Continuous Wave Radar System Research Articles

Determination of the Amount of Grain in Silos With Deep Learning Methods Based on Radar Spectrogram Data

Since the grain is a crucial food source, the determination of the quantity of stored grain in silos is inevitable in terms of commercial and correct inventory planning. In this study, a convolutional neural network (CNN) is developed to determine grain quantity using the spectrograms of the radar backscattering data. The radar backscattering signals of different amounts of grain for different grain surface condition types are collected using a stepped frequency continuous-wave radar system. In the scaled model silo, a total of 5681 measurements are carried out for grain stacks with different surface patterns and different weights (0-20 kg). Then, the dataset is constituted by using the spectrograms of these radar measurements. Randomly selected 4261 data corresponding to 75% of the dataset are used for training and the remaining 1420 data are used for testing. The proposed method is compared with pretrained CNN. Accuracy of the methods is given with metric parameters for both classification and regression. The classification task results of the proposed method are obtained as 98.45% accuracy, 98.15% sensitivity, 99.07% specitivity, 98.77% precision, 98.45% F1-Score, and 97.62% Matthews correlation coefficient. The regression task results are calculated as 0.3228 mean absolute error, 0.5150 mean absolute percentage error (MAPE), 0.9649 mean squared error, and 0.9823 root-mean-squared error. The proposed method is also compared with previous studies in the literature (with 3.29 MAPE) and its superiority is demonstrated with metric parameters. The results point out that, if CNN is properly modeled and trained, the combination of CNN and proper signal processing can provide effective results in the quantity measurement applications of the grain stacks.

A Human Fall Detection System Using Continuous Wave Radar

Background & Objective: In this paper contactless human fall detection system has been designed, developed and tested. Methods: Continuous wave radar system is implemented at 2.10 GHz of frequency. It consists of transmitter and receiver section. In Radio Frequency (RF) transmitter system is developed with the use of frequency synthesizer, power amplifier and patch antenna for the transmission of 2.10 GHz. Similarly, at the receiver side 2.1001GHz of frequency signal is generated with the use of frequency synthesizer. For the measurement of the fall detection high frequency signal is down converted to 100 KHz of signal with the use of mixer. Number of experiment has been performed for the measurement of fall detection. Here non-living object has been used for the experimental purpose. Results & Conclusion: A fall event has been detected according to the change in the received frequency in respect with the reference frequency.

A K-Band FMCW Frequency Synthesizer Using Q-Boosted Switched Inductor VCO in SiGe BiCMOS for 77 GHz Radar Applications

In this article, a fractional-N phase-locked loop (PLL) with integrated chirp generation circuit block for a 76~81 GHz frequency-modulated continuous-wave (FMCW) radar system is presented. Thanks to the switched inductor voltage-controlled oscillator (VCO) topology, the linearity, phase noise, chirp bandwidth, and chirp rate of the FMCW synthesizer can be optimized for the short-range radar (SRR) and long-range radar (LRR) applications, with switch at ON/OFF states, respectively, according to different requirements and concerns. In this way, the proposed FMCW synthesizer shows improved phase noise for switch OFF-state, good for LRR applications, compared to the conventional single-varactor VCOs or cap-bank VCOs. The switch loss at ON-state is further decreased with the Q-boosting technique, which helps the FMCW synthesizer to simultaneously obtain a wide chirp bandwidth, steep modulation rates and good phase noise for SRR applications. The FMCW synthesizer is fabricated in 0.13 µm SiGe BiCMOS technology, occupies an area of 1.7 × 1.9 mm2, and consumes 330 mW from a 3.3 V voltage supply. Measured results show that the FMCW synthesizer can cover 25.3~27 GHz (with a frequency tripler to fully cover 76~81 GHz band), showing optimized phase noise, chirp bandwidth, linearity, and modulation rates performance. The measured K-band phase noise is −110.5 dBc/Hz for switch OFF-state, and −106 dBc/Hz for switch ON-state at 1 MHz offset. The normalized root mean square (RMS) frequency error is 518 kHz for chirp rate of ±14.6 MHz/μs and 1.44 MHz for chirp rate of ±39 MHz/μs for the 77 GHz band. Moreover, the integrated waveform generator offers fully programmability in chirp rate, duration and bandwidth, which supports multi-slope chirp generations. With a frequency tripler, the chip is well suited for the 76~81 GHz FMCW radar system.

