Portable Microwave Radar Systems for Short-Range Localization and Life Tracking: A Review

Incorporation of Digital Modulation into Vital Sign Detection and Gesture Recognition Using Multimode Radar Systems

The incorporation of digital modulation into radar systems poses various challenges in the field of radar design, but it also offers a potential solution to the shrinking availability of low-noise operating environments as the number of radar applications increases. Additionally, digital systems have reached a point where available components and technology can support higher speeds than ever before. These advancements present new avenues for radar design, in which digitally controlled phase-modulated continuous wave (PMCW) radar systems can look to support multiple collocated radar systems with low radar-radar interference. This paper proposes a reconfigurable PMCW radar for use in vital sign detection and gesture recognition while utilizing digital carrier modulation and compares the radar responses of various modulation schemes. Binary sequences are used to introduce phase modulation to the carrier wave by use of a field programable gate array (FPGA), allowing for flexibility in the modulation speed and binary sequence. Experimental results from the radar demonstrate the differences between CW and PMCW modes when measuring the respiration rate of a human subject and in gesture detection.

Multi-Layer Beam Scanning Leaky Wave Antenna for Remote Vital Signs Detection at 60 GHz

A multi-layer beam-scanning leaky wave antenna (LWA) for remote vital sign monitoring (RVSM) at 60 GHz using a single-tone continuous-wave (CW) Doppler radar has been developed in a typical dynamic environment. The antenna's components are: a partially reflecting surface (PRS), high-impedance surfaces (HISs), and a plain dielectric slab. A dipole antenna works as a source together with these elements to produce a gain of 24 dBi, a frequency beam scanning range of 30°, and precise remote vital sign monitoring (RVSM) up to 4 m across the operating frequency range (58-66 GHz). The antenna requirements for the DR are summarised in a typical dynamic scenario where a patient is to have continuous monitoring remotely, while sleeping. During the continuous health monitoring process, the patient has the freedom to move up to one meter away from the fixed sensor position.The proposed multi-layer LWA system was placed at a distance of 2 m and 4 m from the test subject to confirm the suitability of the developed antenna for dynamic RVSM applications. A proper setting of the operating frequency range (58 to 66 GHz) enabled the detection of both heart beats and respiration rates of the subject within a 30° angular range.

Time-domain convolution model for studying oscillation dynamics in an injection-locked optoelectronic oscillator

A time-domain convolution model is proposed to study the oscillation dynamics in the injection-locked optoelectronic oscillator (OEO). The model has the ability to calculate multiple characteristics of the oscillation signal, such as the spectrum and the phase noise. Based on the model, the injection locking, the frequency pulling and the asymmetrical spectrum generation phenomena are numerically simulated in success. The simulation results fit in with the experimental results, indicating that the proposed model accurately describes the oscillation dynamics in the injection-locked OEO. In addition, the building-up process of the oscillation signal in the OEO is simulated. Alternating appearance of the sidebands on both sides of the primary oscillation mode is observed for the first time in the asymmetrical spectrum generation. This model is a powerful tool to study the oscillation dynamics in the injection-locked OEO.

Verification of the System for Ship Position Keeping Equipped with a Set of Anchors in Unity3d

Modern computers with specialised software are able to simulate oceans with waves and sea currents, and the action of wind, gravity, ships and other vehicles. The high-level programming languages that are used in this type of software can read information from navigation devices connected to the computer (e.g., via serial ports), and proceed to use the raw data in control algorithms. More and more desktop software and simulators can use data from additional electronic devices such as pressure sensors, temperature sensors, etc. Thus, it is possible to conduct real-time communication with a PLC (programmable logic controller) and use it in simulators. In this article, a user interface designed in Unity3d is presented. The user interface was able to read data from navigation devices, which were used in a ship positioning control algorithm. Verification of the algorithm occurred during research on a real ship, which used an anchor-based positioning system. Using data obtained on the real ship, a mathematical model of anchor winches was developed. Next, the mathematical model was implemented in the simulator developed in Unity3d. The simulator contained the same environmental conditions as during the research on the real ship. The mathematical model of anchor winches and implementation developed in the simulator will allow for future research on anchor-based positioning systems (e.g., in different environmental conditions). The research resulted in a shift of the ship's position by 26.3 m under 280 degrees. The difference in arrival time to the target point between the real ship and the virtual ship was 19%, and the difference in position deviation was 330%.

