소형 선박용 FMCW 레이더 시스템에서의 위상 배열 안테나를 사용한 선박의 위치 추정

In this paper, we compare the performances of impulse radio ultra-wideband (IR-UWB) and frequency modulation continuous wave (FMCW) radars in measuring noncontact vital signs such as respiration rate and heart rate. These two type radars have been widely used in various fields and have shown their applicability to extract vital signs in noncontact ways. IR-UWB radar can extract vital signs using distance information. On the other hand, FMCW radar requires phase information to estimate vital signs, and the result can be enhanced with Multi-input Multi-output (MIMO) antenna topologies. By using commercial radar chipsets, the operation of radars under different conditions and frequency bands will also affect the performance of vital sign detection capabilities. We compared the accuracy and signal-to-noise (SNR) ratios of IR-UWB and FMCW radars in various scenarios, such as distance, orientation, carotid pulse, harmonics, and obstacle penetration. In general, the IR-UWB radars offer a slightly better accuracy and higher SNR in comparison to FMCW radar. However, each radar system has its own unique advantages, with IR-UWB exhibiting fewer harmonics and a higher SNR, while FMCW can combine the results from each channel.

In this paper proposed is a cost-efficient signal processing algorithm of a frequency modulation continuous wave (FMCW) radar for the close range under indoor environments. Recently, the use of an FMCW radar has been expanded to short distance from long distance due to easy production and inexpensive designing costs. In order to obtain satisfying resolution of the close range radar, it still requires a wide modulation bandwidth, which results in high costing wideband RF devices. This paper proposes a cost-efficient signal processing method using correlations between a reflected signal in frequency-domain and a lookup table which holds the frequency information depending on distances. The proposed algorithm shows the similar performance to a wideband system just by updating a firmware of low costing narrowband RF FMCW radar devices without additional hardware modification.

Frequency-modulated continuous wave (FMCW) radar has seen a significant increase in use due to its ability to operate in low-power and mobile environments suited for low peak power solid state transmitters. Though there are many current applications for the FMCW technique, the majority are focused on measuring “hard” targets (e.g., cars, humans, etc.) and it is not heavily used in the measurement of volume targets such as precipitation. Moreover, current FMCW radars used for volumetric measurements are generally focused on very specific use cases such as vertical profiling. This work presents DARMA, a low-power, dual-polarized, FMCW radar with a phased array antenna capable of being deployed on unmanned aircraft systems (UAS). It will be used to test the feasibility of measuring and classifying complex weather events on a mobile platform which will provide different perspectives at the lowest kilometer of precipitation volumes.

In this article, a novel method for continuous hand gesture detection and recognition is proposed based on a frequency modulated continuous wave (FMCW) radar. Firstly, we adopt the 2-Dimensional Fast Fourier Transform (2D-FFT) to estimate the range and Doppler parameters of the hand gesture raw data, and construct the range-time map (RTM) and Doppler-time map (DTM). Meanwhile, we apply the Multiple Signal Classification (MUSIC) algorithm to calculate the angle and construct the angle-time map (ATM). Secondly, a hand gesture detection method is proposed to segment the continuous hand gestures using a decision threshold. Thirdly, the central time-frequency trajectory of each hand gesture spectrogram is clustered using the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -means algorithm, and then the Fusion Dynamic Time Warping (FDTW) algorithm is presented to recognize the hand gestures. Finally, experiments show that the accuracy of the proposed hand gesture detection method can reach 96.17%. The hand gesture average recognition accuracy of the proposed FDTW algorithm is 95.83%, while its time complexity is reduced by more than 50%.

Phase noise can severely degrade the radar performances by producing strong sidelobes which may mask small targets. Reducing those sidelobes by improving the radar analog front-end severely increases the system complexity. Another approach consists in developing waveforms more robust to phase noise. Even if the phase noise phenomenon is well known, it has been intensively investigated only in Frequency Modulated Continuous Wave (FMCW) radars, not so much in Phase Modulated Con-tinuous Wave (PMCW) radars. In this paper, we will provide a detailed analysis of the impact of phase noise on FMCW radars and on different code sequences for PMCW radars. This analysis will be used to compare the impact of phase noise on different waveforms. Both range profile and Doppler profile will be investigated. We will show that radar waveforms can be designed to provide low sidelobes in presence of phase noise. Our model will be validated by simulation.