Adaptive leakage signal cancellation algorithm in heterodyne FMCW system

To mitigate the leakage signal that obstructs the target information extraction in miniatured frequency modulated continuous wave (FMCW) radar system, an adaptive leakage signal cancellation algorithm is proposed in this paper. Without any help of the additional auxiliary path, all the processing for the intermediate frequency (IF) signal can be conducted in the digital domain. The basic principle to cancel leakage signal is to produce a cancelling signal, which has the same amplitude and anti-phase with the leakage signal. Differently, in this paper, the input phase parameter of the proposed method can be arbitrary and it can be modified by the corresponding corrected function. Besides, to acquire the phase noise in the leakage signal conveniently when generating cancelling signal, the information geometry theory is applied to transform the complex phase noise model into statistical characteristics calculation of the input signal. The effectiveness of the model and the proposed algorithm is verified by simulated results. Furthermore, a real FMCW system is designed to collect the practice echo signals. The resulting data denotes that the leakage signal in the received signal is greatly suppressed and the leakage signal power drops by −15.92 dB.

A novel approach to fractional-N PLLs generating ultra-fast low-noise chirps for FMCW radar

This paper presents a novel approach to generate ultra-fast chirps for frequency-modulated continuous wave (FMCW) radar systems. A symmetric triangular frequency chirp is analyzed. A very large loop bandwidth could minimize the PLL settling time, but would result in a high phase noise and in large spurs. This work minimizes the settling times without using an excessive loop bandwidth. Rather, the initial condition of the dynamic phase error after the turn-around points (TAPs) is minimized by switching the reference phase in conjunction with one of the two methods: Firstly, a well-defined offset current is added to the charge pump output current, and its sign is switched at the TAPs. Secondly, a binary weighted loop filter capacitor array is used in the loop filter, and the total capacitance is adapted to the slope of the frequency sweep. While the first method lends itself to a fully integrated PLL design including the loop filter, the second method achieves a lower in-band phase noise.

A Target Detection Scheme With Decreased Complexity and Enhanced Performance for Range-Doppler FMCW Radar

In conventional fast-ramp-based target detection schemes for the frequency modulated continuous wave (FMCW) radar systems, all chirp sequences are performed fully on two-dimensional fast Fourier transforms (FFTs) to estimate range and velocity of a target, and all range-Doppler cells are scanned by the two-dimensional (2-D) cell-averaging constant false alarm rate (CA-CFAR) detector to extract valid targets against background noise. This results in an extremely high computational complexity. To decrease the complexity while maintaining a desired performance, this article proposes a target detection scheme based on distributed one-dimensional (1-D) CA-CFAR and region-of-interest (ROI) preprocessing, which performs Doppler-FFT only for range ROI cells and scans only Doppler ROI cells to extract pretargets. Simulation results indicate that the proposed scheme significantly decreases complexity and simultaneously improves detection performance in terms of detection probability, figure of merit (FoM), and CFAR loss, as compared with the conventional schemes. The advantages of the proposed scheme over the conventional ones are further verified by field tests under multitarget scenario, using our recently developed FMCW radar with high angular resolution.

Non-Contact Multi-Subject Human Gait Analysis Using A State-Space Method With Enhanced 1-D Block Representation

A stepped-frequency continuous-wave (SFCW) radar system with adequate pulse repetition frequency (PRF) is developed to track relatively fast motions from various parts of the human body, separately. Robust signal processing technique is utilized, where a 1-D block processing technique, based on state-space method (SSM) is developed in two dimensions to form an enhanced Hankel matrix to track human motions from two-dimensional (2-D) data collected using the developed SFCW radar system in single- or multi-subject motion scenarios. Experimental datasets are used to track body parts' motions and velocities with high accuracy. The significant improvement, besides de-noising the data, is due to the fact that applying the 1-D block processing technique to 2-D dataset matrix accounts for the correlation between motion's estimates from consecutive frames. Results agree well with our reference, the human Boulic model.

A dataset of clinically recorded radar vital signs with synchronised reference sensor signals

Using Radar it is possible to measure vital signs through clothing or a mattress from the distance. This allows for a very comfortable way of continuous monitoring in hospitals or home environments. The dataset presented in this article consists of 24 h of synchronised data from a radar and a reference device. The implemented continuous wave radar system is based on the Six-Port technology and operates at 24 GHz in the ISM band. The reference device simultaneously measures electrocardiogram, impedance cardiogram and non-invasive continuous blood pressure. 30 healthy subjects were measured by physicians according to a predefined protocol. The radar was focused on the chest while the subjects were lying on a tilt table wired to the reference monitoring device. In this manner five scenarios were conducted, the majority of them aimed to trigger hemodynamics and the autonomic nervous system of the subjects. Using the database, algorithms for respiratory or cardiovascular analysis can be developed and a better understanding of the characteristics of the radar-recorded vital signs can be gained.

