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Angular information resolution limit of sensor arrays

Angular information resolution limit of sensor arrays

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sensor array and multichannel signal processing workshop

作者： Yongqiang Cheng Xuezhi Wang Bill Moran School of Electronic Science and Engineering National University of Defense Technology China Department of Electrical and Electronic Engineering University of Melbourne Australia Defense Science Institute University of Melbourne Australia

A measure of the ability of a sensor array to resolve two closely spaced point sources in angle is proposed here based on the framework of information geometry. The consideration of the geometric structure of a measurement model leads to the concept of information resolution which serves as a new metric to measure intrinsic similarities and differences between signal distributions that generate the manifold geometry. The statistical divergence between two sources is characterized in terms of the geodesic distance induced by the Fisher information metric. An analytical expression of the angular information resolution limit (AIRL) is derived using the constraints on the probability of error for a binary hypothesis test associated with the resolution of two sources. The influence of the detection error as well as the signal-to-noise ratio (SNR) on resolvability are demonstrated. The proposed AIRL can be used as a performance measure for sensor arrays in localizing remote sources and is applicable to various applications (e.g. radar, sonar, or astronomy).

关键词： sensors

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Mismatch loss constrained instrumental variable filtering for GSM passive bistatic radar

Mismatch loss constrained instrumental variable filtering fo...

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sensor array and multichannel signal processing workshop

作者： Florian Bahlke Reda Zemmari Ulrich Nickel Marius Pesavento Sensor Data and Information Fusion Dept. Fraunhofer FKIE Wachtberg Germany Communication Systems Group University of Science Darmstadt Darmstadt Germany

In this paper we address the design and evaluation of a mismatched filter for application in GSM passive bistatic radar (PBR). We discuss various exisiting approaches for the filter design, which originate from active radar systems and have to be adapted for PBR. Our proposed method minimizes the L p -norm of the sidelobe level energies under an upper-bound constraint on the SNR loss relative to the matched filter. We show the suitibility of this approach for GSM based PBR and give a performance evaluation. Based on simulated and measured data, we show that the proposed filter can improve range resultion and target detection of the PBR system.

关键词： GSM Vectors signal to noise ratio Passive radar Conferences Bistatic radar

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Distributed detection of correlated random processes under energy and bandwidth constraints

Distributed detection of correlated random processes under e...

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sensor array and multichannel signal processing workshop

作者： Juan Augusto Maya Leonardo Rey Vega Cecilia G. Galarza Andrés Altieri University of Buenos Aires CSC-CONICET Buenos Aires Argentina

We analyze a binary hypothesis testing problem built on a wireless sensor network (WSN). Using Large Deviation Theory (LDT), we compute the probability error exponents of a distributed scheme for detecting a correlated circularly-symmetric complex Gaussian process under the Neyman-Pearson framework. Using an analog scheme, the sensors transmit scaled versions of their measurements several times through a multiple access channel (MAC) to reach the fusion center (FC), whose task is to decide whether the process is present or not. In the analysis, we consider the energy constraint on each node transmission. We show that the proposed distributed scheme requires relatively few MAC channel uses to achieve the centralized error exponents when detecting correlated Gaussian processes.

关键词： Detectors Bandwidth Wireless sensor networks Covariance matrices Gaussian processes Vectors Communication channels

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On the uniform asymptotic convergence of a distributed particle filter

On the uniform asymptotic convergence of a distributed parti...

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sensor array and multichannel signal processing workshop

作者： Joaquín Míguez Department of Signal Theory & Communications Universidad Carlos III de Madrid. Leganés Madrid Spain

Distributed signal processing algorithms suitable for their implementation over wireless sensor networks (WSNs) and ad hoc networks with communications and computing capabilities have become a hot topic during the past years. One class of algorithms that have received special attention are particles filters. However, most distributed versions of this type of methods involve various heuristic or simplifying approximations and, as a consequence, classical convergence theorems for standard particle filters do not hold for their distributed counterparts. In this paper, we look into a distributed particle filter scheme that has been proposed for implementation in both parallel computing systems and WSNs, and prove that, under certain stability assumptions regarding the physical system of interest, its asymptotic convergence is guaranteed. Moreover, we show that convergence is attained uniformly over time. This means that approximation errors can be kept bounded for an arbitrarily long period of time without having to progressively increase the computational effort.

