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A digital direction of arrival estimator based on fast on-line sequential random vector functional link network.

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Title: A digital direction of arrival estimator based on fast on-line sequential random vector functional link network.
Authors: Raiguru, Priyadarshini1,2 (AUTHOR), Sahani, Mrutyunjaya1 (AUTHOR), Rout, Susanta Kumar1 (AUTHOR), Panda, Dhruba Charan2 (AUTHOR), Mishra, Rabindra Kishore1,2 (AUTHOR) r.k.mishra@ieee.org
Source: AEU: International Journal of Electronics & Communications. Dec2021, Vol. 142, pN.PAG-N.PAG. 1p.
Subjects: Direction of arrival estimation, Machine learning, Standard deviations, Sequential learning, Array processors
Abstract: This article develops a fast online sequential random vector functional network. In this network, input nodes are connected to both hidden nodes and output nodes. This architecture results in high-speed learning and testing. The network estimates the direction of arrival from signals received by the sparse array in real-time. Neither noisy signal nor effect of mutual coupling among array elements affect the estimation appreciably using the network. The network shows competitive performance in terms of learning speed, accuracy, short event regression time, simplicity, and robustness in comparison to other state-of-the-art methods such as support vector regression, extreme learning machine, and online sequential extreme learning machine. Finally, validation of the network translation into a hardware platform using a fast digital high-speed reconfigurable Xilinx Virtex-5 field-programmable gate array embedded processor for single-source detection completes the work. Comparison with reported measurement shows results with good accuracy and low root mean square error both by the network and its hardware-implemented platform. The direction of arrival estimation is thus possible using the network either through a computer or dedicated hardware as per necessity. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:This article develops a fast online sequential random vector functional network. In this network, input nodes are connected to both hidden nodes and output nodes. This architecture results in high-speed learning and testing. The network estimates the direction of arrival from signals received by the sparse array in real-time. Neither noisy signal nor effect of mutual coupling among array elements affect the estimation appreciably using the network. The network shows competitive performance in terms of learning speed, accuracy, short event regression time, simplicity, and robustness in comparison to other state-of-the-art methods such as support vector regression, extreme learning machine, and online sequential extreme learning machine. Finally, validation of the network translation into a hardware platform using a fast digital high-speed reconfigurable Xilinx Virtex-5 field-programmable gate array embedded processor for single-source detection completes the work. Comparison with reported measurement shows results with good accuracy and low root mean square error both by the network and its hardware-implemented platform. The direction of arrival estimation is thus possible using the network either through a computer or dedicated hardware as per necessity. [ABSTRACT FROM AUTHOR]
ISSN:14348411
DOI:10.1016/j.aeue.2021.153986