Stationary Target Identification in a Traffic Monitoring Radar System

Recently, as one of the intelligent transportation systems, radar systems that monitor traffic on the road have received attention. To ensure the reliable detection performance of the traffic monitoring radar, it is necessary to distinguish stationary road structures from moving vehicles. Therefore, in this paper, we propose a method for discriminating stationary targets in traffic monitoring radar systems. First, we install a frequency-modulated continuous wave radar system using a center frequency of 24.15 GHz on an overpass to monitor multiple lanes on the road. Then, we process the raw data obtained by the radar sensor to extract target information such as the distance, angle, velocity, and radar cross-section. Finally, we analyze the target characteristics in the angle-velocity domain to classify stationary targets and moving vehicles. In this domain, stationary targets appear as points lying around a straight line, and if we estimate that line, we can extract the stationary targets among all targets. To find the trend line, we use a random sample consensus-based estimation method, which can extract a dominant line component from a set of sample points. Through the proposed method, we can effectively remove the stationary targets in the field of view of the radar system.

Improved Drone Classification Using Polarimetric Merged-Doppler Images

We propose a drone classification method for polarimetric radar, based on convolutional neural network (CNN) and image processing methods. The proposed method improves drone classification accuracy when the micro-Doppler signature is very weak by the aspect angle. To utilize received polarimetric signal, we propose a novel image structure for three-channel image classification CNN. To reduce the size of data from four different polarization while securing high classification accuracy, an image processing method and structure are introduced. The data set is prepared for a three type of drone, with a polarimetric Ku-band frequency modulated continuous wave (FMCW) radar system. Proposed method is tested and verified in an anechoic chamber environment for fast evaluation. A famous CNN structure, GoogLeNet, is used to evaluate the effect of the proposed radar preprocessing. The result showed that the proposed method improved the accuracy from 89.9% to 99.8%, compared with single polarized micro-Doppler image. We compared the result from the proposed method with conventional polarimetric radar image structure and achieved similar accuracy while having half of full polarimetric data.

Opportunities and challenges in RCS measurement of 9‐mm bullet model with 77 GHz mmwave COTS radar systems

AbstractThis article indicates a thus far unexplored area of applied research and development to the application and system engineers and researchers from broad engineering backgrounds. Results of a study are presented for measurement of calibrated Radar Cross Section (RCS) of a 9‐mm bullet (projectile) model by using a commercial‐of‐the‐shelf (COTS) millimeter wave Frequency Modulated Continuous Wave (FMCW) radar system operating in 77 to 81 GHz frequency range. The calibrated RCS variation against the aspect angle is measured experimentally, analyzed, and compared with the simulation results which shows fair matching between the two. The opportunities and challenges attached with the use of such COTS systems for development of Hostile Fire Indication (HFI) systems are discussed. This bullet type and this mmwave frequency has not been thus far studied and reported in literature. This may motivate interested individuals/entities to try to measure (at acceptable accuracy before anechoic chamber measurements) RCS of similar low‐size objects by using such low‐cost COTS platforms.

Guided Reflectometry Imaging Unit Using Millimeter Wave FMCW Radars

Frequency-modulated continuous wave (FMCW) radar systems in the millimeter and submillimeter range are technologically mature for many applicative fields such as automotive and aerospace industries for imaging and nondestructive testing. This work reports on a new implementation of a guided FMCW radar reflectometry unit for sensing and imaging applications. Only a terahertz dielectric waveguide is used for signal transmission between the transceiver module and the sample, thus drastically simplifying the experimental setup. Compared to continuous wave guided systems, one of the main advantages granted by the use of FMCW radars in combination with waveguides, is the differentiation capability between the reflected signals generated along the wave guide as parasitic signals or at its probing end as sensing information and therefore improving the expected signal-to-noise ratio. This innovative approach is demonstrated by using a dielectric hollow-core waveguide integrated with two different radar transceivers; the high-performance, III–V based 100 GHz SynView unit as a reference system and a compact, low-cost, printed circuit board (PCB)-integrated, 122 GHz transceiver developed by silicon-radar GmbH. Both three-dimensional electromagnetic simulations and raster scans are performed to investigate quantitatively the propagation behaviors including the coupling capabilities, dynamic range limitations, beam profile, and induced artefacts of the guided FMCW reflectometry system. The feasibility of a simplified guided terahertz FMCW reflectometry probing unit is proven. The integration of a solid immersion lens at the end of the waveguide is also demonstrated for imaging resolution improvement.

