Joint Angle and Range Estimation for Frequency Diverse Array Using Multi-layer Perceptron Neural Network

Autor:	Deping Xia, Tianfang Chen
Rok vydání:	2019
Předmět:	Hardware_MEMORYSTRUCTURES Artificial neural network Computer science business.industry Computer Science::Neural and Evolutionary Computation ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 020206 networking & telecommunications Pattern recognition 02 engineering and technology 010501 environmental sciences Perceptron 01 natural sciences Signal Joint angle Computer Science::Computer Vision and Pattern Recognition Multi layer perceptron neural network 0202 electrical engineering electronic engineering information engineering Range (statistics) Artificial intelligence business 0105 earth and related environmental sciences
Zdroj:	ICCT
DOI:	10.1109/icct46805.2019.8947138
Popis:	This paper proposes a joint angle and range estimation method for frequency diverse array (FDA) using multi-layer perceptron (MLP) neural network. Signal data generated by ideal FDA radar system model are used to construct the MLP model. Through experimental evaluation, the performance of the trained MLP neural network is demonstrated.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::d330f9896f978c774d0cb99c98115b3a https://doi.org/10.1109/icct46805.2019.8947138 Zobrazit plný text záznamu