关键词： Convergence signal processing algorithms Approximation methods Wireless sensor networks signal processing Approximation algorithms Monte Carlo methods

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Envelope only TDOA estimation for sensor network self calibration

Envelope only TDOA estimation for sensor network self calibr...

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sensor array and multichannel signal processing workshop

作者： Dan Ohev Zion Hagit Messer School of Electrical Engineering Tel Aviv University

Self localization of nodes in wireless sensor networks (WSN) attracts wide interest in the past few years. The main challenge is to adopt calibration algorithms to the low-power/ low-bandwidth/ low-cost constrains on each sensor in the network. In this paper we present a novel approach, in which position-known anchors transmit special calibration signals simultaneously. Each node measures only the received signal strength (RSSI), i.e. envelope of the sum of received signals, and estimates time difference of arrivals (TDOA) of the signals. Two or more TDOA measurements allow the node to estimate its own position in the WSN. Based on an analysis of the appropriate Cramer Rao Lower Bound (CRLB) on the TDOA estimates, we suggest the choice of calibrating signals which provide optimal performance. The theoretical results are supported by simulations of the Maximum Likelihood (ML) estimators and numerical evaluation of the CRLB for a specific scenario of interest.

关键词： Conferences Wireless sensor networks Position measurement

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Tracking simplified shapes using a stochastic boundary

Tracking simplified shapes using a stochastic boundary

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sensor array and multichannel signal processing workshop

作者： Antonio Zea Florian Faion Marcus Baum Uwe D. Hanebeck Intelligent Sensor-Actuator-Systems Laboratory (ISAS) Karlsruhe Institute of Technology (KIT) Germany University of Connecticut USA

When tracking extended objects, it is often the case that the shape of the target cannot be fully observed due to issues of visibility, artifacts, or high noise, which can change with time. In these situations, it is a common approach to model targets as simpler shapes instead, such as ellipsoids or cylinders. However, these simplifications cause information loss from the original shape, which could be used to improve the estimation results. In this paper, we propose a way to recover information from these lost details in the form of a stochastic boundary, whose parameters can be dynamically estimated from received measurements. The benefits of this approach are evaluated by tracking an object using noisy, real-life RGBD data.

关键词： Robot sensing systems Shape Noise measurement Fitting Q measurement

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Direction of arrival estimation using nested vector-sensor arrays via tensor modeling

Direction of arrival estimation using nested vector-sensor a...

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sensor array and multichannel signal processing workshop

作者： Keyong Han Arye Nehorai Preston M. Green Department of Electrical and Systems Engineering Washington University in St. Louis MO USA

We propose a new class of nested vector-sensor arrays, via tensor modeling, which is capable of increasing the number of degrees of freedom (DOFs) for direction-of-arrival (DOA) estimation, compared with the uniform linear array (ULA). By using one component's information of the interspectral tensor, the proposed nested vector-sensor array can provide O(N 2 ) DOFs with only N physical sensors. To utilize the increased DOFs, a novel spatial smoothing approach is proposed, which preserves the data structure and avoids reorganization via tensor modeling. Further, we propose corresponding direction of arrival (DOA) estimation strategies. Based on the analytical results, we consider two main applications: electromagnetic (EM) vector sensors and acoustic vector sensors. We show that the DOA estimation performance is greatly improved due to the combination of the nested-array strategy and tensor modeling. The effectiveness of the proposed methods is verified through numerical examples.

关键词： arrays Vectors Tensile stress Estimation Direction-of-arrival estimation Acoustics Electromagnetics

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A GNSS structural interference mitigation technique using antenna array processing

A GNSS structural interference mitigation technique using an...

