant modulus sources

Finite sample identifiability of multiple constant modulus s...

引用

sensor array and multichannel signal processing workshop

作者： A. Leshem N. Petrochilos A.-J. van der Veen Department ITS/Electrical Engineering Delft University of Technnology Delft Netherlands

We prove that mixtures of continuous constant modulus sources can be identified with probability 1 with a finite number of samples (under noise-free conditions). This strengthens earlier results which only considered an infinite number of samples. The proof is based on the linearization technique of the Analytical Constant Modulus Algorithm, together with a simple inductive argument. We then study the finite alphabet case. In this case we provide an upper bound on the probability of non-identifiability for finite sample of sources. We show that under practical assumptions, this upper bound is tighter than the currently known bound.

关键词： signal processing signal processing algorithms Upper bound sensor arrays Linearization techniques Cost function Binary phase shift keying Constellation diagram Vectors Algorithm design and analysis

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Collaborative classification applications in sensor networks

Collaborative classification applications in sensor networks

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sensor array and multichannel signal processing workshop

作者： C. Meesookho S. Narayanan C.S. Raghavendra Department of Electrical Engineering University of Southern California USA

Distributed sensor networks are a significant technology nowadays. Inexpensive, smart devices with multiple sensors provide opportunities for instrumenting, monitoring and controlling targeting systems. Such sensor nodes have capability for acquiring and embedded-processing of variety of data forms. Collaborative signal processing and fusion algorithms are needed to aggregate the distributed data from among the nodes in the network, including possibly multiple modalities of data within a sensor node, to make decisions in a reliable and efficient manner. One of the important sensor network applications is target classification in battlefields. This paper presents improved moving vehicle target classification performance using data obtained from sensor networks with collaboration both across nodes and within a node in terms of multimodal fusion. Results show that a 50% relative improvement in classification error can be obtained using collaboration both in the case of single vehicle target and those involving multi-vehicle convoys.

关键词： Collaboration Intelligent sensors signal processing algorithms sensor fusion Vehicles sensor systems Instruments Monitoring Control systems Aggregates

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A new model of source dependent noise for robust array signal processing

A new model of source dependent noise for robust array signa...

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sensor array and multichannel signal processing workshop

作者： P.G. Georgiou C. Kyriakakis Immersive Audio Laboratory-Integrated Media Systems Center University of Southern California Los Angeles CA USA

We investigate an alternative model for signals encountered in acoustic environments to the traditional Gaussian process. The sound signals in this case are assumed to be sub-Gaussian of impulsive nature. The noise encountered in these environments predominantly stems from reverberation or multipath effects, which makes it significantly dependent on the source. Hence, the noise is also modeled as jointly sub-Gaussian. The Levy alpha-stable distribution, of characteristic exponent 0.5 and index of symmetry 1, is used together with a multivariate Gaussian density to derive the sub-Gaussian process. Based on this density, the stochastic Maximum Likelihood (ML) estimator is formulated. A separable solution of the estimator is given. Subsequently, simulations demonstrating the performance gains relative to the Gaussian-based ML estimator are provided, as well as a comparison of the two methods on localization of real sounds gathered with a 20- and 41-microphone arrays.

关键词： Noise robustness array signal processing Acoustic noise Working environment noise Maximum likelihood estimation Gaussian processes Acoustic signal processing signal processing Reverberation Stochastic resonance

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Blind channel estimation in multicode CDMA system with antenna array

Blind channel estimation in multicode CDMA system with anten...

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sensor array and multichannel signal processing workshop

作者： M. Melvasalo V. Koivunen Signal Processing Laboratory Helsinki University of Technology Finland

This paper addresses the problem of blind channel estimation in a multicode DS-CDMA system in multipath environment using antenna array at the receiver. Long code CDMA system model is employed. Multicode transmission is used to provide a higher data rate to a mobile user and receiver antenna array improves the signal to interference and noise ratio (SINR). First we extend blind multicode channel estimation methods to CDMA receiver with multiple antennas. Subspace channel estimation is employed with two different pre-processing schemes. We also extend the principal component channel estimation method used for antenna array CDMA receiver to a multicode model. The performances of the proposed methods are compared in simulation using mean square error measure and bit error rates. Different numbers of interfering users are used in simulations and the impact of code selection is considered.

关键词： Blind equalizers Multiaccess communication Antenna arrays Receiving antennas Channel estimation signal to noise ratio Mobile antennas Transmitting antennas Interference Working environment noise

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Secondary data partitioning via maximum likelihood estimation: extensions

Secondary data partitioning via maximum likelihood estimatio...

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sensor array and multichannel signal processing workshop

作者： D.J. Rabideau Lincoln Laboratory MIT Lexington MA USA

Adaptive beamforming based on sample matrix inversion requires the availability of a secondary data set for "training" (i.e., estimating the sample covariance matrix). For each snapshot processed, near-optimal detection performance can be achieved as long as the training data is independent, identically distributed, and free of target-like signals. Oftentimes, however, the available set of secondary data is contaminated by an unknown heterogeneous mixture of clutter, jamming, electromagnetic interference, and strong targets. Previously, the author and his colleagues applied maximum likelihood estimation (MLE) to pre-screen the available secondary data, identifying subsets having similar clutter and jamming statistics, and using these subsets for adaptive beamformer training. This paper extends the basic MLE approach to cases where the secondary data may contain electromagnetic interference and/or strong targets.

关键词： Maximum likelihood estimation Covariance matrix Clutter Jamming Electromagnetic interference array signal processing Availability Training data signal processing Statistics

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Suppression of moving interference with SIS-based projections, in shallow water environments

Suppression of moving interference with SIS-based projection...

