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POPLAR: Parafac2 decOmPosition using auxiLiAry infoRmation

POPLAR: Parafac2 decOmPosition using auxiLiAry infoRmation

引用

sensor array and multichannel signal processing workshop

作者： Ekta Gujral Georgios Theocharous Evangelos E. Papalexakis UC Riverside Adobe Inc

ISBN: (数字)9781728119465

ISBN: (纸本)9781728119472

PARAFAC2 is a powerful method for analyzing multi-modal data consisting of irregular frontal slices. In this work, we propose POPLAR method that imposes graph Laplacians constraints induced by the similarity symmetric tensor as auxiliary information to force decomposition factors to behave similarly and the method is developed using AO-ADMM for 3-way PARAFAC2 tensor decomposition. To the best of our knowledge, POPLAR is the first approach to incorporate graph Laplacians constraints using auxiliary information. We extensively evaluate POPLAR’s performance in comparison to state-of-the-art approaches across synthetic and real dataset, and POPLAR clearly exhibits better performance with respect to the Fitness (better 3−8%), and F1 score (better 5−20%) among the state-of-the-art factorization method. Furthermore, the running time for the method is comparable to the state-of-art method.

关键词： Tensors Laplace equations array signal processing Conferences Force sensor arrays

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Adaptive array Filter Using a Combined QLMS-CLMS Algorithm

Adaptive Array Filter Using a Combined QLMS-CLMS Algorithm

引用

2011 3rd International Conference on signal processing Systems(ICSPS 2011)

作者： Zhiyong Lin Jianwu Tao Fei Yu Department of Control Engineering Aviation University

ISBN: (纸本)9781457701726

A new adaptive filter, called quaternion least mean square plus complex least mean square (QLMS-CLMS) filter, is proposed for an electromagnetic vector sensor array. The QLMS-CLMS filter involves the use of two LMS-based sections. The first one is the quaternion LMS algorithm, the other is the complex LMS algorithm. Computer simulation results show that QLMS-CLMS filter is superior in the output signal-to-noise ratio over earlier CLMS filter. In addition, the proposed QLMS-CLMS algorithm is robust when the interference signal is coherent with the desired signal.

关键词： Adaptive filter Convergence QLMS-CLMS CLMS SINR Stepsize

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Improving distributed space-time coding through combining at the multiple-antenna relay

Improving distributed space-time coding through combining at...

引用

ieee International workshop on Computational Advances in Multi-sensor Adaptive processing (CAMSAP)

作者： Yindi Jing University of Alberta Canada

ISBN: (纸本)9781424451791;9781424451807

Distributed space-time coding (DSTC) is a cooperative scheme proposed for wireless relay networks that achieves full diversity when the transmit power is infinitely high. In this paper, we propose to employ maximum-ratio combining (MRC) at the multiple-antenna relay to improve the performance of DSTC. Simulations and analysis show that the new scheme achieves a much better performance in relay networks than DSTC. In some network scenarios, it can even improve the scaling of the error rate with the transmit power. Furthermore, the new scheme requires a shorter training interval than DSTC.

关键词： Relays Receiving antennas Transmitting antennas signal processing Transmitters Diversity reception Error analysis Analytical models Performance analysis signal design

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Antenna Placement in Compressive Sensing Radar using Binary Optimization

Antenna Placement in Compressive Sensing Radar using Binary ...

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sensor array and multichannel signal processing workshop

作者： Adnan Hamida Mohammed Saif Jun Li Shahrokh Valaee Electrical and Computer Engineering University of Toronto Toronto ON Canada NXP Semiconductors San Jose CA USA

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

Compressive sensing has allowed for the improve-ment of angular resolution in radar technology, which involves two aspects: sparse signal recovery and measurement matrix design. Assuming a sparse target scene, compressive sensing radar depends solely on the design of the measurement matrix to possess certain properties, such as satisfying the restricted isometry property (RIP) and low coherence. The design of the measurement matrix depends on the location of the antennas. In this work, we consider the antenna placement problem in compressive sensing radar. The problem is interpreted as a binary program, where we propose to solve it directly using a heuristic binary optimization algorithm. The proposed binary differen-tial evolution (BDE) algorithm is able to navigate the search space with relatively high diversity while still refining promising candidates. Results illustrate the superiority of approaching the problem directly using BDE rather than resorting to relaxation approaches in the literature.

