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Sensor Array Processing

Subspace-Based Imaging Using Only Power Measurements


In this paper, we are interested in the high-resolution
imaging of an unknown area based on only power measurements
of a small number of wireless transceivers located on one
side of the unknown area. In order to do so, we propose a
framework that achieves a polynomial order reduction in the
number of antennas required for high-resolution imaging. More
specifically, we show that by spacing the antennas at multiples
of the wavelength and applying subspace-based analysis, we can
image M targets using only 2M+1 transmit/receive antennas (as

Paper Details

Authors:
Saandeep Depatla, Yasamin Mostofi
Submitted On:
9 July 2018 - 5:24pm
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KoranyDepatlaMostofi_SAM18_poster.pdf

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[1] Saandeep Depatla, Yasamin Mostofi, "Subspace-Based Imaging Using Only Power Measurements", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3376. Accessed: Jul. 17, 2018.
@article{3376-18,
url = {http://sigport.org/3376},
author = {Saandeep Depatla; Yasamin Mostofi },
publisher = {IEEE SigPort},
title = {Subspace-Based Imaging Using Only Power Measurements},
year = {2018} }
TY - EJOUR
T1 - Subspace-Based Imaging Using Only Power Measurements
AU - Saandeep Depatla; Yasamin Mostofi
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3376
ER -
Saandeep Depatla, Yasamin Mostofi. (2018). Subspace-Based Imaging Using Only Power Measurements. IEEE SigPort. http://sigport.org/3376
Saandeep Depatla, Yasamin Mostofi, 2018. Subspace-Based Imaging Using Only Power Measurements. Available at: http://sigport.org/3376.
Saandeep Depatla, Yasamin Mostofi. (2018). "Subspace-Based Imaging Using Only Power Measurements." Web.
1. Saandeep Depatla, Yasamin Mostofi. Subspace-Based Imaging Using Only Power Measurements [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3376

Symmetric Sparse Linear Array for Active Imaging


Sparse sensor arrays can achieve significantly more degrees of freedom than the number of elements by leveraging the co-array, a virtual structure that arises from the far field narrowband signal model. Although several sparse array configurations have been developed for passive sensing tasks, less attention has been paid to arrays suitable for active sensing. This paper presents a novel active sparse linear array, called the Interleaved Wichmann Array (IWA). The IWA only has a few closely spaced elements, which may make it more robust to mutual coupling effects.

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Authors:
Visa Koivunen
Submitted On:
8 July 2018 - 5:20am
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[1] Visa Koivunen, "Symmetric Sparse Linear Array for Active Imaging", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3371. Accessed: Jul. 17, 2018.
@article{3371-18,
url = {http://sigport.org/3371},
author = {Visa Koivunen },
publisher = {IEEE SigPort},
title = {Symmetric Sparse Linear Array for Active Imaging},
year = {2018} }
TY - EJOUR
T1 - Symmetric Sparse Linear Array for Active Imaging
AU - Visa Koivunen
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3371
ER -
Visa Koivunen. (2018). Symmetric Sparse Linear Array for Active Imaging. IEEE SigPort. http://sigport.org/3371
Visa Koivunen, 2018. Symmetric Sparse Linear Array for Active Imaging. Available at: http://sigport.org/3371.
Visa Koivunen. (2018). "Symmetric Sparse Linear Array for Active Imaging." Web.
1. Visa Koivunen. Symmetric Sparse Linear Array for Active Imaging [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3371

Fast direction of arrival estimation using a sensor-saving coprime array with enlarged inter-element spacing

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Authors:
Jianfeng Li, Mingwei Shen and Defu Jiang
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6 July 2018 - 9:42am
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[1] Jianfeng Li, Mingwei Shen and Defu Jiang, "Fast direction of arrival estimation using a sensor-saving coprime array with enlarged inter-element spacing", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3369. Accessed: Jul. 17, 2018.
@article{3369-18,
url = {http://sigport.org/3369},
author = {Jianfeng Li; Mingwei Shen and Defu Jiang },
publisher = {IEEE SigPort},
title = {Fast direction of arrival estimation using a sensor-saving coprime array with enlarged inter-element spacing},
year = {2018} }
TY - EJOUR
T1 - Fast direction of arrival estimation using a sensor-saving coprime array with enlarged inter-element spacing
AU - Jianfeng Li; Mingwei Shen and Defu Jiang
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3369
ER -
Jianfeng Li, Mingwei Shen and Defu Jiang. (2018). Fast direction of arrival estimation using a sensor-saving coprime array with enlarged inter-element spacing. IEEE SigPort. http://sigport.org/3369
Jianfeng Li, Mingwei Shen and Defu Jiang, 2018. Fast direction of arrival estimation using a sensor-saving coprime array with enlarged inter-element spacing. Available at: http://sigport.org/3369.
Jianfeng Li, Mingwei Shen and Defu Jiang. (2018). "Fast direction of arrival estimation using a sensor-saving coprime array with enlarged inter-element spacing." Web.
1. Jianfeng Li, Mingwei Shen and Defu Jiang. Fast direction of arrival estimation using a sensor-saving coprime array with enlarged inter-element spacing [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3369

