Circular array design based on Bayesian Cramer–Rao bound

In this paper, we consider circular array design in the presence of a far-field or a near-field signal source. The location of the source is introduced to our optimization problem by its probability density function (PDF) as a priori information. We consider Bayesian Cramer–Rao bound as the cost fun...

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Bibliographic Details
Published inMultidimensional systems and signal processing Vol. 31; no. 1; pp. 317 - 328
Main Authors Behmandpoor, Pourya, Haddadi, Farzan
Format Journal Article
LanguageEnglish
Published New York Springer US 01.01.2020
Springer Nature B.V
Subjects
Arrays
Artificial Intelligence
Bayesian analysis
Circuits and Systems
Circularity
Cost function
Electrical Engineering
Engineering
Optimization
Probability density functions
Signal,Image and Speech Processing
Location estimation
Array design
Bayesian CRB
Circular array
Optimization
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Summary:In this paper, we consider circular array design in the presence of a far-field or a near-field signal source. The location of the source is introduced to our optimization problem by its probability density function (PDF) as a priori information. We consider Bayesian Cramer–Rao bound as the cost function to be minimized to specify the best locations of the sensors on a circular boundary. In the far-field case, a closed form solution is derived for an arbitrary PDF of the bearing. In the near-field scenario, we divide the design problem into three categories: known source bearing, known source range, and the general case. We present some examples to exhibit the process of array design by the proposed method. Finally, we show the array optimized by the proposed method outperforms arrays with other configurations in source localization.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:0923-6082
1573-0824
DOI:10.1007/s11045-019-00668-1