Nested Vector-Sensor Array Processing via Tensor Modeling

We propose a new class of nested vector-sensor arrays which is capable of significantly increasing the degrees of freedom (DOF). This is not a simple extension of the nested scalar-sensor array, but a novel signal model. The structure is obtained by systematically nesting two or more uniform linear...

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Vydáno v:IEEE transactions on signal processing Ročník 62; číslo 10; s. 2542 - 2553
Hlavní autoři: Keyong Han, Nehorai, Arye
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York, NY IEEE 15.05.2014
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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ISSN:1053-587X, 1941-0476
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Abstract We propose a new class of nested vector-sensor arrays which is capable of significantly increasing the degrees of freedom (DOF). This is not a simple extension of the nested scalar-sensor array, but a novel signal model. The structure is obtained by systematically nesting two or more uniform linear arrays with vector sensors. By using one component's information of the interspectral tensor, which is equivalent to the higher-dimensional second-order statistics of the received data, the proposed nested vector-sensor array can provide O(N 2 ) DOF with only N physical sensors. To utilize the increased DOF, a novel spatial smoothing approach is proposed, which needs multilinear algebra in order to preserve the data structure and avoid reorganization. Thus, the data is stored in a higher-order tensor. Both the signal model of the nested vector-sensor array and the signal processing strategies, which include spatial smoothing, source number detection, and direction of arrival (DOA) estimation, are developed in the multidimensional sense. Based on the analytical results, we consider two main applications: electromagnetic (EM) vector sensors and acoustic vector sensors. The effectiveness of the proposed methods is verified through numerical examples.
AbstractList We propose a new class of nested vector-sensor arrays which is capable of significantly increasing the degrees of freedom (DOF). This is not a simple extension of the nested scalar-sensor array, but a novel signal model. The structure is obtained by systematically nesting two or more uniform linear arrays with vector sensors. By using one component's information of the interspectral tensor, which is equivalent to the higher-dimensional second-order statistics of the received data, the proposed nested vector-sensor array can provide [Formula Omitted] DOF with only [Formula Omitted] physical sensors. To utilize the increased DOF, a novel spatial smoothing approach is proposed, which needs multilinear algebra in order to preserve the data structure and avoid reorganization. Thus, the data is stored in a higher-order tensor. Both the signal model of the nested vector-sensor array and the signal processing strategies, which include spatial smoothing, source number detection, and direction of arrival (DOA) estimation, are developed in the multidimensional sense. Based on the analytical results, we consider two main applications: electromagnetic (EM) vector sensors and acoustic vector sensors. The effectiveness of the proposed methods is verified through numerical examples.
We propose a new class of nested vector-sensor arrays which is capable of significantly increasing the degrees of freedom (DOF). This is not a simple extension of the nested scalar-sensor array, but a novel signal model. The structure is obtained by systematically nesting two or more uniform linear arrays with vector sensors. By using one component's information of the interspectral tensor, which is equivalent to the higher-dimensional second-order statistics of the received data, the proposed nested vector-sensor array can provide O openbracket N 2 [ closebracket ] DOF with only N physical sensors. To utilize the increased DOF, a novel spatial smoothing approach is proposed, which needs multilinear algebra in order to preserve the data structure and avoid reorganization. Thus, the data is stored in a higher-order tensor. Both the signal model of the nested vector-sensor array and the signal processing strategies, which include spatial smoothing, source number detection, and direction of arrival (DOA) estimation, are developed in the multidimensional sense. Based on the analytical results, we consider two main applications: electromagnetic (EM) vector sensors and acoustic vector sensors. The effectiveness of the proposed methods is verified through numerical examples.
We propose a new class of nested vector-sensor arrays which is capable of significantly increasing the degrees of freedom (DOF). This is not a simple extension of the nested scalar-sensor array, but a novel signal model. The structure is obtained by systematically nesting two or more uniform linear arrays with vector sensors. By using one component's information of the interspectral tensor, which is equivalent to the higher-dimensional second-order statistics of the received data, the proposed nested vector-sensor array can provide O(N 2 ) DOF with only N physical sensors. To utilize the increased DOF, a novel spatial smoothing approach is proposed, which needs multilinear algebra in order to preserve the data structure and avoid reorganization. Thus, the data is stored in a higher-order tensor. Both the signal model of the nested vector-sensor array and the signal processing strategies, which include spatial smoothing, source number detection, and direction of arrival (DOA) estimation, are developed in the multidimensional sense. Based on the analytical results, we consider two main applications: electromagnetic (EM) vector sensors and acoustic vector sensors. The effectiveness of the proposed methods is verified through numerical examples.
Author Keyong Han
Nehorai, Arye
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  givenname: Arye
  surname: Nehorai
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Keywords Performance evaluation
Second order
Direction-of-arrival estimation
Parameter estimation
Array signal processing
Signal estimation
multilinear algebra
Modeling
Higher order statistic
nested array
direction of arrival estimation
Linear antenna arrays
Data structure
Signal detection
Electromagnetism
Acoustic measurement
Measurement sensor
Order statistic
electromagnetic vector sensors
source number detection
tensor
Cross spectral density
Statistical method
Analytical method
Signal processing
Numerical simulation
Acoustic vector sensors
Acoustic sensor
Sensor array
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Snippet We propose a new class of nested vector-sensor arrays which is capable of significantly increasing the degrees of freedom (DOF). This is not a simple extension...
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SubjectTerms Acoustic vector sensors
Applied sciences
Arrays
Detection, estimation, filtering, equalization, prediction
Direction-of-arrival estimation
electromagnetic vector sensors
Estimation
Exact sciences and technology
Information, signal and communications theory
Mathematical analysis
Miscellaneous
multilinear algebra
nested array
Preserves
Sensor arrays
Sensors
Signal and communications theory
Signal processing
Signal, noise
source number detection
Spatial smoothing
Telecommunications and information theory
Tensile stress
tensor
Tensors
Vectors
Vectors (mathematics)
Title Nested Vector-Sensor Array Processing via Tensor Modeling
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