scholarly journals Multi-Input Deep Learning Based FMCW Radar Signal Classification

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1144
Author(s):  
Daewoong Cha ◽  
Sohee Jeong ◽  
Minwoo Yoo ◽  
Jiyong Oh ◽  
Dongseog Han
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Continuous Wave ◽  
Autonomous Driving ◽  
Radar Data ◽  
Classification Performance ◽  
Radar Signal ◽  
Fmcw Radar ◽  
Input Structure ◽  
Single Input

In autonomous driving vehicles, the emergency braking system uses lidar or radar sensors to recognize the surrounding environment and prevent accidents. The conventional classifiers based on radar data using deep learning are single input structures using range–Doppler maps or micro-Doppler. Deep learning with a single input structure has limitations in improving classification performance. In this paper, we propose a multi-input classifier based on convolutional neural network (CNN) to reduce the amount of computation and improve the classification performance using the frequency modulated continuous wave (FMCW) radar. The proposed multi-input deep learning structure is a CNN-based structure using a distance Doppler map and a point cloud map as multiple inputs. The classification accuracy with the range–Doppler map or the point cloud map is 85% and 92%, respectively. It has been improved to 96% with both maps.

scholarly journals FPGA Implementation of an Efficient FFT Processor for FMCW Radar Signal Processing

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6443
Author(s):  
Jinmoo Heo ◽  
Yongchul Jung ◽  
Seongjoo Lee ◽  
Yunho Jung
Keyword(s):  
Signal Processing ◽  
Detection Rate ◽  
Continuous Wave ◽  
Vital Signs ◽  
Radar Signal ◽  
Radar Signal Processing ◽  
Fmcw Radar ◽  
Design And Implementation ◽  
Fft Processor ◽  
Point Operator

This paper presents the design and implementation results of an efficient fast Fourier transform (FFT) processor for frequency-modulated continuous wave (FMCW) radar signal processing. The proposed FFT processor is designed with a memory-based FFT architecture and supports variable lengths from 64 to 4096. Moreover, it is designed with a floating-point operator to prevent the performance degradation of fixed-point operators. FMCW radar signal processing requires windowing operations to increase the target detection rate by reducing clutter side lobes, magnitude calculation operations based on the FFT results to detect the target, and accumulation operations to improve the detection performance of the target. In addition, in some applications such as the measurement of vital signs, the phase of the FFT result has to be calculated. In general, only the FFT is implemented in the hardware, and the other FMCW radar signal processing is performed in the software. The proposed FFT processor implements not only the FFT, but also windowing, accumulation, and magnitude/phase calculations in the hardware. Therefore, compared with a processor implementing only the FFT, the proposed FFT processor uses 1.69 times the hardware resources but achieves an execution time 7.32 times shorter.

scholarly journals Asynchronous electric field visualization using an integrated multichannel electro-optic probe

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Shintaro Hisatake ◽  
Junpei Kamada ◽  
Yuya Asano ◽  
Hirohisa Uchida ◽  
Makoto Tojo ◽  
...  
Keyword(s):  
Continuous Wave ◽  
Near Field ◽  
Horn Antenna ◽  
Optimal Placement ◽  
Radar Signal ◽  
Radiation Patterns ◽  
Practical Applications ◽  
Fmcw Radar ◽  
Realistic Situation ◽  
Reference Probe

Abstract The higher the frequency, the more complex the scattering, diffraction, multiple reflection, and interference that occur in practical applications such as radar-installed vehicles and transmitter-installed mobile modules, etc. Near-field measurement in “real situations” is important for not only investigating the origin of unpredictable field distortions but also maximizing the system performance by optimal placement of antennas, modules, etc. Here, as an alternative to the previous vector-network-analyzer-based measurement, we propose a new asynchronous approach that visualizes the amplitude and phase distributions of electric near-fields three-dimensionally without placing a reference probe at a fixed point or plugging a cable to the RF source to be measured. We demonstrate the visualization of a frequency-modulated continuous wave (FMCW) signal (24 GHz ± 40 MHz, modulation cycle: 2.5 ms), and show that the measured radiation patterns of a standard horn antenna agree well with the simulation results. We also demonstrate a proof-of-concept experiment that imitates a realistic situation of a bumper installed vehicle to show how the bumper alters the radiation patterns of the FMCW radar signal. The technique is based on photonics and enables measuring in the microwave to millimeter-wave range.