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sensor array and multichannel signal processing workshop

作者： Saeed Daneshmand Ali Jafarnia-Jahromi Ali Broumandan Gérard Lachapelle Position Location and Navigation (PLAN) Group University of Calgary Calgary Alberta Canada

Position solutions provided by Global Navigation Satellite Systems (GNSS) can be completely misled by structural interference or spoofing threats. An approach utilizing an antenna array is proposed in order to suppress spoofing attacks. The proposed method is based on the assumption that all spoofing signals are transmitted from a single point source. A spatial domain processing technique is proposed to extract the spoofing signal steering vector and consequently to discard the spoofing signals. This method is implemented before despreading and acquisition stage of a GNSS receiver. Hence, it does not impose a heavy computational load on the receiver operational process since it does not require any extensive search in the code and Doppler domains to separately despread individual authentic and spoofing signals. Moreover, the proposed method does not require any antenna array calibration process. This pre-despreading interference mitigation technique is further extended to maximize signal-to-noise ratio (SNR) of each individual authentic GNSS signal. Simulation results show that the proposed method effectively countermeasures spoofing attacks for a wide range of received spoofing power.

关键词： Global Positioning System arrays signal to noise ratio Receivers Vectors Antenna arrays Correlation

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Real-time kernel-based multiple target tracking for robotic beating heart surgery

Real-time kernel-based multiple target tracking for robotic ...

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sensor array and multichannel signal processing workshop

作者： Gerhard Kurz Marcus Baum Uwe D. Hanebeck Intelligent Sensor-Actuator-Systems Laboratory (ISAS) Karlsruhe Institute of Technology (KIT) Germany University of Connecticut USA

Performing surgery on the beating heart has significant advantages for the patient compared to traditional heart surgery on the stopped heart. A remote-controlled robot can be used to automatically cancel out the movement of the beating heart. This necessitates precise tracking of the heart surface. For this purpose, we track 24 identical artificial markers placed on the heart. This creates a data association problem, because it is not known which measurement was obtained from which marker. To solve this problem, we apply a multiple target tracking method based on a symmetric kernel transformation. This method allows efficient handling of the data association problem even for a reasonably large number of targets. We demonstrate how to implement this method efficiently. The proposed approach is evaluated on in-vivo data of a real beating heart surgery performed on a porcine beating heart.

关键词： Heart Target tracking Vectors Surgery Covariance matrices Mathematical model signal processing algorithms

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Joint beamforming and transmit design for the non-regenerative MIMO broadcast relay channel

Joint beamforming and transmit design for the non-regenerati...

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sensor array and multichannel signal processing workshop

作者： Jens Steinwandt Sergiy A. Vorobyov Martin Haardt Communications Research Laboratory Ilmenau University of Technology Ilmenau Germany Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada

In this paper, we consider a multiple-input multiple-output (MIMO) broadcast relay channel (BRC), in which the communication of a multi-antenna base station (BS) with several multi-antenna mobile stations (MS) is assisted by a fixed half-duplex multi-antenna relay station (RS). Applying dirty paper coding (DPC) at the BS and beamforming at the RS, we jointly optimize the transmit covariance matrices at the BS and the beamforming matrix at the RS by maximizing the system sum rate, which is a nonconvex problem. To solve this problem, we resort to the more tractable sum rate maximization in the dual multiple access relay channel (MARC), which is still a nonconvex difference of convex functions (DC) programming problem. We develop an iterative algorithm, termed alternating matrix polynomial time DC (POTDC) algorithm, based on an alternating optimization of the beamforming matrix and the transmit covariance matrices. The resulting covariance matrices for the MARC are then mapped to the desired BRC covariance matrices. The sum rate performance of the proposed algorithm is demonstrated by simulations.

关键词： Covariance matrices MIMO Relays array signal processing signal processing algorithms Optimization

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