A neural network approach to pulse radar detection

Author

Kwan, Hong Keung ; Lee, Chi Kin

Author_Institution

Dept. of Electr. Eng., Windsor Univ., Ont., Canada

Volume

29

Issue

1

fYear

1993

fDate

1/1/1993 12:00:00 AM

Firstpage

9

Lastpage

21

Abstract

A multilayer feedforward neural network is applied to pulse compression. The 13-element Barker code and the maximum-length sequences (m-sequences) with lengths 15, 31, and 63 b were used as the signal codes, and four networks were implemented, respectively. In each of these networks, the number of input units was the same as the signal length while the number of hidden units was three and the number of output units was one. In training each of these networks, backpropagation learning was used and the number of training epochs was 500. Using this approach, a more than 40 dB output peak signal-to-sidelobe ratio can be achieved. These fault-tolerant neural networks can provide a robust means for pulse radar detection

Keywords

backpropagation; fault tolerant computing; neural nets; radar theory; signal detection; Barker code; backpropagation learning; fault-tolerant neural networks; maximum-length sequences; misalignment; multilayer feedforward neural network; pulse compression; pulse radar detection; signal codes; training; Backpropagation; Councils; Fault tolerance; Feedforward neural networks; Filters; Multi-layer neural network; Neural networks; Neurons; Pulse compression methods; Radar detection; Robustness; Silicon;

fLanguage

English

Journal_Title

Aerospace and Electronic Systems, IEEE Transactions on

Publisher

ieee

ISSN

0018-9251

Type

jour

DOI

10.1109/7.249109

Filename

249109