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Algorithms for discrete nonlinear optimization in FICO Xpress

Algorithms for discrete nonlinear optimization in FICO Xpres...

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sensor array and multichannel signal processing workshop

作者： Pietro Belotti Timo Berthold Kelligton Neves FICO (Fair Isaac Corporation) Birmingham UK FICO (Fair Isaac Corporation) Berlin Germany FICO (Fair Isaac Corporation) Sao Paulo Brazil

ISBN: (纸本)9781509021048

The FICO Xpress-Optimizer is a commercial optimization solver for linear programming (LP), mixed integer linear programming (MIP), convex quadratic programming (QP), convex quadratically constrained quadratic programming (QCQP), second-order cone programming (SOCP) and their mixed-integer counterparts. Xpress also includes a general purpose non-linear solver, Xpress-NonLinear, which features a successive linear programming algorithm (SLP, first-order method), interior point methods and Artelys Knitro (second-order methods). This work explores algorithms for mixed-integer nonlinear programming problems (MINLPs), which are NP-hard in general, then it presents applications in signal processing and capitalizes advances in solving these problems with Xpress and its comprehensive suite of high-performance nonlinear solvers. Computational results show that signal processing nonlinear problems can be solved quickly and accurately, taking advantage of the algebraic modeling and procedural programming language, Xpress-Mosel, that allows to interact with the Xpress solver engines in a easy-to-learn way, and its unified modeling interface for all solvers, from linear to general nonlinear solvers.

关键词： Optimization Linear programming Programming signal processing algorithms signal processing Mathematical model Search problems

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Rectified ALS algorithm for multidimensional harmonic retrieval

Rectified ALS algorithm for multidimensional harmonic retrie...

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sensor array and multichannel signal processing workshop

作者： Rémy Boyer Pierre Comon Signals and Statistics Departement CNRS Gif-SurYvette France Institut NEEL Grenoble RhÃ´ne-Alpes FR

ISBN: (纸本)9781509021048

MultiDimensional (MD) Harmonic Retrieval is a challenging multi-parameter estimation problem and is useful for a plethora of operational applications as for instance channel sounding or MIMO radar processing. The MD-harmonic model follows a structured Canonical Polyadic Decomposition (CPD) in the sense that the factors of the CPD are Vandermonde. A standard and popular estimation scheme to derive the CPD is the Alternating Least Squares (ALS) algorithm. Unfortunately, the ALS algorithm does not exploit the a priori known factor structure, which considerably degrades the estimation performance. In this work, a modified ALS-type algorithm is proposed. This new algorithm, called Rectified ALS (RecALS), is able to take into account the Vandermonde structure of the factors. The RecALS algorithm belongs to the Lift-and-Project family and exploits iterated projections on the set of Toeplitz rank-1 matrices. It exhibits a fast convergence and is very accurate in the sense that its Mean Square Error (MSE) is close to the Cramér-Rao Bound for a wide range of signal to Noise Ratio (SNR).

关键词： Approximation algorithms Tensile stress signal processing algorithms Estimation Standards Matrix decomposition Convergence

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Antenna selection in massive MIMO systems: Full-array selection or subarray selection?

Antenna selection in massive MIMO systems: Full-array select...

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sensor array and multichannel signal processing workshop

作者： Yuan Gao Thomas Kaiser Institute of Digital Signal Processing University of Duisburg-Essen Duisburg Germany

ISBN: (纸本)9781509021048

This paper presents a concept of subarray antenna selection in massive MIMO systems, i.e., the whole antenna array is segmented into subarrays and only one antenna is selected in each subarray. The subarray antenna selection can reduce the hardware complexity of the RF switching network, but at the cost of performance degradation due to the introduced selection constraint. We evaluate such performance degradation not only with the ideal independent and identically distributed (i.i.d.) but also with the realistic measured channels. To serve our evaluation, an antenna selection algorithm based on branch and bound method is presented, which aims to select an optimal channel submatrix with the largest minimum singular value. A qualitative analysis is also given to compare the performance between the full-array and subarray selection. Simulation results show that the massive MIMO subarray antenna selection has close performance to the full-array antenna selection, no matter with the i.i.d. channels or the realistic channels. The results in this paper imply that the with the concept of subarray antenna selection, antenna selection is still a practical and promising technology in the realistic massive MIMO systems.

