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共 1267 条记录，以下是551-560 订阅

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Exploiting array Motion for Augmentation of Co-prime arrays

Exploiting Array Motion for Augmentation of Co-prime Arrays

引用

8th ieee sensor array and multichannel signal processing workshop (SAM)

作者： Ramirez, Juan, Jr. Odom, Jonathan Krolik, Jeffrey Duke Univ Dept Elect Comp Engn Durham NC 27708 USA

ISBN: (纸本)9781479914814

In this paper, we combine concepts from synthetic aperture processing and non-uniform linear array theory. Our objective is to use array motion to synthesize an array that can achieve the performance of a filled uniform linear array (ULA), but with fewer sensors than required for spatial Nyquist sampling. The class of physical arrays we use for synthesis are co-prime arrays constructed by nesting under-sampled ULA's with co-prime inter-element spacings. In particular, we use array motion to fill in missing co-array spacings. For co-prime M and N, a physical array of M + 2 N - 1 sensors plus modest array motion is used to achieve a filled co-array corresponding to approximately 2 M N elements. This facilitates spatial spectral estimation via non-adaptive beamforming over an extended aperture with low sidelobe performance.

关键词： Spectrum analysis

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A Moving Horizon Convex Relaxation for Mobile sensor Network Localization

A Moving Horizon Convex Relaxation for Mobile Sensor Network...

引用

8th ieee sensor array and multichannel signal processing workshop (SAM)

作者： Simonetto, Andrea Leus, Geert Delft Univ Technol Fac EEMCS NL-2826 CD Delft Netherlands

ISBN: (纸本)9781479914814

In mobile sensor network localization problems we seek to estimate the position of the mobile sensor nodes by using a subset of pair-wise range measurements (among the nodes and with mobile anchors). When the sensor nodes are static, convex relaxations have been shown to provide a remarkably accurate approximate solution to this NP-hard estimation problem. In this paper, we propose a novel convex relaxation to tackle the more challenging dynamic case and we develop a moving horizon convex estimator based on a maximum a posteriori (MAP) formulation. The resulting estimator is then compared to standard extended and unscented Kalman filters both with respect to computational complexity and performance with simulated data. The results are promising, yet a more detailed analysis is needed.

关键词： sensor networks Convexity Mobile Ganglia Kalman filters Horizon complexity classes Moving Relaxation

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RIDGE REGRESSION AND KALMAN FILTERING FOR TARGET TRACKING IN WIRELESS sensor NETWORKS

RIDGE REGRESSION AND KALMAN FILTERING FOR TARGET TRACKING IN...

引用

8th ieee sensor array and multichannel signal processing workshop (SAM)

作者： Mahfouz, Sandy Mourad-Chehade, Farah Honeine, Paul Farah, Joumana Snoussi, Hichem Univ Technol Troyes Inst Charles Delaunay CNRS Troyes France Univ St Esprit Kaslik Fac Engn Dept Telecommun Juniyah Lebanon

ISBN: (纸本)9781479914814

This paper introduces an original method for target tracking in wireless sensor networks that combines machine learning and Kalman filtering. A database of radio-fingerprints is used, along with the ridge regression learning method, to compute a model that takes as input RSSI information, and yields, as output, the positions where the RSSIs are measured. This model leads to a position estimate for each target. The Kalman filter is used afterwards to combine the model's estimates with predictions of the target's positions based on acceleration information, leading to more accurate ones.