EEG-Based Classification of Emotional State Using an Autonomous Vehicle Simulator


Societal acceptance of self-driving cars (SDC) is predicated on a level of trust between humans and the au- tonomous vehicle. Although the performance of SDCs has im- proved dramatically, the question of mainstream acceptance and requisite trust is still open. We are exploring this question through integration of virtual reality SDC simulator and an electroencephalographic (EEG) recorder. In order for a passenger to build and maintain trust, the SDC will need to operate in a manner that elicits positive emotional response and avoids negative emotional response.

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Authors:
Corey Park, Shervin Shahrdar and Mehrdad Nojoumian
Submitted On:
5 July 2018 - 2:58pm
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[1] Corey Park, Shervin Shahrdar and Mehrdad Nojoumian, "EEG-Based Classification of Emotional State Using an Autonomous Vehicle Simulator", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3361. Accessed: Jul. 17, 2018.
@article{3361-18,
url = {http://sigport.org/3361},
author = {Corey Park; Shervin Shahrdar and Mehrdad Nojoumian },
publisher = {IEEE SigPort},
title = {EEG-Based Classification of Emotional State Using an Autonomous Vehicle Simulator},
year = {2018} }
TY - EJOUR
T1 - EEG-Based Classification of Emotional State Using an Autonomous Vehicle Simulator
AU - Corey Park; Shervin Shahrdar and Mehrdad Nojoumian
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3361
ER -
Corey Park, Shervin Shahrdar and Mehrdad Nojoumian. (2018). EEG-Based Classification of Emotional State Using an Autonomous Vehicle Simulator. IEEE SigPort. http://sigport.org/3361
Corey Park, Shervin Shahrdar and Mehrdad Nojoumian, 2018. EEG-Based Classification of Emotional State Using an Autonomous Vehicle Simulator. Available at: http://sigport.org/3361.
Corey Park, Shervin Shahrdar and Mehrdad Nojoumian. (2018). "EEG-Based Classification of Emotional State Using an Autonomous Vehicle Simulator." Web.
1. Corey Park, Shervin Shahrdar and Mehrdad Nojoumian. EEG-Based Classification of Emotional State Using an Autonomous Vehicle Simulator [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3361

Performance analysis of distributed radio interferometric calibration


Distributed calibration based on consensus optimization is a computationally efficient method to calibrate large radio interferometers such as LOFAR and SKA. Calibrating along multiple directions in the sky and removing the bright foreground signal is a crucial step in many science cases in radio interferometry. The residual data contain weak signals of huge scientific interest and of particular concern is the effect of incomplete sky models used in calibration on the residual. In order to study this, we consider the mapping between the input uncalibrated data and the output residual data.

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17 July 2018 - 6:22am
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[1] , "Performance analysis of distributed radio interferometric calibration", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3358. Accessed: Jul. 17, 2018.
@article{3358-18,
url = {http://sigport.org/3358},
author = { },
publisher = {IEEE SigPort},
title = {Performance analysis of distributed radio interferometric calibration},
year = {2018} }
TY - EJOUR
T1 - Performance analysis of distributed radio interferometric calibration
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3358
ER -
. (2018). Performance analysis of distributed radio interferometric calibration. IEEE SigPort. http://sigport.org/3358
, 2018. Performance analysis of distributed radio interferometric calibration. Available at: http://sigport.org/3358.
. (2018). "Performance analysis of distributed radio interferometric calibration." Web.
1. . Performance analysis of distributed radio interferometric calibration [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3358

AN IMPROVED DOA ESTIMATOR BASED ON PARTIAL RELAXATION APPROACH


In the partial relaxation approach, at each desired direction, the manifold structure of the remaining interfering signals impinging on the sensor array is relaxed, which results in closed form estimates for the interference parameters. By adopting this approach, in this paper, a new estimator based on the unconstrained covariance fitting problem is proposed. To obtain the null-spectra efficiently, an iterative rooting scheme based on the rational function approximation is applied.

