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New Cramér-Rao bound expressions for coprime and other sparse arrays

New Cramér-Rao bound expressions for coprime and other spar...

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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

The Cramér-Rao bound (CRB) offers a lower bound on the variances of unbiased estimates of parameters, e.g., directions of arrival (DOA) in array processing. While there exist landmark papers on the study of the CRB in the context of array processing, the closed-form expressions available in the literature are not easy to use in the context of sparse arrays (such as minimum redundancy arrays (MRAs), nested arrays, or coprime arrays) for which the number of identifiable sources D exceeds the number of sensors N. Under such situations, the existing literature does not spell out the conditions under which the Fisher information matrix is nonsingular, or the condition under which specific closed-form expressions for the CRB remain valid. This paper derives a new expression for the CRB to fill this gap. The conditions for validity of this expression are expressed as the rank condition of a matrix defined based on the difference coarray. The rank condition and the closed-form expression lead to a number of new insights. For example, it is possible to prove the previously known experimental observation that, when there are more sources than sensors, the CRB stagnates to a constant value as the SNR tends to infinity.

关键词： sensor arrays signal to noise ratio Closed-form solutions Covariance matrices Direction-of-arrival estimation Sparse matrices

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Joint frequency and direction of arrival estimation with space-time array

Joint frequency and direction of arrival estimation with spa...

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sensor array and multichannel signal processing workshop

作者： A Anil Kumar Sirajudeen Gulam Razul M Girish Chandra Chong Meng See P Balamuralidhar TCS Innovation Labs Bangalore India Temasek Laboratories@NTU Nanyang Technological University Singapore

ISBN: (纸本)9781509021048

Joint frequency and DOA estimation of more sources than the number of sensors is considered in this paper. We assume a simple uniform linear array (ULA) and propose to employ a multiple delay channel network at every sensor, which can easily be realized by sampling at a slightly higher rate. By combining the outputs of the ULA and the delay network, we show that the manifold matrix is analogous to that of a uniform rectangular array, and hence we appropriately refer to this array as the space-time array. Further, estimation of parameters via the rotational invariance technique (ESPRIT) based algorithm referred to as space-time (ST)-Euler-ESPRIT is proposed. ST-Euler-ESPRIT, similar to well known unitary-ESPRIT provides automatically paired frequencies and their DOAs. We further show that with the proposed approach for a M element ULA and with N-1 delay channel, O(MN) frequencies and their DOAs can be estimated. The performance of ST-Euler-ESPRIT is verified by simulations, where it shows consistently better performance than the unitary-ESPRIT algorithm under noisy conditions.

关键词： Delays Direction-of-arrival estimation Frequency estimation Estimation sensor arrays Manifolds

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array interpolation based on multivariate adaptive regression splines

Array interpolation based on multivariate adaptive regressio...

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sensor array and multichannel signal processing workshop

作者： Marco A. M. Marinho João Paulo C. L. da Costa Felix Antreich André L. F. de Almeida Giovanni Del Galdo Edison Pignaton de Freitas Alexey Vinel Department of Electrical Engineering (ENE) University of Brasília (UnB) Fraunhofer Institute for Integrated Circuits IIS Erlangen Germany Institute for Information Technology Ilmenau University of Technology Ilmenau Germany Institute for Communications and Navigation German Aerospace Center (DLR) Department of Teleinformatics Engineering Federal University of Ceará (UFC) Fortaleza Brazil Informatics Institute Federal University of Rio Grande do Sul (UFRGS) IDE Halmstad University Halmstad Sweden

Many important signal processing techniques such as Spatial Smoothing, Forward Backward Averaging and Root-MUSIC, rely on antenna arrays with specific and precise structures. arrays with such ideal structures, such as a centro-hermitian structure, are often hard to build in practice. array interpolation is used to enable the usage of these techniques with imperfect (not having a centro-hermitian structure) arrays. Most interpolation methods rely on methods based on least squares (LS) to map the output of a perfect virtual array based on the real array. In this work, the usage of Multivariate Adaptive Regression Splines (MARS) is proposed instead of the traditional LS to interpolate arrays with responses largely different from the ideal.

关键词： Mars Interpolation Fasteners Linear antenna arrays Direction-of-arrival estimation Estimation

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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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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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multichannel sparse recovery of complex-valued signals using Huber's criterion 3

Multichannel sparse recovery of complex-valued signals using...

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3rd International workshop on Compressed Sensing Theory and its Application to Radar, Sonar, and Remote Sensing (CoSeRa)

作者： Ollila, Esa Aalto Univ Dept Signal Proc & Acoust POB 13000 FI-00076 Aalto Finland

ISBN: (纸本)9781479974207

In this paper, we generalize Huber's criterion to multichannel sparse recovery problem of complex-valued measurements where the objective is to find good recovery of jointly sparse unknown signal vectors from the given multiple measurement vectors which are different linear combinations of the same known elementary vectors. This requires careful characterization of robust complex-valued loss functions as well as Huber's criterion function for the multivariate sparse regression problem. We devise a greedy algorithm based on simultaneous normalized iterative hard thresholding (SNIHT) algorithm. Unlike the conventional SNIHT method, our algorithm, referred to as HUB-SNIHT, is robust under heavy-tailed non-Gaussian noise conditions, yet has a negligible performance loss compared to SNIHT under Gaussian noise. Usefulness of the method is illustrated in source localization application with sensor arrays.

关键词： Gaussian noise array signal processing iterative methods regression analysis Huber criterion SNIHT algorithm complex-valued signal multichannel sparse recovery elementary vector linear combination greedy algorithm heavy-tailed nonGaussian noise condition multiple measurement vector multivariate sparse regression problem sensor array simultaneous normalized iterative hard thresholding algorithm source localization application Brain modeling Compressed sensing Conferences Radar Robustness Sparse matrices Yttrium Gaussian noise greedy algorithm Brain modeling sensor arrays Sparse matrix array signal processing Compressed sensing iterative methods Rare earths yttrium radar regression analysis Robustness

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2014 ieee 8th sensor array and multichannel signal processing workshop, SAM 2014

2014 IEEE 8th Sensor Array and Multichannel Signal Processin...

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2014 ieee 8th sensor array and multichannel signal processing workshop, SAM 2014

ISBN: (纸本)9781479914814

The proceedings contain 134 papers. The topics discussed include: decentralized direction finding using Lanczos method;on MSE performance of time-reversal MUSIC;optimal virtual array length under position imperfections;direction finding and array calibration based on sparse reconstruction in partly calibrated arrays;analytical esprit-based performance study: what can we gain from non-circular sources?;direction of arrival estimation of harmonic signal using single moving sensor;network design via modular set function optimization in localization problems;cooperative simultaneous localization and synchronization: toward a low-cost hardware implementation;preprocessing algorithm for source localisation in a multipath environment;a weighted KNN epipolar geometry-based approach for vision-based indoor localization using smartphone cameras;and improving the projection method for TOA source localization in the presence of sensor position errors.

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