Road Infrastructure Challenges Faced by Automated Driving: A Review

Automated driving can no longer be referred to as hype or science fiction but rather a technology that has been gradually introduced to the market. The recent activities of regulatory bodies and the market penetration of automated driving systems (ADS) demonstrate that society is exhibiting increasing interest in this field and gradually accepting new methods of transport. Automated driving, however, does not depend solely on the advances of onboard sensor technology or artificial intelligence (AI). One of the essential factors in achieving trust and safety in automated driving is road infrastructure, which requires careful consideration. Historically, the development of road infrastructure has been guided by human perception, but today we are at a turning point at which this perspective is not sufficient. In this study, we review the limitations and advances made in the state of the art of automated driving technology with respect to road infrastructure in order to identify gaps that are essential for bridging the transition from human control to self-driving. The main findings of this study are grouped into the following five clusters, characterised according to challenges that must be faced in order to cope with future mobility: international harmonisation of traffic signs and road markings, revision of the maintenance of the road infrastructure, review of common design patterns, digitalisation of road networks, and interdisciplinarity. The main contribution of this study is the provision of a clear and concise overview of the interaction between road infrastructure and ADS as well as the support of international activities to define the requirements of road infrastructure for the successful deployment of ADS.

Educational Low-Cost C-Band FMCW Radar System Comprising Commercial Off-the-Shelf Components for Indoor Through-Wall Object Detection

This paper presents an educational low-cost C-band frequency-modulated continuous wave (FMCW) radar system for use in indoor through-wall metal detection. Indoor remote-sensing applications, such as through-wall detection and positioning, are essential for the comprehensive realization of the internet of things or super-connected societies. The proposed system comprises a two-stage radio-frequency power amplifier, a voltage-controlled oscillator, circuits for frequency modulation and system synchronization, a mixer, a 3-dB power divider, a low-noise amplifier, and two cylindrical horn antennas (Tx/Rx antennas). The antenna yields gain values in the 6.8~7.8 range when operating in the 5.83~5.94 GHz frequency band. The backscattered Tx signal is sampled at 4.5 kHz using the Arduino UNO analog-to-digital converter. Thereafter, the sampled signal is transferred to the MATLAB platform and analyzed using a customized FMCW radar algorithm. The proposed system is built using commercial off-the-shelf components, and it can detect targets within a 56.3 m radius in indoor environments. In this study, the system could successfully detect targets through a 4 cm-thick ply board with a measurement accuracy of less than 10 cm.

Modeling an actively mode-locked optoelectronic oscillator based on electric amplitude modulation

A theoretical model and its calculation method are proposed to simulate an actively mode-locked optoelectronic oscillator (OEO) based on electric amplitude modulation. The model includes electric amplitude modulation to achieve mode locking and convolution of electric signal and filter impulse response function to achieve mode selection. Numerical simulation is carried out through enhancing the calculating time window to an integral multiple of the roundtrip time and employing pulse tracking method with a precise delay. Through using this model, the waveform, the spectrum and the phase noise characteristic of the generated microwave pulse train from an actively mode-locked OEO are numerically simulated, where the simulation results fit in with the experimental results. This model can be used to design an actively mode-locked OEO based on electric amplitude modulation. More importantly, it is favorable for studying the dynamic process in an actively mode-locked OEO, which is difficult to grasp by carrying out an experiment.

Parametric Electromagnetic Analysis of Radar-Based Advanced Driver Assistant Systems

Efficient and optimal design of radar-based Advanced Driver Assistant Systems (ADAS) needs the evaluation of many different electromagnetic solutions for evaluating the impact of the radome on the electromagnetic wave propagation. Because of the very high frequency at which these devices operate, with the associated extremely small wavelength, very fine meshes are needed to accurately discretize the electromagnetic equations. Thus, the computational cost of each numerical solution for a given choice of the design or operation parameters, is high (CPU time consuming and needing significant computational resources) compromising the efficiency of standard optimization algorithms. In order to alleviate the just referred difficulties the present paper proposes an approach based on the use of reduced order modeling, in particular the construction of a parametric solution by employing a non-intrusive formulation of the Proper Generalized Decomposition, combined with a powerful phase-angle unwrapping strategy for accurately addressing the electric and magnetic fields interpolation, contributing to improve the design, the calibration and the operational use of those systems.