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sensor array and multichannel signal processing workshop

作者： S. Kraut J.L. Krolik Department of ECE Duke University Durham NC USA

We present an approach for suppressing moving interference in shallow water environments. Adaptive beamforming techniques are based upon applying a covariance matrix inverse or projection operator to the data, in order to suppress noise and interference. Their performance typically degrades in the presence of moving interference, since more adaptive degrees of freedom are needed to suppress it. In this work we propose a method for obtaining a projection operator that dynamically evolves with time. The method uses sequential importance sampling (SIS) to obtain sequences of wavefronts based on hypothesized velocities and depths. The sequences are associated with weights whose magnitude indicates how well they represent the data. We base the projection operator on the dominant sequences at surface, reducing the rank required for nulling of the surface interferer.

关键词： Interference suppression sensor arrays array signal processing Covariance matrix Degradation Monte Carlo methods Sonar Marine vehicles Telephony Working environment noise

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array configuration design for space-time adaptive processing systems

Array configuration design for space-time adaptive processin...

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sensor array and multichannel signal processing workshop

作者： K. Yang N. Zhang T. Ohira ATR Adaptive Communication Research Laboratories Soraku-gun Kyoto Japan Department of Electrical and Computer Engineering Villanova University Villanova PA USA

It is known that the design of antenna array configuration is an important issue to the antenna array applications. We present a method to design antenna array configurations for space-time adaptive processing (STAP) applied to mobile communication systems. We first derive an upper bound of the residual error power of STAP systems under the minimum mean square error (MMSE) criterion. Then, the relationship between the bound and the space-time channel auto- and cross-correlation functions is established. According to this relationship, an objective function under perfect Monte Carlo sampling is proposed, based on which the array configuration that optimizes the performance is thus obtained. Numerical simulation results under a given statistical signal environment show the effectiveness of the proposed design method.

关键词： Adaptive arrays Adaptive systems Antenna arrays Design methodology Mobile antennas Mobile communication Upper bound Mean square error methods Monte Carlo methods Numerical simulation

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Total variation regularized velocity estimation to image subsurface reflectivity

Total variation regularized velocity estimation to image sub...

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sensor array and multichannel signal processing workshop

作者： X. Xu E.L. Miller Department of Electrical and Computer Engineering Northeastern University Boston MA USA

We propose a modified frequency-wavenumber migration method to enhance subsurface imagery obtained by ground penetrating radar (GPR) array to localize buried landmines and other objects. The method aims to minimize the entropy of processed image by introducing different wave propagation velocity for each array element. The different wave propagation velocity is organized to form a velocity vector. On the other hand, by constraining the total variation of the velocity vector, the method satisfies physics requirement that the velocity shall not change abruptly. The optimization scheme is built in the framework of Tikhonov regularization by selecting proper regularization parameter. We use examples obtained from 2-D GPR images. The frequency-wavenumber method and its optimization can be generalized to 3-D in a straight-forward manner.

关键词： Reflectivity sensor arrays Buried object detection Surface waves Ground penetrating radar Landmine detection Electromagnetic scattering Soil Partial differential equations Entropy

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The MIT Lincoln Laboratory KASSPER algorithm testbed and baseline algorithm suite

The MIT Lincoln Laboratory KASSPER algorithm testbed and bas...

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sensor array and multichannel signal processing workshop

作者： D.E. Kreithen N.B. Pulsone C.M. Rader G.E. Schrader Lincoln Laboratory MIT Lexington MA USA

MIT Lincoln Laboratory is investigating how to use a-priori information in an adaptive MTI radar under the DARPA-funded knowledge-aided sensor signal processing and expert reasoning (KASSPER) program. Our objective is to demonstrate an embedded processor and some algorithms capable of real-time space-time adaptive processing (STAP) enhanced by a-priori knowledge of terrain types, locations of clutter discretes and road layouts. The immediate goal is to realize a baseline system, which is a conventional STAP chain making no use of a-priori information. It is designed to allow the entire KASSPER community to insert new algorithm features that exploit a-priori knowledge. The paper describes both the hardware and the algorithms that are being coded to realize the baseline system. This baseline algorithm suite is intended to provide a reasonable starting point for adding a-priori knowledge in the form of new training schemes or new STAP algorithms to improve target detectability near the mainbeam clutter ridge.

关键词： Laboratories Testing signal processing algorithms Frequency Clutter array signal processing Linear antenna arrays Covariance matrix Radar signal processing Hardware

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Broadband signal subspace processing for range estimation

Broadband signal subspace processing for range estimation

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sensor array and multichannel signal processing workshop

作者： R. Jeffers K.L. Bell Digital Systems Resources Inc. Fairfax VA USA George Mason University Fairfax VA USA

The problem of broadband range and bearing estimation is considered. Coherent focusing of the data covariance matrix over multiple frequency bins for 2D MUSIC estimation is studied and compared to incoherent summing of the MUSIC cost function over frequency bins. Coherent focusing provides improved performance over incoherent processing in the farfield bearing-only estimation problem, but introduces instabilities when applied to the near-field range and bearing estimation problem. The cost function response in the range parameter is not highly peaked, so initial focusing using even a slightly incorrect range estimate prevents convergence of the range estimate to the true range value. Incoherent processing, on the other hand, is less computationally complex, and achieves the Cramer-Rao lower bound at high SNR.

关键词： signal processing Multiple signal classification Cost function Direction of arrival estimation Frequency estimation Narrowband sensor arrays Covariance matrix Convergence Digital systems

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