关键词： Receiving antennas MIMO Antenna arrays Transmitting antennas Random variables Radio frequency

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High order super nested arrays

High order super nested arrays

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sensor array and multichannel signal processing workshop

作者： Chun-Lin Liu P. P. Vaidyanathan Dept. of Electrical Engineering 136-93 California Institute of Technology Pasadena CA USA

ISBN: (纸本)9781509021048

Mutual coupling between sensors has a negative impact on the estimation of directions of arrival (DOAs). Sparse arrays such as nested arrays, coprime arrays, and minimum redundancy arrays (MRAs) have less mutual coupling than uniform linear arrays (ULAs). These sparse arrays also have a difference coarray of size O(N 2 ), where N is the number of sensors, and can therefore resolve O(N 2 ) uncorrelated source directions. The various sparse arrays proposed in the literature have their pros and cons. The nested array is practical and easy to use but has a dense ULA part which suffers from mutual coupling effects like the traditional ULA. The recently introduced super nested arrays reduce this mutual coupling problem, while maintaining the desirable hole-free O(N 2 ) difference coarray of the nested array. In this paper, a generalization of super nested arrays is introduced, called the Qth-order super nested array. This has all the properties of the second-order super nested array with the additional advantage that mutual coupling effects are further reduced for Q > 2. A numerical example is included to demonstrate the superior performance of these arrays. 1

关键词： sensor arrays Mutual coupling sensor phenomena and characterization Direction-of-arrival estimation Couplings Estimation

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Interpolation-and-decimation-based dimensionality reduction applied to space-time processing

Interpolation-and-decimation-based dimensionality reduction ...

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sensor array and multichannel signal processing workshop

作者： Aline de Oliveira Raimundo Sampaio-Neto José Mauro Fortes Fabian Backx PUC-Rio Instituto de Pesquisas da Marinha (IPqM) Rio de Janeiro Brasil PUC-Rio Rio de Janeiro Brasil Instituto de Pesquisas da Marinha (IPqM) Rio de Janeiro Brasil

ISBN: (纸本)9781509021048

In this paper, we specialize a novel stage of dimensionality reduction, based on a joint interpolation and decimation scheme (JIDS), proposed in [1] and [2], for airborne phased-array radars. The JIDS design approach consists of a very elegant and effective way to obtain the joint interpolation filter and decimation pattern, taking advantage of the correlation generated by the interpolation filter in order to eliminate samples and still achieve low signal to interference and noise ratio (SINR) loss. We compare its results in terms of probability of detection and Doppler performance with the full rank Minimum Variance Distortionless Response sample matrix inversion (MVDR-SMI) space-time filter and other rank reduction techniques such as Principal Components (PC), Cross-Spectral Metric (CSM) and Multistage Wiener Filter (MWF). Simulation results show that the proposed approach has an impressive ability to significantly reduce the length of the space-time snapshots and achieves very good performance in Doppler SINR loss and probability of detection, especially in sample starving scenarios.

关键词： Interpolation signal to noise ratio Covariance matrices Clutter Eigenvalues and eigenfunctions Doppler effect

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Low-complexity robust radar-embedded Sidelobe Level modulation using linear constrained optimization design

Low-complexity robust radar-embedded Sidelobe Level modulati...

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sensor array and multichannel signal processing workshop

作者： Aline de Oliveira Raimundo Sampaio Neto José Mauro Fortes PUC-Rio and IPqM Rio de Janeiro Brazil Pontificia Universidade Catolica do Rio de Janeiro Rio de Janeiro RJ BR PUC-Rio Rio de Janeiro Brazil

In this paper, we propose a low-complexity robust radar-embedded Sidelobe Level (SLL) modulation for a communication system as a secondary task for the radar. The robust radar-embedded SLL modulation aims the generation of transmit beampatterns, that satisfactorily match a given transmit profile (with high fidelity adjustment at the mainlobe), where each beampattern embeds a different SLL towards the communication receiver direction and sustains its amplitude over a small angular region. We achieve these goals by modifying the robust SLL optimization problem enunciated in [1], in order to handle differently the mainlobe adjustment requirement. The proposed formulation is based on eigenvector, point and first derivative constraints. We derive its closed form solution and we simplify it through mathematical manipulation and eigenspectrum analysis. These procedures make the proposed solution suitable for online real-time processing. We also derive simple equations for updating the beampatterns for following a moving communication receiver platform.

关键词： Radar Receivers Modulation Eigenvalues and eigenfunctions Robustness Optimization array signal processing

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Low-complexity correlated time-averaged variable forgetting factor mechanism for diffusion RLS algorithm in sensor networks

Low-complexity correlated time-averaged variable forgetting ...

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sensor array and multichannel signal processing workshop

作者： Ling Zhang Yunlong Cai Chunguang Li Rodrigo C. de Lamare Minjian Zhao Department of Information Science and Electronic Engineering Zhejiang University Hangzhou China CETUC-PUC-Rio Rio de Janeiro Brazil

ISBN: (纸本)9781509021048

In this work, we present the low-complexity variable forgetting factor (VFF) technique for the diffusion recursive least squares (RLS) algorithm. In particular, we adopt the VFF mechanism that rely on time-averages of the posteriori error signal and incorporate it into the diffusion RLS (DRLS) algorithm to yield the low-complexity correlated time-averaged VFF diffusion RLS (LCTVFF-DRLS) algorithm. We develop detailed analyses in terms of mean and mean square performance for the proposed algorithm, and derive mathematical expressions to compute the mean square deviation (MSD) and the excess mean square error (EMSE). The simulation results show that the proposed LCTVFF-DRLS algorithm outperforms the existing DRLS algorithm with the fixed forgetting factor, and demonstrate a good match between our proposed analytical expressions and simulated results.