关键词： radio-fingerprinting Kalman filter ridge regression RSSI tracking WSN

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Distributed Source processing with Linear Nested arrays

Distributed Source Processing with Linear Nested Arrays

引用

8th ieee sensor array and multichannel signal processing workshop (SAM)

作者： Han, Keyong Nehorai, Arye Washington Univ Preston M Green Dept Elect & Syst Engn St Louis MO 63130 USA

ISBN: (纸本)9781479914814

We consider the problem of using linear nested arrays to estimate the direction of arrival (DOA) of spatially distributed sources and detect the source number, where we have more sources than the actual physical sensors. Angular spread, caused by the multipath nature of distributed sources, makes the commonly used point source assumption ineffective. In this paper, we establish the signal model for distributed sources using a nested array. Due to the characteristics of distributed sources, the regular spatial smoothing technique, which is used to exploit the increased degrees of freedom provided by the co-array, no longer works. We thus propose a novel spatial smoothing approach to circumvent this problem. Based on the analytical results, we construct corresponding DOA estimation and source number detection methods. The effectiveness of the proposed methods is verified through numerical examples.

关键词： Distributed source nested array direction of arrival estimation source number detection

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Localization and array Shape Estimation Using Software Defined Radio array Testbed

Localization and Array Shape Estimation Using Software Defin...

引用

8th ieee sensor array and multichannel signal processing workshop (SAM)

作者： Akindoyin, Akinbiyi Willerton, Marc Manikas, Athanassios Imperial Coll London Elect & Elect Engn London England

ISBN: (纸本)9781479914814

The emergence of software defined radio (SDR) aims to increase flexibility as well as reduce cost, size, weight and power (SWAP) inherent in traditional hardware radios. This paper is concerned with addressing issues associated with the formation of an array system from multiple SDR boards where each has an independent local oscillators (LO) in a multi-antenna system using representative examples such as localization and array shape estimation. In particular, practical experimental results are initially presented for estimating the unknown location of a single source using an SDR array of known array geometry. Furthermore, in the case that the SDR array geometry is unknown, a novel array shape estimation algorithm is proposed. The proposed algorithm estimates the antenna locations without requiring any external sources. This is achieved by allowing the array elements operate as transceivers.

关键词： Software defined radio array shape estimation localization

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Distributed Ensemble Kalman Filtering

Distributed Ensemble Kalman Filtering

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8th ieee sensor array and multichannel signal processing workshop (SAM)

作者： Shahid, Arslan Uestebay, Deniz Coates, Mark McGill Univ Dept Elect & Comp Engn Montreal PQ H3A 2A7 Canada

ISBN: (纸本)9781479914814

We address the problem of distributed filtering in a wireless sensor network and develop distributed approximations of three variants of the ensemble Kalman filter. We express the update equations in an alternative information form in order to formulate a distributed measurement update mechanism. The distributed filters use randomized gossip to reach consensus on the statistics needed to perform an update. Simulation results suggest that in the case of linear measurements and high-dimensional nonlinear measurements (with measurement model parameters known network-wide) with nonlinear state dynamics the proposed schemes achieve accuracy comparable to state-of-the-art distributed filters while significantly reducing the communication overhead.

关键词： Kalman filtering Distribute Wireless sensor networks Linear measurement Filters Distributed measurement Simulation results Gossip

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Performance Analysis of Time frequency Subspace based Direction Finding algorithms in presence of perturbed array manifold

Performance Analysis of Time frequency Subspace based Direct...

引用

8th ieee sensor array and multichannel signal processing workshop (SAM)

作者： Belouchrani, Adel Khodja, Mohamed Ecole Natl Polytech Dept Elect Engn Algiers Algeria

ISBN: (纸本)9781479914814

Conventional subspace based direction finding approaches such as MUSIC and ESPRIT algorithms commonly use the array data covariance matrix. In non stationary context, the use of the Spatial Time-Frequency Distribution (STFD) instead of the covariance matrix can significantly improve the performance of such algorithms. In this paper we are interested in the performance analysis of such approaches in the presence of both additive noise and perturbed array manifold. An unified expression of the Direction Of Arrival (DOA) error estimation is derived for both approaches. The obtained results show that for low signal to Noise Ratio (SNR) and high signal to sensor Perturbation Ratio (SPR) the STFD based DOA estimations perform better, while for high SNR and for the same SPR both Covariance and STFD based approaches have similar performance.