Low Cost and Compact FMCW 24 GHz Radar Applications for Snowpack and Ice Thickness Measurements.

Monitoring the evolution of snow on the ground and lake ice—two of the most important components of the changing northern environment—is essential. In this paper, we describe a lightweight, compact and autonomous 24 GHz frequency-modulated continuous-wave (FMCW) radar system for freshwater ice thickness and snow mass (snow water equivalent, SWE) measurements. Although FMCW radars have a long-established history, the novelty of this research lies in that we take advantage the availability of a new generation of low cost and low power requirement units that facilitates the monitoring of snow and ice at remote locations. Test performance (accuracy and limitations) is presented for five different applications, all using an automatic operating mode with improved signal processing: (1) In situ lake ice thickness measurements giving 2 cm accuracy up to ≈1 m ice thickness and a radar resolution of 4 cm; (2) remotely piloted aircraft-based lake ice thickness from low-altitude flight at 5 m; (3) in situ dry SWE measurements based on known snow depth, giving 13% accuracy (RMSE 20%) over boreal forest, subarctic taiga and Arctic tundra, with a measurement capability of up to 3 m in snowpack thickness; (4) continuous monitoring of surface snow density under particular Antarctic conditions; (5) continuous SWE monitoring through the winter with a synchronized and collocated snow depth sensor (ultrasonic or LiDAR sensor), giving 13.5% bias and 25 mm root mean square difference (RMSD) (10%) for dry snow. The need for detection processing for wet snow, which strongly absorbs radar signals, is discussed. An appendix provides 24 GHz simulated effective refractive index and penetration depth as a function of a wide range of density, temperature and wetness for ice and snow.

Low-Complexity-Based RD-MUSIC with Extrapolation for Joint TOA and DOA at Automotive FMCW Radar Systems

Low-complexity-based reduced-dimension–multiple-signal classification (RD-MUSIC) is proposed with extrapolation for joint time delay of arrivals (TOA) and direction of arrivals (DOA) at automotive frequency-modulated continuous-wave (FMCW) radar systems. When a vehicle is driving on the road, the automotive FMCW radar can estimate the position of multiple other vehicles, because it can estimate multiple parameters, such as TOA and DOA. Over time, the requirement of the accuracy and resolution parameters of automotive FMCW radar is increasing. To accurately estimate the parameters of multiple vehicles, such as range and angle, it is difficult to use a low-resolution algorithm, such as the two-dimensional fast Fourier transform. To improve parameter estimation performance, high-resolution algorithms, such as the 2D-MUSIC, are required. However, the conventional high-resolution methods have a high complexity and, thus, are not applicable to a real-time radar system for a vehicle. Therefore, in this work, a low-complexity RD-MUSIC with extrapolation algorithm is proposed to have a resolution similar to that of a high-resolution algorithm to estimate the position of other vehicles. Compared with conventional low complexity high resolution, in experimental results, the proposed method had better performance.

Improved DOA estimation method by distinction of different transmit signals in automotive MIMO frequency‐modulated continuous wave radar systems

This study proposes an advanced direction-of-arrival (DOA) estimation method for automotive multiple-input multiple-output (MIMO) fast-ramp radar systems. To distinguish the transmitted signal at the receiving antenna, the orthogonal code can be applied to each transmitted signal. In this case, to correctly estimate the angles of the targets, the authors also have to match the distinguished signals to each transmit antenna element. In this study, they use the space-time block code to distinguish the transmitted signals from the receiving antennas. Furthermore, by applying the deterministic maximum likelihood algorithm to each of the distinguished signals, they can find the true antenna array for DOA estimation and achieve higher estimation accuracy than false antenna array cases. Furthermore, the maximum detectable velocity is doubled compared to the conventional decoding method. In the simulation results, the proposed method provides a three degree smaller root-mean-squared error compared to the case when it is not applied. The proposed method also estimates the DOAs of the targets well at low signal-to-noise ratios. In addition, the effectiveness of the proposed method was verified by measurement results.