Method for Remote Determination of Object Coordinates in Space Based on Exact Analytical Solution of Hyperbolic Equations

Accurate remote determination of the object coordinates in 3D space is one of the main questions in many applications. In one of the most popular methods, such determination of the location of an object uses the measurement by receiving an electromagnetic signal transmitted by several spatially distributed base stations (BS). The main problem is that it is necessary to reduce errors and computation time. To overcome these difficulties, an analytical method for determining the position of an object based on the analysis of time difference of arrival (TDoA) of signals from the transmitter of the object to the receivers of the BS is proposed. One of the main advantages of this method is that it is possible to eliminate the ambiguity in determining the coordinates of the object in space and to increase the accuracy of determining the coordinates when the TDoA measurement between base stations fluctuates. Applications for autonomous automotive vehicles and spacebased positioning systems are analyzed. The results obtained show that the proposed algorithm has an accuracy of determining coordinates several times higher than the method of linearization of hyperbolic equations and is less sensitive to TDoA fluctuations at base stations.

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.

Foreign Object Intrusion Detection on Metro Track Using Commodity WiFi Devices with the Fast Phase Calibration Algorithm

With continuous development in the scales of cities, the role of the metro in urban transportation is becoming more and more important. When running at a high speed, the safety of the train in the tunnel is significantly affected by any foreign objects. To address this problem, we propose a foreign object intrusion detection method based on WiFi technology, which uses radio frequency (RF) signals to sense environmental changes and is suitable for lightless tunnel environments. Firstly, based on extensive experiments, the abnormal phase offset between the RF chains of the WiFi network card and its offset law was observed. Based on this observation, a fast phase calibration method is proposed. This method only needs the azimuth information between the transmitter and the receiver to calibrate the the phase offset rapidly through the compensation of the channel state information (CSI) data. The time complexity of the algorithm is lower than the existing algorithm. Secondly, a method combining the MUSIC algorithm and static clutter suppression is proposed. This method utilizes the incoherence of the dynamic reflection signal to improve the efficiency of foreign object detection and localization in the tunnel with a strong multipath effect. Finally, experiments were conducted using Intel 5300 NIC in the indoor environment that was close to the tunnel environment. The performance of the detection probability and localization accuracy of the proposed method is tested.

An Accurate Method to Distinguish Between Stationary Human and Dog targets Under Through-Wall Condition Using UWB Radar

Research work on distinguishing humans from animals can help provide priority orders and optimize the distribution of resources in earthquake- or mining-related rescue missions. However, the existing solutions are few and their stability and accuracy of classification are less. This study proposes an accurate method for distinguishing stationary human targets from dog targets under through-wall condition based on ultra-wideband (UWB) radar. Eight humans and five beagles were used to collect 130 samples of through-wall signals using the UWB radar. Twelve corresponding features belonging to four categories were combined using the support vector machine (SVM) method. A recursive feature elimination (RFE) method determined an optimal feature subset from the twelve features to overcome overfitting and poor generalization. The results after ten-fold cross-validation showed that the area under the receiver operator characteristic (ROC) curve can reach 0.9993, which indicates that the two subjects can be distinguished under through-wall condition. The study also compared the ability of the proposed features of four categories when used independently in a classifier. Comparison results indicated that wavelet entropy-corresponding features among them have the best performance. The method and results are envisioned to be applied in various practical situations, such as post-disaster searching, hostage rescues, and intelligent homecare.

A Survey of Deep Learning-Based Human Activity Recognition in Radar

Radar, as one of the sensors for human activity recognition (HAR), has unique characteristics such as privacy protection and contactless sensing. Radar-based HAR has been applied in many fields such as human–computer interaction, smart surveillance and health assessment. Conventional machine learning approaches rely on heuristic hand-crafted feature extraction, and their generalization capability is limited. Additionally, extracting features manually is time–consuming and inefficient. Deep learning acts as a hierarchical approach to learn high-level features automatically and has achieved superior performance for HAR. This paper surveys deep learning based HAR in radar from three aspects: deep learning techniques, radar systems, and deep learning for radar-based HAR. Especially, we elaborate deep learning approaches designed for activity recognition in radar according to the dimension of radar returns (i.e., 1D, 2D and 3D echoes). Due to the difference of echo forms, corresponding deep learning approaches are different to fully exploit motion information. Experimental results have demonstrated the feasibility of applying deep learning for radar-based HAR in 1D, 2D and 3D echoes. Finally, we address some current research considerations and future opportunities.