Denoising of Radar Pulse Streams With Autoencoders

There are many cases in which the noise corrupts the signals in a significant manner. To better analyze these signals, the noise must be removed from the signals for further data analysis, and the process of noise removal is referred to as denoising. In this letter, we propose a novel approach to th...

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Published in:	IEEE communications letters Vol. 24; no. 4; pp. 797 - 801
Main Authors:	Li, Xueqiong, Liu, Zhang-Meng, Huang, Zhitao
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.04.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Artificial neural networks autoencoders binary coding Computer simulation Data analysis Decoding Denoising Feature extraction Noise Noise reduction Pulse repetition interval pulse streams Radar Signal denoising Signal processing Switches Task analysis TOA sequences Training
ISSN:	1089-7798, 1558-2558
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Abstract	There are many cases in which the noise corrupts the signals in a significant manner. To better analyze these signals, the noise must be removed from the signals for further data analysis, and the process of noise removal is referred to as denoising. In this letter, we propose a novel approach to the pulse denoising problem by extracting features from time of arrival (TOA) sequences using the autoencoders. The noise-contaminated TOA sequence is first coded into a binary vector and then fed into an autoencoder for training. Then, the trained autoencoder is capable of generating the original TOA sequence without lost and spurious pulses. Moreover, the proposed method does not require a noise-free TOA sequence as a priori as with conventional autoencoders. Simulation results show that the new technique can deal with TOA sequences with complex pulse repetition interval (PRI) modes that have not been tackled before. In addition, the proposed method has a better performance in noisy environments than conventional methods and general deep neural network structures.
AbstractList	There are many cases in which the noise corrupts the signals in a significant manner. To better analyze these signals, the noise must be removed from the signals for further data analysis, and the process of noise removal is referred to as denoising. In this letter, we propose a novel approach to the pulse denoising problem by extracting features from time of arrival (TOA) sequences using the autoencoders. The noise-contaminated TOA sequence is first coded into a binary vector and then fed into an autoencoder for training. Then, the trained autoencoder is capable of generating the original TOA sequence without lost and spurious pulses. Moreover, the proposed method does not require a noise-free TOA sequence as a priori as with conventional autoencoders. Simulation results show that the new technique can deal with TOA sequences with complex pulse repetition interval (PRI) modes that have not been tackled before. In addition, the proposed method has a better performance in noisy environments than conventional methods and general deep neural network structures.
Author	Li, Xueqiong Liu, Zhang-Meng Huang, Zhitao
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SubjectTerms	Artificial neural networks autoencoders binary coding Computer simulation Data analysis Decoding Denoising Feature extraction Noise Noise reduction Pulse repetition interval pulse streams Radar Signal denoising Signal processing Switches Task analysis TOA sequences Training
Title	Denoising of Radar Pulse Streams With Autoencoders
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