Road Environment Recognition for Automotive FMCW RADAR Systems Through Convolutional Neural Network

The objective of this paper is to propose a multiple target identification technique for orthogonal frequency division multiplexing (OFDM) radars. First, a 2-D (range & Doppler) periodogram is obtained from the reflected signal through 2-D fast Fourier transform (FFT) of the received OFDM symbols. Usually, the peaks of the periodogram indicates the targets. Conventionally, peak search algorithms are used to find the multiple targets. In this paper, however, a convolutional neural network (CNN) classifier is proposed to identify the targets. The proposed technique does not need any additional information but the 2-D periodogram while the conventional method requires the noise variance as well as the periodogram. The performance is examined through computer simulation. According to the results, if the number of maximum identifiable targets are small, the proposed technique performs well. However, as the number increases, the detection accuracy decreases. In the simulation environments, the proposed method outperforms the conventional one. The proposed OFDM radar technique can be applied to 6G mobile communications to identify the moving targets around the transmitter without additional frequency resource for radar systems.

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Intra-Pulse Modulation Classification of Radar Emitter Signals Based on a 1-D Selective Kernel Convolutional Neural Network

Remote Sensing ◽

10.3390/rs13142799 ◽

2021 ◽

Vol 13 (14) ◽

pp. 2799

Author(s):

Shibo Yuan ◽

Bin Wu ◽

Peng Li

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Data Preprocessing ◽

Fast Fourier Transformation ◽

Modulation Classification ◽

Pulse Modulation ◽

Radar Systems ◽

Tremendous Amount ◽

Amplitude Normalization

The intra-pulse modulation of radar emitter signals is a key feature for analyzing radar systems. Traditional methods which require a tremendous amount of prior knowledge are insufficient to accurately classify the intra-pulse modulations. Recently, deep learning-based methods, especially convolutional neural networks (CNN), have been used in classification of intra-pulse modulation of radar emitter signals. However, those two-dimensional CNN-based methods, which require dimensional transformation of the original sampled signals in the stage of data preprocessing, are resource-consuming and poorly feasible. In order to solve these problems, we proposed a one-dimensional selective kernel convolutional neural network (1-D SKCNN) to accurately classify the intra-pulse modulation of radar emitter signals. Compared with other previous methods described in the literature, the data preprocessing of the proposed method merely includes zero-padding, fast Fourier transformation (FFT) and amplitude normalization, which is much faster and easier to achieve. The experimental results indicate that the proposed method has the advantages of faster speed in data preprocessing and higher accuracy in intra-pulse modulation classification of radar emitter signals.

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CONVOLUTIONAL NEURAL NETWORK BASED ALGORITHM FOR CECUM ACHIEVEMENT CONFIRMATION

10.1055/s-0040-1705059 ◽

2020 ◽

Author(s):

S Kashin ◽

D Zavyalov ◽

A Rusakov ◽

V Khryashchev ◽

A Lebedev

Keyword(s):

Neural Network ◽

Convolutional Neural Network

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