关键词： direction finding variance covariance matrix low SNR Direction of arrival estimation high SNR Subspace Multiple signal classification Additive noise Manifolds Covariance United States Department of Agriculture based Covariance matrix

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Improving the Projection Method for TOA Source Localization in the Presence of sensor Position Errors

Improving the Projection Method for TOA Source Localization ...

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8th ieee sensor array and multichannel signal processing workshop (SAM)

作者： Li, Jinzhou Ho, K. C. Guo, Fucheng Jiang, Wenli Natl Univ Def Technol Sch Elect Sci & Engn Changsha 410073 Hunan Peoples R China Univ Missouri ECE Dept Columbia MO 65211 USA

ISBN: (纸本)9781479914814

This paper develops a closed-form solution that improves the projection matrix method for time of arrival (TOA) source localization in the presence of sensor position inaccuracy. The projection approach is attractive because it does not require the use of an extra variable as in the traditional closed-form solution. Compared with the previous projection method, the proposed algorithm offers a closed-form solution and at the same time attains the Cramer-Rao lower bound (CRLB) performance for Gaussian noise over the small noise region. The performance of the proposed method is validated by simulations.

关键词： Time of arrival

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Optimal Virtual array Length Under Position Imperfections

Optimal Virtual Array Length Under Position Imperfections

引用

8th ieee sensor array and multichannel signal processing workshop (SAM)

作者： Mannesson, Anders Bernhardsson, Bo Yaqoob, Muhammad Atif Tufvesson, Fredrik Lund Univ Dept Automat Control S-22100 Lund Sweden Lund Univ Dept Elect & Informat Technol S-22100 Lund Sweden

ISBN: (纸本)9781479914814

This article contains a study of how spatial errors and receiver imperfections affect the angle of arrival estimation accuracy for virtual antenna arrays. A virtual antenna array consists of one receiver element whose location is tracked as the element is moved and in this work, linear arrays are studied. If the location of the receiver is tracked using an inertial measurement unit, an interesting trade-off emerges. The array should extend as far as possible but since the position estimates from the unaided inertial measurement unit become increasingly uncertain over time, the angle of arrival estimation will deteriorate. Several algorithms are available for estimating the angle of arrival in such a scenario but the one used for evaluation here is a sparse enforcing least squares method.

关键词： Angle of arrival Antenna arrays Measurement units Receivers INERTIAL MEASUREMENT UNITS Virtual Elements

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Direction of Arrival Estimation of Wideband signals using Sub-Nyquist Samples

Direction of Arrival Estimation of Wideband Signals using Su...

引用

8th ieee sensor array and multichannel signal processing workshop (SAM)

作者： Chaturvedi, Amal Fan, H. Howard Univ Cincinnati Dept Elect Engn & Comp Syst Cincinnati OH 45220 USA

ISBN: (纸本)9781479914814

This work is dedicated towards finding the Direction of Arrival (DOA) of Wideband signals using Sub-Nyquist samples. In a sensor array, the time delay between two sensors depends on the position of sources and thus its estimate could be used in finding the DOA of the sources. In this paper, we propose to estimate the time delay among sensors using cross-correlation of the signals received at the sensor array. The wider the bandwidth of the signal, the sharper will be the peak of its autocorrelation. Thus, it would be safe to assume that the autocorrelation of a wideband signal is sparse. The number of sharp peaks in the autocorrelation will be the same as the number of uncorrelated sources. The strength of Compressive Sampling (CS) lies in solving an underdetermined problem with a very high probability, provided the solution is sparse. This fits our wideband DOA problem very well, and is used to estimate the autocorrelation and hence the DOA of the signals with sub-Nyquist sampling. Simulations are given to support this concept.

关键词： broadband signal Direction of arrival estimation Autocorrelation signals United States Department of Agriculture time delay sensors broadband sensor arrays Cross correlation

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