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Authors:
Mats Viberg, Marius Pesavento
Submitted On:
22 April 2018 - 11:14am
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slides - ICASSP2018.pdf

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[1] Mats Viberg, Marius Pesavento, "AN IMPROVED DOA ESTIMATOR BASED ON PARTIAL RELAXATION APPROACH", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3133. Accessed: Jul. 17, 2018.
@article{3133-18,
url = {http://sigport.org/3133},
author = {Mats Viberg; Marius Pesavento },
publisher = {IEEE SigPort},
title = {AN IMPROVED DOA ESTIMATOR BASED ON PARTIAL RELAXATION APPROACH},
year = {2018} }
TY - EJOUR
T1 - AN IMPROVED DOA ESTIMATOR BASED ON PARTIAL RELAXATION APPROACH
AU - Mats Viberg; Marius Pesavento
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3133
ER -
Mats Viberg, Marius Pesavento. (2018). AN IMPROVED DOA ESTIMATOR BASED ON PARTIAL RELAXATION APPROACH. IEEE SigPort. http://sigport.org/3133
Mats Viberg, Marius Pesavento, 2018. AN IMPROVED DOA ESTIMATOR BASED ON PARTIAL RELAXATION APPROACH. Available at: http://sigport.org/3133.
Mats Viberg, Marius Pesavento. (2018). "AN IMPROVED DOA ESTIMATOR BASED ON PARTIAL RELAXATION APPROACH." Web.
1. Mats Viberg, Marius Pesavento. AN IMPROVED DOA ESTIMATOR BASED ON PARTIAL RELAXATION APPROACH [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3133

Semidefinite Programming for TDOA Localization with Locally Synchronized Anchor Nodes


The most state-of-art time-difference-of-arrival (TDOA) localization algorithms are performed under the assumption that all the nodes are synchronized. However, for a widely distributed wireless sensor networks (WSNs), time synchronization between all the nodes is not a trival problem. In this paper, we study the problem of source localization using signal TDOA measurements in the system of nodes part synchronization. Starting from the maximum likelihood estimator (MLE), we develop a semidefinite programming (SDP) approach.

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Authors:
Yanbin Zou, Qun Wan, and Huaping Liu,
Submitted On:
20 April 2018 - 12:20am
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Yanbin Zou Poster for ICASSP 2018

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[1] Yanbin Zou, Qun Wan, and Huaping Liu, , "Semidefinite Programming for TDOA Localization with Locally Synchronized Anchor Nodes", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3066. Accessed: Jul. 17, 2018.
@article{3066-18,
url = {http://sigport.org/3066},
author = {Yanbin Zou; Qun Wan; and Huaping Liu; },
publisher = {IEEE SigPort},
title = {Semidefinite Programming for TDOA Localization with Locally Synchronized Anchor Nodes},
year = {2018} }
TY - EJOUR
T1 - Semidefinite Programming for TDOA Localization with Locally Synchronized Anchor Nodes
AU - Yanbin Zou; Qun Wan; and Huaping Liu;
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3066
ER -
Yanbin Zou, Qun Wan, and Huaping Liu, . (2018). Semidefinite Programming for TDOA Localization with Locally Synchronized Anchor Nodes. IEEE SigPort. http://sigport.org/3066
Yanbin Zou, Qun Wan, and Huaping Liu, , 2018. Semidefinite Programming for TDOA Localization with Locally Synchronized Anchor Nodes. Available at: http://sigport.org/3066.
Yanbin Zou, Qun Wan, and Huaping Liu, . (2018). "Semidefinite Programming for TDOA Localization with Locally Synchronized Anchor Nodes." Web.
1. Yanbin Zou, Qun Wan, and Huaping Liu, . Semidefinite Programming for TDOA Localization with Locally Synchronized Anchor Nodes [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3066

Joint Estimation of the Room Geometry and Modes with Compressed Sensing


Acoustical behavior of a room for a given position of microphone and sound source is usually described using the room impulse response. If we rely on the standard uniform sampling, the estimation of room impulse response for arbitrary positions in the room requires a large number of measurements. In order to lower the required sampling rate, some solutions have emerged that exploit the sparse representation of the room wavefield in the terms of plane waves in the low-frequency domain. The plane wave representation has a simple form in rectangular rooms.

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13 April 2018 - 10:07am
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[1] , "Joint Estimation of the Room Geometry and Modes with Compressed Sensing", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2708. Accessed: Jul. 17, 2018.
@article{2708-18,
url = {http://sigport.org/2708},
author = { },
publisher = {IEEE SigPort},
title = {Joint Estimation of the Room Geometry and Modes with Compressed Sensing},
year = {2018} }
TY - EJOUR
T1 - Joint Estimation of the Room Geometry and Modes with Compressed Sensing
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2708
ER -
. (2018). Joint Estimation of the Room Geometry and Modes with Compressed Sensing. IEEE SigPort. http://sigport.org/2708
, 2018. Joint Estimation of the Room Geometry and Modes with Compressed Sensing. Available at: http://sigport.org/2708.
. (2018). "Joint Estimation of the Room Geometry and Modes with Compressed Sensing." Web.
1. . Joint Estimation of the Room Geometry and Modes with Compressed Sensing [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2708