In order to reduce the probability of automobile safety incidents, the in-vehicle occupant monitoring is indispensable. However, occupant monitoring using frequency-modulated continuous wave (FMCW) radar can be challenging due to the interference from passengers’ posture, movement, and the presence of multiple people. This paper proposes an improved method for generating point clouds using FMCW radar. The approach involves point cloud clustering, post-processing operations such as segmentation, merging, and filtering of the clustered point cloud to match the actual in-vehicle environment, and a state machine combination step. Experimental results show that the proposed method can achieve high recognition accuracy in scenarios with multiple passengers who are moving and sitting in a relaxed manner.

Human body detection is very important in the research of automotive safety technology. The extraction and analysis of human micro-motion based on frequency-modulated continuous wave (FMCW) radar is gradually receiving attention. Aimed at the modulation effect of human micro-motion on FMCW radar, a human running model is proposed to study human radar characteristics. According to the scattering characteristics of rigid bodies, the analytical expression of human running radar echoes is established. By using time–frequency analysis, the micro-Doppler features in the radar echoes are extracted during the running period. Under running conditions, the micro-Doppler characteristics of key components are studied. This model is applied to the real FMCW radar verification platform, and the runners are measured at a distance of 10 m. The fit rate of all parts of the human body can reach above 90%. The overall fit rate of the human model can reach up to 90.6%. The model proposed is a realistic and simple human kinematic model. This model, which can realize the real simulation of a running human body and provide strong support for human target radar echo analysis, can fill the deficiency of FMCW radar technology in the complex motion model.

This letter presents a novel method to reduce measurement time for remote heart rate measurement using millimeter-wave frequency-modulated continuous wave (FMCW) radar. The proposed method is based on the sampling theory and the frame structure of recent FMCW radar systems. The range displacement from heartbeat is measured by 60 GHz FMCW radar with a fixed frame interval, 50 ms. With additional chirp repetition in each frame, the heartbeat signal can be sampled with increased or decreased interval. By adopting three different sampling rates from a single radar measurement, the resolution of heart rate measurement is improved in a short time, 2.5 s. The proposed method is evaluated with a commercial off-the-shelf FMCW radar system and three different human subjects, for no respiration and natural status. To evaluate the performance of the proposed method, the heart rate result is compared with electrocardiogram (ECG) sensor and conventional signal processing method. The results show that the proposed method estimates the heart rate with lower error and shorter measurement time.

We note that the cost function of the MUSIC (multiple signal classification) algorithm is quadratic in an array vector and that it can be expressed in a least squares form. Based on this observation, we present a rigorous Levenberg-Marquardt (LM) formulation of the MUSIC algorithm for simultaneous estimation of an azimuth and an elevation. We show a convergence property and compare the performance of the LM-based MUSIC algorithm with that of the standard MUSIC algorithm via Monte-Carlo simulation. We also compare the performance of the MUSIC algorithm with that of the Capon algorithm both for the standard implementation and for the LM-based implementation.

The reliability of measurements is crucial for ensuring the safety of control systems that depend on such measurements. Frequency-modulated continuous-wave (FMCW) radar is an active sensor used to measure distance and speed. Security evaluations of commercial FMCW radars have focused primarily on deception attacks, assuming that jamming attacks are easier to address. In this study, we propose a novel and efficient jamming attack called cover chirp jamming. This attack utilizes deception techniques and concentrates energy near the target, resulting in higher efficiency compared to conventional jamming methods. Furthermore, it can bypass existing countermeasures against noise, interference, and jamming. We demonstrate the effectiveness and feasibility of the attack through field and simulation experiments using a modern 77-GHz multi-input multi-output FMCW radar. Moreover, we propose a software-based countermeasure that detects and mitigates the attack. Our quantitative evaluation shows that the power of cover chirp jamming is 17.4 dB higher than conventional jamming. In addition, the countermeasure effectively mitigates the attack if the jamming-to-signal ratio (JSR) is below 0.6 dB, whereas the cover chirp jamming cannot be mitigated when the JSR exceeds 0.6 dB.

Millimeter-wave radars are essential for automotive sensing, medical imaging, and safety- monitoring applications. Among different architectures, frequency-modulated continuous-wave (FMCW) radars are particularly suited for miniature integration targeting portable consumer applications with detection ranges up to a few tens of meters. This translates to a relatively low requirement of transmitter (TX) output peak power and hence is well suited for implementation using low-supply-voltage CMOS processes. To avoid saturating the receiver (RX) under CW operation, FMCW radars must achieve high TX-to-RX isolation in the front-end. Typically, at least 40dB of isolation is necessary for W-band (75 to 85GHz) FMCW radars, which usually employ separate TX and RX antennas. Hence, this requirement dictates the minimum achievable radar size as a typical W-band planar antenna array requires a footprint of about 10cm2. Sharing a single antenna between RX and TX is thus highly desirable to reduce the overall form factor, which is paramount for portable systems. As a result, different analog and digital TX leakage-cancellation circuit techniques have been proposed to support single-antenna operation [1], [2]. In this work, we present a single-antenna TRX SoC and module design for an 80GHz FMCW radar featuring continuous-time adaptive leakage cancellation. © 2020 IEEE.

