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 Neural Networks Application for Processing of the Data from the FMICW Radars

Symmetry ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1308 ◽  
Author(s):  
Lubos Rejfek ◽  
Tan N. Nguyen ◽  
Pavel Chmelar ◽  
Ladislav Beran ◽  
Phuong T. Tran
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Signal Processing ◽  
Continuous Wave ◽  
Doppler Radar ◽  
Radar Signal ◽  
Radar Signal Processing ◽  
Target Area ◽  
The Neural Network ◽  
Machine Learning Applications

In this paper the results of the Neural Networks and machine learning applications for radar signal processing are presented. The radar output from the primary radar signal processing is represented as a 2D image composed from echoes of the targets and noise background. The Frequency Modulated Interrupted Continuous Wave (FMICW) radar PCDR35 (Portable Cloud Doppler Radar at the frequency 35.4 GHz) was used. Presently, the processing is realized via a National Instruments industrial computer. The neural network of the proposed system is using four or five (optional for the user) signal processing steps. These steps are 2D spectrum filtration, thresholding, unification of the target, target area transforming to the rectangular shape (optional step), and target board line detection. The proposed neural network was tested with sets of four cases (100 tests for every case). This neural network provides image processing of the 2D spectrum. The results obtained from this new system are much better than the results of our previous algorithm.

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.

Automotive Anti-Collision Radar Signal Processing System Design Based on DSP

2014 ◽  
Vol 1044-1045 ◽  
pp. 727-730
Author(s):  
Xin Yu Zhang
Keyword(s):  
Signal Processing ◽  
Power Supply ◽  
Continuous Wave ◽  
Processing System ◽  
Radar Signal ◽  
Radar Signal Processing ◽  
Signal Processing System ◽  
Positive Role ◽  
Wave Radar ◽  
Circuit Power

The principle of the frequency modulated continuous wave radar for measuring distance and velocity and signal processing method are described in this paper; To solve the signal processing problem for FMCW automotive anti-collision radar system ,the radar signal processing circuit is researched and designed, including the design of corresponding gain control amplifier circuit, power supply and filter circuit, external memory circuit ,power supply circuit ,signal interface circuit, and analyzed the results of the measurement for system. Experiments showed that the system achieved good accuracy design effect and higher measurement precision and has certain positive role to improve vehicle safety.

scholarly journals Design and Implementation Pulse Compression for S-Band Surveillance Radar

2020 ◽  
Vol 7 (1) ◽  
pp. 20
Author(s):  
Kalfika Yani ◽  
Fiky Y Suratman ◽  
Koredianto Usman
Keyword(s):  
Signal Processing ◽  
High Speed ◽  
Pulse Compression ◽  
Continuous Wave ◽  
Radar Signal ◽  
Radar Signal Processing ◽  
Digital Platform ◽  
Range Resolution ◽  
Pulse Compression Radar ◽  
Cw Radar

The radar air surveillance system consists of 4 main parts, there are antenna, RF front-end, radar signal processing, and radar data processing. Radar signal processing starts from the baseband to IF section. The radar waveform consists of two types of signal, there are continuous wave (CW) radar, and pulse compression radar [1]. Range resolution for a given radar can be significantly improved by using very short pulses. Pulse compression allows us to achieve the average transmitted power of a relatively long pulse, while obtaining the range resolution corresponding to a short pulse. Pulse compression have compression gain. With the same power, pulse compression radar can transmit signal further than CW radar. In the modern radar, waveform is implemented in digital platform. With digital platform, the radar waveform can optimize without develop the new hardware platform. Field Programmable Gate Array (FPGA) is the best platform to implemented radar signal processing, because FPGA have ability to work in high speed data rate and parallel processing. In this research, we design radar signal processing from baseband to IF using Xilinx ML-605 Virtex-6 platform which combined with FMC-150 high speed ADC/DAC.

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