关键词： wireless sensor networks computational complexity least squares approximations recursive estimation recursive estimation least squares approximations complexity classes Wireless sensor networks Restless Legs Syndrome Voice Frequency Filter forgetting average time Mathematical Formulas Mean square error diffusion

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Location estimation in multipath environments with unsynchronized base stations

Location estimation in multipath environments with unsynchro...

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sensor array and multichannel signal processing workshop

作者： Oded Bialer Dan Raphaeli Anthony J. Weiss Department of Electrical and Computer Engineering University of Toronto Toronto ON Canada Department of Industrial Electrical Power Conversion University of Malta Msida Malta

ISBN: (纸本)9781509021048

Position estimation of a target node based on time-of-flight measurements in multipath environments is challenging, especially when the signal bandwidth is limited, such as is the case in WIFI 802.11. Furthermore, the availability of location-based services in today's wireless networks is still limited, since it requires either the overhead of synchronizing the base stations (BSs) or applying modification in the BSs protocols to support round trip delay measurements. In this paper, an algorithm for location estimation of a target node in a multipath environment based on one-way transmission of OFDM signals from unsynchronized base stations is developed. The solution approach is based on using a reference node with known position, which also receives the BSs transmission, and deriving an approximate maximum likelihood position estimation of the target node based on the collected samples at both the target and the reference nodes. We demonstrate the algorithm's performance with off-the-shelf WIFI 802.11g routers, which are unsynchronized and do not include any dedicated protocol for range or location estimation. The novel algorithm attains a position root mean square error of 1.7m and a position norm error that is smaller than 3m in 90%.

关键词： OFDM Delays Base stations ieee 802.11 Standard Wireless networks Maximum likelihood estimation

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Enhanced block term decomposition for atrial activity extraction in atrial fibrillation ECG

Enhanced block term decomposition for atrial activity extrac...

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sensor array and multichannel signal processing workshop

作者： Lucas N. Ribeiro André L. F. de Almeida Vicente Zarzoso Federal University of Ceará Fortaleza Brazil I3S Laboratory Institut Universitaire de France France

ISBN: (纸本)9781509021048

Atrial fibrillation (AF) is the most prevalent sustained cardiac arrhythmia but is still considered a challenging research subject since its electrophysiological mechanisms are not yet fully understood. Analyzing the atrial activity (AA) signal observed in surface electrocardiograms (ECG) is useful for clinical management and better understanding the propagation mechanisms inside the atria, but ventricular activity (VA) masks the AA in time and frequency domains. signal processing techniques have been used to extract the AA signal. Blind Source Separation (BSS) methods can accomplish this task from multi-lead ECG. Recently, a deterministic tensor-based BSS method based on the Block Term Decomposition (BTD) was proposed and offered promising results in AA estimation. This method assumes that AF ECG leads can be expressed as linear combinations of damped exponential sources. However, QRST complexes of VA do not match this model, causing numerical issues. The present contribution proposes a Principal Component Analysis (PCA) preprocessing stage to attenuate the ventricular components. Experimental results show that this stage alleviates the ECG model mismatch, resulting in better AA estimation compared to competing methods and improved numerical properties compared to BTD without preprocessing.

关键词： Electrocardiography Principal component analysis Tensile stress Matrix decomposition Convergence signal processing Estimation

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Analytical performance evaluation of multi-dimensional Tensor-ESPRIT-based algorithms for strictly non-circular sources

Analytical performance evaluation of multi-dimensional Tenso...

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sensor array and multichannel signal processing workshop

作者： Jens Steinwandt Florian Roemer Martin Haardt Communications Research Laboratory Ilmenau University of Technology P.O. Box 100565 98684 Ilmenau Germany Digital Broadcasting Laboratory Ilmenau University of Technology P.O. Box 100565 98684 Ilmenau Germany

ISBN: (纸本)9781509021048

Exploiting inherent signal structure is a common approach towards improving the performance of conventional parameter estimation algorithms. It has recently been shown that the multi-dimensional (RD) nature of the signals and their statistical properties, i.e., their second-order (SO) strictly non-circular (NC) structure, can be exploited simultaneously by R-D NC Tensor-ESPRIT-type algorithms. In this contribution, we develop an analytical first-order performance evaluation of R-D NC Standard Tensor-ESPRIT and R-D NC Unitary Tensor-ESPRIT. The derived expressions are asymptotic in the effective signal-to-noise ratio (SNR), i.e., they become exact for high SNRs or a large sample size. Moreover, apart from a zero mean and finite SO moments, no assumptions on the noise statistics are required. We show that as in the corresponding NC matrix case, the performance of R-D NC Standard Tensor-ESPRIT and R-D NC Unitary Tensor-ESPRIT is asymptotically identical. Simulations verify the derived expressions.

关键词： Tensile stress Standards signal processing algorithms Parameter estimation Algorithm design and analysis sensors Performance evaluation

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