SOURCE AND DIRECTION OF ARRIVAL ESTIMATION BASED ON MAXIMUM LIKELIHOOD COMBINED WITH GMM AND EIGENANALYSIS


A method is proposed for estimating the source signal and its direction of arrival (DOA) in this paper. It is based on ML estimation of the transfer function between microphones combined with the EM algorithm for a Gaussian Mixture Model (GMM), assuming that the signal is captured at each microphone with delay corresponding to the traveling of sound and some decay. By this modeling, search for the maximum log-likelihood in the ML estimation can be realized simply by eigenvalue decomposition of a properly designed matrix.

ICASSP2018_B0.pdf

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Authors:
Yoiti Suzuki
Submitted On:
12 April 2018 - 8:26pm
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[1] Yoiti Suzuki, "SOURCE AND DIRECTION OF ARRIVAL ESTIMATION BASED ON MAXIMUM LIKELIHOOD COMBINED WITH GMM AND EIGENANALYSIS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2529. Accessed: Jul. 17, 2018.
@article{2529-18,
url = {http://sigport.org/2529},
author = {Yoiti Suzuki },
publisher = {IEEE SigPort},
title = {SOURCE AND DIRECTION OF ARRIVAL ESTIMATION BASED ON MAXIMUM LIKELIHOOD COMBINED WITH GMM AND EIGENANALYSIS},
year = {2018} }
TY - EJOUR
T1 - SOURCE AND DIRECTION OF ARRIVAL ESTIMATION BASED ON MAXIMUM LIKELIHOOD COMBINED WITH GMM AND EIGENANALYSIS
AU - Yoiti Suzuki
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2529
ER -
Yoiti Suzuki. (2018). SOURCE AND DIRECTION OF ARRIVAL ESTIMATION BASED ON MAXIMUM LIKELIHOOD COMBINED WITH GMM AND EIGENANALYSIS. IEEE SigPort. http://sigport.org/2529
Yoiti Suzuki, 2018. SOURCE AND DIRECTION OF ARRIVAL ESTIMATION BASED ON MAXIMUM LIKELIHOOD COMBINED WITH GMM AND EIGENANALYSIS. Available at: http://sigport.org/2529.
Yoiti Suzuki. (2018). "SOURCE AND DIRECTION OF ARRIVAL ESTIMATION BASED ON MAXIMUM LIKELIHOOD COMBINED WITH GMM AND EIGENANALYSIS." Web.
1. Yoiti Suzuki. SOURCE AND DIRECTION OF ARRIVAL ESTIMATION BASED ON MAXIMUM LIKELIHOOD COMBINED WITH GMM AND EIGENANALYSIS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2529

Cooperative Tracking using Marginal Diffusion Particle Filters


This paper formulates the general Adapt-then-Combine (ATC) and Random Exchange (RndEx) diffusion filters for an arbitrary nonlinear state-space model. Subsequently, we propose two novel marginal Particle Filter implementations of the general ATC and RndEx filters using respectively a pure Sequential Monte Carlo (SMC) strategy and a hybrid Gaussian/SMC methodology. The proposed algorithms are assessed via simulation in a numerical example of cooperative target tracking with received-signal-strength (RSS) sensors.

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Authors:
Marcelo G. S. Bruno, Stiven S. Dias
Submitted On:
12 April 2018 - 7:15pm
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[1] Marcelo G. S. Bruno, Stiven S. Dias, "Cooperative Tracking using Marginal Diffusion Particle Filters", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2517. Accessed: Jul. 17, 2018.
@article{2517-18,
url = {http://sigport.org/2517},
author = {Marcelo G. S. Bruno; Stiven S. Dias },
publisher = {IEEE SigPort},
title = {Cooperative Tracking using Marginal Diffusion Particle Filters},
year = {2018} }
TY - EJOUR
T1 - Cooperative Tracking using Marginal Diffusion Particle Filters
AU - Marcelo G. S. Bruno; Stiven S. Dias
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2517
ER -
Marcelo G. S. Bruno, Stiven S. Dias. (2018). Cooperative Tracking using Marginal Diffusion Particle Filters. IEEE SigPort. http://sigport.org/2517
Marcelo G. S. Bruno, Stiven S. Dias, 2018. Cooperative Tracking using Marginal Diffusion Particle Filters. Available at: http://sigport.org/2517.
Marcelo G. S. Bruno, Stiven S. Dias. (2018). "Cooperative Tracking using Marginal Diffusion Particle Filters." Web.
1. Marcelo G. S. Bruno, Stiven S. Dias. Cooperative Tracking using Marginal Diffusion Particle Filters [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2517

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