Autoencoder-Based Target Detection in Automotive MIMO FMCW Radar System

In general, a constant false alarm rate algorithm (CFAR) is widely used to automatically detect targets in an automotive frequency-modulated continuous wave (FMCW) radar system. However, if the number of guard cells, the number of training cells, and the probability of false alarm are set improperly...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 22; no. 15; p. 5552
Main Authors: Kang, Sung-wook, Jang, Min-ho, Lee, Seongwook
Format: Journal Article
Language:English
Published: Basel MDPI AG 25.07.2022
MDPI
Subjects:
Algorithms
Antennas
autoencoder
Climate change
constant false alarm rate
Deep learning
False alarms
Fourier transforms
frequency-modulated continuous wave radar
multiple-input and multiple-output
Neural networks
Radar systems
Sensors
target detection
ISSN:1424-8220, 1424-8220
Online Access:Get full text
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Summary:In general, a constant false alarm rate algorithm (CFAR) is widely used to automatically detect targets in an automotive frequency-modulated continuous wave (FMCW) radar system. However, if the number of guard cells, the number of training cells, and the probability of false alarm are set improperly in the conventional CFAR algorithm, the target detection performance is severely degraded. Therefore, we propose a method using a convolutional neural network-based autoencoder (AE) to replace the CFAR algorithm in the multiple-input and multiple-output FMCW radar system. In the AE, the entire detection result is compressed at the encoder side, and only significant signal components are recovered on the decoder side. In this work, by changing the number of hidden layers and the number of filters in each layer, the structure of the AE showing a high signal-to-noise ratio in the target detection result is determined. To evaluate the performance of the proposed method, the AE-based target detection result is compared with the target detection results of conventional CFAR algorithms. As a result of calculating the correlation coefficient with the data marked with the actual target position, the proposed AE-based target detection shows the highest similarity with a correlation of 0.73 or higher.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s22155552