关键词： Antenna measurements Radar measurements signal processing algorithms Radar Coherence Radar antennas Sparse matrices

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A transceive strategy for regenerative multi-antenna multi-way relaying

A transceive strategy for regenerative multi-antenna multi-w...

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ieee International workshop on Computational Advances in Multi-sensor Adaptive processing (CAMSAP)

作者： Aditya Umbu Tana Amah Anja Klein Communications Engineering Laboratory Technische Universität Darmstadt Darmstadt Germany

In this paper, firstly, we introduce regenerative multi-way relaying. A half-duplex multi-antenna relay station (RS) assists multiple nodes which want to communicate to each other. Each node has a message and wants to decode the messages from all other nodes. The number of communication phases is equal to the number of nodes, N, such that when N = 2, we have the well known two-way relaying. In the first phase, multiple access (MAC) phase, all nodes transmit simultaneously to the RS and the RS decodes the data streams of all nodes. In the following (N - 1) broadcast (BC) phases, the RS transmits to each node the intended (N - 1) data streams from the other (N -1) nodes. Secondly, we propose a transceive strategy at the RS which couples the MAC and the BC phases. The strategy exploits the information about the achievable MAC signal to interference and noise ratio at the RS which enables the RS to transmit with the achievable MAC rate in each BC phase. As the strategy involves transmit beamforming at the RS, we intend to minimise the power at the RS. Since the power minimisation problem is NP-hard, we rewrite the problem into a semidefinite optimisation to solve it in reasonable time.

关键词： Relays Decoding array signal processing Bidirectional control Conferences Regeneration engineering Broadcasting Interference signal to noise ratio Phase noise

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Blind Detection, Separation, and Location of Dense Co-Channel Emitters using Multiplatform Spatial-Coherence Restoral

Blind Detection, Separation, and Location of Dense Co-Channe...

引用

sensor array and multichannel signal processing workshop

作者： B.G. Agee B3 Advanced Communication Systems San Jose CA USA

The multiplatform spatial-coherence restoral (MP-SCORE) algorithm, introduced in 1988 to blindly separate spatially-coherent, temporally featureless signals based on their differing TDOA and FDOA at spatially separated antenna arrays, is extended to blind detection, separation, and location of dense co-channel emitters using wide-baseline collection networks. The original MP-SCORE algorithm is reviewed and related to the ML estimator of the location of a Gaussian-distributed source (or of the location and content of an unknown source) received in the presence of independent Gaussian-distributed interference with arbitrary spatial covariance at each array in the network. False-alarm and miss-rates for the corresponding MP-SCORE detector are also derived, and are shown to be a function of only the time-bandwidth product of the detector and the maximum source SENR attainable at each array in the network. Practical methods for detecting, separating, and locating multiple co-channel sources using MP-SCORE are then presented, and the end-to-end algorithm is demonstrated for a GPS C/A code jamming scenario in which two 8-sensor airborne receivers attempt to detect and locate 100 narrowband (2 MHz) noise-loaded emitters continuously transmitting over a wide (150times150 nmi) geographical region at the center of the GPS L1 frequency band. The simulated algorithm detects and geolocates 27 emitters to within 1/2 nmi over a single 26 ms snapshot, and detects and geolocates 38 emitters to within 445 feet over eleven 26 ms snapshots collected at 15 nmi intervals along the edge of the emission region

关键词： signal restoration Gaussian processes Detectors Global Positioning System Antenna arrays Maximum likelihood estimation Interference sensor arrays Jamming Narrowband

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Methods for factorization and approximation of Tensors by partial Fiber Sampling

Methods for factorization and approximation of Tensors by pa...