On the Use of Lateral Wave for the Interlayer Debonding Detecting in an Asphalt Airport Pavement Using a Multistatic GPR System

In this article, we focus on the detection of the interlayer debonding of the asphalt airport pavement by the ground-penetrating radar (GPR) system. Since the interlayer debonding usually occurs in the shallow region of the asphalt airport pavement (several centimeters), it is difficult to interpret the anomalies or the defects from the GPR signals composed of many waves under the boundary conditions. Moreover, the wavelength of the ordinary GPR system is over several centimeters. Therefore, the spatial resolution of the system is not accurate enough to consider the millimeter thickness of the debonding layer. To overcome these problems, we propose a new method based on evaluating the lateral wave behavior of common midpoint (CMP) gathers collected by a multiple static GPR system. The multistatic GPR system is a stepped frequency continuous wave (SFCW) radar system, which consists of eight transmitting and eight receiving bowtie antennas. The system operates in the frequency range from 50 MHz to 1.5 GHz. After the validation of the simulation, the results of the interlayer debonding detection were evaluated by a field experiment obtained at Tokyo International Airport. The proposed method can detect the debonding layers which are less than 1 mm. Also, it is shown that our proposed method has a high consistency with the conventional acoustic finding method in the field measurement. It provides an innovative and effective method for the interlayer debonding detection of a partially damaged airport asphalt pavement, which is difficult to be observed by the ordinary GPR signals.

Real-Time Radar-Based Gesture Detection and Recognition Built in an Edge-Computing Platform

In this paper, a real-time signal processing frame-work based on a 60 GHz frequency-modulated continuous wave (FMCW) radar system to recognize gestures is proposed. In order to improve the robustness of the radar-based gesture recognition system, the proposed framework extracts a comprehensive hand profile, including range, Doppler, azimuth and elevation, over multiple measurement-cycles and encodes them into a feature cube. Rather than feeding the range-Doppler spectrum sequence into a deep convolutional neural network (CNN) connected with recurrent neural networks, the proposed framework takes the aforementioned feature cube as input of a shallow CNN for gesture recognition to reduce the computational complexity. In addition, we develop a hand activity detection (HAD) algorithm to automatize the detection of gestures in real-time case. The proposed HAD can capture the time-stamp at which a gesture finishes and feeds the hand profile of all the relevant measurement-cycles before this time-stamp into the CNN with low latency. Since the proposed framework is able to detect and classify gestures at limited computational cost, it could be deployed in an edge-computing platform for real-time applications, whose performance is notedly inferior to a state-of-the-art personal computer. The experimental results show that the proposed framework has the capability of classifying 12 gestures in real-time with a high F1-score.

Review of Self-Injection-Locked Radar Systems for Noncontact Detection of Vital Signs

In recent decades, wireless healthcare detection has been demonstrated to be an efficient method for monitoring the health condition of targets; it has involved the detection of motion and vital sign signals with minimal discomfort. Self-injection-locked (SIL) radar systems exhibit several useful advantages over conventional continuous-wave (CW) radar systems in healthcare applications, including a low system complexity, high sensitivity, and high clutter immunity. Several approaches to detecting vital sign signals and locations using SIL radar systems have been proposed. This paper summarizes developments in vital sign detection and localization. Finally, some results of detecting animals using SIL radar systems are presented.

Improved Contactless Heartbeat Estimation in FMCW Radar via Kalman Filter Tracking

Radar systems enable a contactless, noninvasive method to simultaneously monitor the respiration and heart rate of a human target. To enhance the stability and accuracy of the detected heart rate, this letter introduces a Kalman filter-based tracking of the heartbeat signal. After providing a first rough estimation, the bandwidth of the applied band-pass filter is successively narrowed down, and the filter limits are steadily updated with respect to the current heart rate of the target. Measurement segments with random body movements are automatically identified and consequentially ignored for the Kalman filter update. The functionality of the proposed algorithm is proven by various verification measurements using a 60-GHz frequency-modulated continuous-wave radar system. The determined heart rates are compared to the output of a commercial chest belt. Measurement data with common breathing, as well as during breath-holding, were recorded for 14 different subjects.

Machine Learning-Based Estimation for Tilted Mounting Angle of Automotive Radar Sensor

Generally, automotive radars are installed behind bumpers, perpendicular to the ground. After mounting the radar, the mounting angle is often distorted due to external shock during driving. If the radar is tilted toward the ground or sky, its detection performance can be severely degraded, and this can pose a serious threat to the safety of drivers. Therefore, it is important that the radars should be installed at an appropriate angle. This paper proposes an effective method for estimating the tilted mounting angle of the radar in an automotive frequency modulated continuous wave radar system. First, we identify that the frequency spectrum of the received radar signal varies significantly depending on the radar tilt angle. Then, we extract features representing the statistical properties of the received radar signal and use them as criteria for classifying various signals with different tilt angles. In addition, we use the principal component analysis algorithm to reduce the dimensionality of the feature space and increase the execution speed. The classification results using the $k$ -nearest neighbor algorithm as a classifier demonstrate that our proposed method can estimate the tilt angle of the radar with an accuracy greater than 90%.