scholarly journals A Deep Learning-Based Perception Algorithm Using 3D LiDAR for Autonomous Driving: Simultaneous Segmentation and Detection Network (SSADNet)

2020 ◽  
Vol 10 (13) ◽  
pp. 4486 ◽  
Author(s):  
Yongbeom Lee ◽  
Seongkeun Park
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Autonomous Driving ◽  
Light Detection And Ranging ◽  
Point Cloud Data ◽  
Cloud Data ◽  
3D Lidar ◽  
Top View ◽  
Single Neural Network ◽  
Reflection Intensity

In this paper, we propose a deep learning-based perception method in autonomous driving systems using a Light Detection and Ranging(LiDAR) point cloud data, which is called a simultaneous segmentation and detection network (SSADNet). SSADNet can be used to recognize both drivable areas and obstacles, which is necessary for autonomous driving. Unlike the previous methods, where separate networks were needed for segmentation and detection, SSADNet can perform segmentation and detection simultaneously based on a single neural network. The proposed method uses point cloud data obtained from a 3D LiDAR for network input to generate a top view image consisting of three channels of distance, height, and reflection intensity. The structure of the proposed network includes a branch for segmentation and a branch for detection as well as a bridge connecting the two parts. The KITTI dataset, which is often used for experiments on autonomous driving, was used for training. The experimental results show that segmentation and detection can be performed simultaneously for drivable areas and vehicles at a quick inference speed, which is appropriate for autonomous driving systems.

Semi-Physical Simulation System for FMCW Radar

2011 ◽  
Vol 135-136 ◽  
pp. 886-892
Author(s):  
Wen Hui Chen ◽  
Xin Xi Meng ◽  
Xiao Min Liu
Keyword(s):  
Power Consumption ◽  
Low Power ◽  
Physical Simulation ◽  
Continuous Wave ◽  
Low Cost ◽  
Simulation System ◽  
Radar Signal ◽  
Low Power Consumption ◽  
Fmcw Radar ◽  
Data Process

In order to process and analyze the signal of frequency modulated continuous wave (FMCW) radar, a radar semi-physical simulation(RSPS) system based on STM32F103VE6 chip is designed in this paper. By designing the hardware and software of system, the RSPS system can process the radar signal, detect the target, verify the data process algorithm and display the result on TFT-LCD screen. In addition, the collected data can be uploaded to PC by RS-232 interfaces which improves the reliability, stability and practicability of system. The waveform and spectrum maps are utilized to show the feasibility of RSPS system in analysing FMCW radar signal. Experimental results show that this system has many advantages, such as multifunction, low power consumption and low cost.

scholarly journals Observations of tropical rain with a polarimetric X-band radar: first results from the CHUVA campaign

2012 ◽  
Vol 5 (1) ◽  
pp. 1717-1761
Author(s):  
M. Schneebeli ◽  
J. Sakuragi ◽  
T. Biscaro ◽  
C. F. Angelis ◽  
I. Carvalho da Costa ◽  
...  
Keyword(s):  
Continuous Wave ◽  
Radar Data ◽  
Rain Attenuation ◽  
Fmcw Radar ◽  
X Band ◽  
Rain Drop ◽  
First Results ◽  
Radar Measurements ◽  
Drop Size Distributions ◽  
Set Up

Abstract. A polarimetric X-band radar has been deployed during one month (April 2011) for a field campaign in Fortaleza, Brazil, together with additional sensors like a Ka-band vertically pointing frequency modulated continuous wave (FMCW) radar and three laser disdrometers. The disdrometers as well as the FMCW radar are capable of measuring the rain drop size distributions (DSDs), hence making it possible to forward-model theoretical polarimetric X-band radar observables at the point where the instruments are located. This set-up allows to thoroughly test the accuracy of the X-band radar measurements as well as the algorithms that are used to correct the radar data for radome and rain attenuation. In the first campaign in Fortaleza it was found that radome attenuation dominantly affects the measurements. With an algorithm that is based on the self-consistency of the polarimetric observables, the radome induced reflectivity offset was estimated. Offset corrected measurements were then further corrected for rain attenuation with two different schemes. The performance of the post-processing steps is being analyzed by comparing the data with disdrometer-inferred polarimetric variables that were measured in a distance of 20 km to the radar.

scholarly journals Deep Learning-Based Indoor Two-Dimensional Localization Scheme Using a Frequency-Modulated Continuous Wave Radar