Ground Penetrating Radar (GPR) application in road surface detection has been greatly developed in the past few decades, which enables rapid and economical estimation of pavement thickness and other physical properties in non-destructive testing (NDT) and non-contact testing (NCT). In recent years, with the rapid development of microwave and millimeter-wave solid-state devices and digital signal processors, the cost of Frequency-Modulated Continuous-Wave (FMCW) radar has dropped significantly, with smaller size and lighter weight. Thereafter, FMCW GPR is considered to be applied during pavement inspection. To improve the precision of FMCW GPR for NDT and NCT of pavement thickness, a Chirp Z-transform (CZT) algorithm is introduced to FMCW GPR and investigated in this paper. A FMCW + CZT GPR at 2.5 GHz with a bandwidth of 1 GHz was built, and laboratory and field experiments were carried out. The experimental results demonstrate that the FMCW + CZT GPR radar can obtain the sample thickness with low error and recognize subtle thickness variations. This method realizes the high precision thickness measurement of shallow asphalt pavement by FMCW radar with a narrow bandwidth pulse signal and would provide a promising low-cost measurement solution for GPR.

The continuous operation of frequency modulated continuous wave (FMCW) radar transceivers leads to permanent leakage from the transmit into the receive path. Besides leakage within FMCW radar systems itself, unwanted reflections from a close object in front of the radar antennas results in so-called short-range (SR) leakage. Specifically the phase noise (PN) contained in the SR leakage causes a severe sensitivity degradation of the radar. In this work, we propose a mixed-signal approach to mitigate the SR leakage, including its contained PN. It makes use of an artificial on-chip target (OCT), whose output signal is modulated on a sinusoidal generated in the digital domain. The concept is based on cross-correlation properties of the residual PN between the OCT and the SR leakage in the intermediate frequency (IF) domain of the transceiver. We prove our analytical investigations with an FMCW radar simulation, wherein almost perfect SR leakage cancelation is achieved.

A respiratory disorder that attacks COVID-19 patients requires intensive supervision of medical practitioners during the isolation period. A non-contact monitoring device will be a suitable solution for reducing the spread risk of the virus while monitoring the COVID-19 patient. This study uses Frequency-Modulated Continuous Wave (FMCW) radar and Machine Learning (ML) to obtain respiratory information and analyze respiratory signals, respectively. Multiple subjects in a room can be detected simultaneously by calculating the Angle of Arrival (AoA) of the received signal and utilizing the Multiple Input Multiple Output (MIMO) of FMCW radar. Fast Fourier Transform (FFT) and some signal processing are implemented to obtain a breathing waveform. ML helps the system to analyze the respiratory signals automatically. This paper also compares the performance of several ML algorithms such as Multinomial Logistic Regression (MLR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGBM), CatBoosting (CB) Classifier, Multilayer Perceptron (MLP), and three proposed stacked ensemble models, namely Stacked Ensemble Classifier (SEC), Boosting Tree-based Stacked Classifier (BTSC), and Neural Stacked Ensemble Model (NSEM) to obtain the best ML model. The results show that the NSEM algorithm achieves the best performance with 97.1% accuracy. In the real-time implementation, the system could simultaneously detect several objects with different breathing characteristics and classify the respiratory signals into five different classes.

A novel lightweight hand gesture recognition approach that is based on Frequency-Modulated Continuous Wave (FMCW) RADAR, which aims to minimize computational complexity and memory usage as well as maintain a high recognition performance, is proposed in this paper. Most of the existing methods utilize two-dimensional (2D) or three-dimensional (3D) features that are combined with complex neural network structures, which result in high computational costs. The proposed approach in contrast extracts four components, which include range, Doppler, azimuth, and elevation, as the one-dimensional (1D) time-series features. These features are fed into a neural network that comprises of a multi-branch temporal convolutional network, depthwise separable convolutions, and a channel attention mechanism in order to enhance the classification performance. The experiments were conducted with 9 hand gestures that were collected from 9 participants. The proposed system achieved a high accuracy of 99.38% with only 44.6K parameters and 1.84M FLOPs. Extensive ablation studies and comparative experiments against the existing models demonstrated that the proposed method effectively balances the performance and computational efficiency. This study validates the expressive capability of 1D features for hand gesture recognition and suggests practical applicability in resource-constrained environments, such as embedded systems.