Sparse linear nested array for active sensing

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussavertaisarvioitu

Standard

Sparse linear nested array for active sensing. / Rajamäki, Robin; Koivunen, Visa.

25th European Signal Processing Conference (EUSIPCO 2017). IEEE, 2017. s. 1976-1980.

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussavertaisarvioitu

Harvard

Rajamäki, R & Koivunen, V 2017, Sparse linear nested array for active sensing. julkaisussa 25th European Signal Processing Conference (EUSIPCO 2017). IEEE, Sivut 1976-1980, Kos, Kreikka, 28/08/2017. https://doi.org/10.23919/EUSIPCO.2017.8081555

APA

Rajamäki, R., & Koivunen, V. (2017). Sparse linear nested array for active sensing. teoksessa 25th European Signal Processing Conference (EUSIPCO 2017) (Sivut 1976-1980). IEEE. https://doi.org/10.23919/EUSIPCO.2017.8081555

Vancouver

Rajamäki R, Koivunen V. Sparse linear nested array for active sensing. julkaisussa 25th European Signal Processing Conference (EUSIPCO 2017). IEEE. 2017. s. 1976-1980 https://doi.org/10.23919/EUSIPCO.2017.8081555

Author

Rajamäki, Robin ; Koivunen, Visa. / Sparse linear nested array for active sensing. 25th European Signal Processing Conference (EUSIPCO 2017). IEEE, 2017. Sivut 1976-1980

Bibtex - Lataa

@inproceedings{73fb0c82662c4d6d80e6d638d4f7a1e4,
title = "Sparse linear nested array for active sensing",
abstract = "Sparse sensor arrays can match the performance of fully populated arrays using substantially fewer elements. However, finding the array configuration with the smallest number of elements is generally a computationally difficult problem. Consequently, simple to generate array configurations that may be suboptimal are of high practical interest. This paper presents a novel closed-form sparse linear array configuration designed for active sensing, called the Concatenated Nested Array (CNA). The key parameters of the CNA are derived. The CNA is also compared to the optimal Minimum-Redundancy Array (MRA) in numerical simulations. The CNA is shown to require only about 10{\%} more elements than the MRA in the limit of large apertures.",
author = "Robin Rajam{\"a}ki and Visa Koivunen",
year = "2017",
doi = "10.23919/EUSIPCO.2017.8081555",
language = "English",
pages = "1976--1980",
booktitle = "25th European Signal Processing Conference (EUSIPCO 2017)",
publisher = "IEEE",

}

RIS - Lataa

TY - GEN

T1 - Sparse linear nested array for active sensing

AU - Rajamäki, Robin

AU - Koivunen, Visa

PY - 2017

Y1 - 2017

N2 - Sparse sensor arrays can match the performance of fully populated arrays using substantially fewer elements. However, finding the array configuration with the smallest number of elements is generally a computationally difficult problem. Consequently, simple to generate array configurations that may be suboptimal are of high practical interest. This paper presents a novel closed-form sparse linear array configuration designed for active sensing, called the Concatenated Nested Array (CNA). The key parameters of the CNA are derived. The CNA is also compared to the optimal Minimum-Redundancy Array (MRA) in numerical simulations. The CNA is shown to require only about 10% more elements than the MRA in the limit of large apertures.

AB - Sparse sensor arrays can match the performance of fully populated arrays using substantially fewer elements. However, finding the array configuration with the smallest number of elements is generally a computationally difficult problem. Consequently, simple to generate array configurations that may be suboptimal are of high practical interest. This paper presents a novel closed-form sparse linear array configuration designed for active sensing, called the Concatenated Nested Array (CNA). The key parameters of the CNA are derived. The CNA is also compared to the optimal Minimum-Redundancy Array (MRA) in numerical simulations. The CNA is shown to require only about 10% more elements than the MRA in the limit of large apertures.

U2 - 10.23919/EUSIPCO.2017.8081555

DO - 10.23919/EUSIPCO.2017.8081555

M3 - Conference contribution

SP - 1976

EP - 1980

BT - 25th European Signal Processing Conference (EUSIPCO 2017)

PB - IEEE

ER -

ID: 16403572