Electronics ◽  
2021 ◽  
Vol 10 (17) ◽  
pp. 2166
Author(s):  
Kyungeun Park ◽  
Jeongpyo Lee ◽  
Youngok Kim
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Continuous Wave ◽  
Two Dimensional ◽  
Fmcw Radar ◽  
Localization Scheme ◽  
Wave Radar ◽  
Conventional Scheme ◽  
24 Ghz

In this paper, we propose a deep learning-based indoor two-dimensional (2D) localization scheme using a 24 GHz frequency-modulated continuous wave (FMCW) radar. In the proposed scheme, deep neural network and convolutional neural network (CNN) models that use different numbers of FMCW radars were employed to overcome the limitations of the conventional 2D localization scheme that is based on multilateration methods. The performance of the proposed scheme was evaluated experimentally and compared with the conventional scheme under the same conditions. According to the results, the 2D location of the target could be estimated with a proposed single radar scheme, whereas two FMCW radars were required by the conventional scheme. Furthermore, the proposed CNN scheme with two FMCW radars produced an average localization error of 0.23 m, while the error of the conventional scheme with two FMCW radars was 0.53 m.

scholarly journals Vital Sign Monitoring Using FMCW Radar in Various Sleeping Scenarios

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6505
Author(s):  
Emmi Turppa ◽  
Juha M. Kortelainen ◽  
Oleg Antropov ◽  
Tero Kiuru
Keyword(s):  
Heart Rate ◽  
Remote Monitoring ◽  
Vital Sign ◽  
Continuous Wave ◽  
Real Life ◽  
Vital Signs ◽  
Absolute Error ◽  
Physical Contact ◽  
Radar Signal ◽  
Fmcw Radar

Remote monitoring of vital signs for studying sleep is a user-friendly alternative to monitoring with sensors attached to the skin. For instance, remote monitoring can allow unconstrained movement during sleep, whereas detectors requiring a physical contact may detach and interrupt the measurement and affect sleep itself. This study evaluates the performance of a cost-effective frequency modulated continuous wave (FMCW) radar in remote monitoring of heart rate and respiration in scenarios resembling a set of normal and abnormal physiological conditions during sleep. We evaluate the vital signs of ten subjects in different lying positions during various tasks. Specifically, we aim for a broad range of both heart and respiration rates to replicate various real-life scenarios and to test the robustness of the selected vital sign extraction methods consisting of fast Fourier transform based cepstral and autocorrelation analyses. As compared to the reference signals obtained using Embla titanium, a certified medical device, we achieved an overall relative mean absolute error of 3.6% (86% correlation) and 9.1% (91% correlation) for the heart rate and respiration rate, respectively. Our results promote radar-based clinical monitoring by showing that the proposed radar technology and signal processing methods accurately capture even such alarming vital signs as minimal respiration. Furthermore, we show that common parameters for heart rate variability can also be accurately extracted from the radar signal, enabling further sleep analyses.

scholarly journals Automotive Frequency Modulated Continuous Wave Radar Interference Reduction Using Per-Vehicle Chirp Sequences

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2831 ◽  
Author(s):  
Youn-Sik Son ◽  
Hyuk-Kee Sung ◽  
Seo Heo
Keyword(s):  
Continuous Wave ◽  
Signal Interference ◽  
Radar Signal ◽  
Driver Assistance ◽  
Interference Reduction ◽  
Fmcw Radar ◽  
Radar Sensors ◽  
Vehicle To Vehicle ◽  
Radar Systems ◽  
Wave Radar

Recently, many automobiles adopt radar sensors to support advanced driver assistance system (ADAS) functions. As the number of vehicles with radar systems increases the probability of radar signal interference and the accompanying ghost target problems become serious. In this paper, we propose a novel algorithm where we deploy per-vehicle chirp sequence in a frequency modulated continuous wave (FMCW) radar to mitigate the vehicle-to-vehicle radar interference. We devise a chirp sequence set so that the slope of each vehicle’s chirp sequence does not overlap within the set. By assigning one of the chirp sequences to each vehicle, we mitigate the interference from the radar signals transmitted by the neighboring vehicles. We confirm the performance of the proposed method stochastically by computer simulation. The simulation results show that the detection and false alarm performance is improved significantly by the proposed method.

scholarly journals Deep Learning for Image and Point Cloud Fusion in Autonomous Driving: A Review

2021 ◽  
pp. 1-18
Author(s):  
Yaodong Cui ◽  
Ren Chen ◽  
Wenbo Chu ◽  
Long Chen ◽  
Daxin Tian ◽  
...  
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Autonomous Driving
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