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ieee International workshop on Computational Advances in Multi-sensor Adaptive processing (CAMSAP)

作者： Cesar F. Caiafa Andrzej Cichocki Laboratory of Advanced Brain Signal Processing Brain Science Institute Riken Japan Systems Research Institute Warsaw University of Technology Pan Poland

ISBN: (纸本)9781424451791;9781424451807

In this paper we present, discuss and compare new methods for the reconstruction of tensors by partial sampling, i.e. based on the information contained only in a subset of their entries. As a generalization of the CUR matrix decomposition, which approximates a matrix from a subset of its rows and columns, we present two methods called Tree-CUR and FSTD (Fiber Sampling Tensor Decomposition) for estimating a tensor by using a subset of its n-mode fibres, i.e. some row, column and tube fibers for a 3-dimensional tensor case. As our experimental results show, these new methods are potentially useful tools for signal processing with huge datasets since they provide fast algorithms for calculating low-rank approximations without needing to sample the whole dataset.

关键词： Tensile stress Sampling methods signal processing algorithms signal sampling Matrix decomposition Frequency Image analysis signal analysis Conferences Laboratories

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Joint diagonalization: Is non-orthogonal always preferable to orthogonal?

Joint diagonalization: Is non-orthogonal always preferable t...

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ieee International workshop on Computational Advances in Multi-sensor Adaptive processing (CAMSAP)

作者： Antoine Souloumiac CEA LIST Stochastic Processes and Spectra Laboratory CEA Saclay Gif-sur-Yvette France

ISBN: (纸本)9781424451791;9781424451807

This paper deals with the efficiency of orthogonal versus non-orthogonal joint diagonalization techniques for blind source separation. The question is: are non-orthogonal algorithms always preferable to orthogonal ones in practical noisy conditions? The two techniques are compared in terms of separation performance through numerical simulations with different levels of additive noise and numbers of samples. Both fourth-order and second-order blind source separation contexts are investigated.

关键词： Source separation Blind source separation Additive noise Decorrelation sensor phenomena and characterization Covariance matrix Conferences Stochastic processes Laboratories signal processing

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Determining the topology of a telephone system using internally sensed network tomography

Determining the topology of a telephone system using interna...

引用

ieee Digital signal processing workshop

作者： J.R. Treichler M.G. Larimore S.L. Wood M. Rabbat Applied Signal Technology Inc. Sunnyvale CA USA Electrical Engineering Santa Clara University Santa Clara CA USA Electrical Engineering University of Wisconsin Madison WI USA

The paper develops methods of characterizing telephone network topology using a small number of internal network sensors. Using methods inspired by medical imaging, telephone call attempts made from many locations around the world provide the measurements. The ability to determine the topology of worldwide telephone networks offers the promise of substantially improving their operating efficiency. This approach can be extended to determine characteristics of data networks as well.

关键词： Network topology Telephony Tomography Probes Biomedical imaging Biomedical engineering sensor phenomena and characterization Bandwidth Timing Humans

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Basis pursuit with sequential measurements and time varying signals

Basis pursuit with sequential measurements and time varying ...

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ieee International workshop on Computational Advances in Multi-sensor Adaptive processing (CAMSAP)

作者： M. Salman Asif Justin Romberg School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA

ISBN: (纸本)9781424451791;9781424451807

Recovery of sparse signals from linear measurements arises in several signal processing applications. Basis pursuit is a standard convex optimization program, often used to perform this task. In this paper we present two algorithms to dynamically update the solution of basis pursuit as (1) new measurements are sequentially added or (2) the underlying signal changes slightly. The goal is to avoid solving the (computationally expensive) optimization routine every time a small change occurs in the measurements. Our proposed update algorithms are based on homotopy principles, which iteratively update the solution by moving from an already solved problem towards the desired problem. Each homotopy step involves only a few matrix-vector multiplications. Simulation results show that the number of homotopy steps required for the update is comparable to the sparsity of the underlying signals.

关键词： Time measurement signal processing Least squares methods signal processing algorithms Electric variables measurement Computational efficiency Image reconstruction Equations Conferences Adaptive signal processing

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