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This article introduces a comprehensive Mode-S radar simulation, designed to evaluate interrogation and reply signal functionality in various uplink formats (UFs) and downlink formats (DFs). Mode-S radar, crucial in modern aviation, facilitates communication between aircraft and ground systems, thereby enhancing aircraft tracking, situational awareness, and air traffic safety. Our simulation focuses on optimizing interrogation and reply sequences in the 1030- and 1090-MHz channels, respectively. It provides a practical solution for testing complex air flight scenarios, which may be impractical or costly in real life, by replicating challenging conditions in a computerized environment. This approach aids in developing advanced radar signaling algorithms for air traffic control. The simulator’s user-friendly interface displays real-time aircraft data, demonstrating its realworld applicability. Its successful operation underscores its potential to advance Mode-S radar technology and its value for research in the field. [ABSTRACT FROM AUTHOR]

Radio sensing is a key envisioned feature for next-generation wireless systems, allowing the use of communication infrastructure and air interface components to also perceive the environment. Enabling efficient use of radio access network (RAN) resources to meet various communication and sensing use case requirements is a primary goal in such systems. This article bridges the gap among RAN architecture requirements, along with radio and signal processing techniques to realize the integration of radar sensing into next-generation cellular systems, e.g., the open RAN (O-RAN) framework. Motivated by the O-RAN disaggregated architecture, this article introduces a framework to analyze sensing processing from the perspective of the radio unit (RU)–distributed unit (DU) functional split and the fronthaul throughput requirements of interfaces between RUs and DUs. Accordingly, the feasibility of adopting sensing in current architectures while meeting fronthaul throughput limitations is demonstrated. Further, it is shown that receiver (Rx) processing techniques tailored for cellular system (e.g., to enable wideband sensing over disjoint bandwidths) can efficiently fit into the DU processing framework. Evaluation results revealing detection performance gains are also presented. [ABSTRACT FROM AUTHOR]

Fast settling phase locked loops (PLLs) play a pivotal role in many applications requiring rapid attainment of a stable frequency and phase. In modern communication standards, these PLLs are extensively utilized to guarantee precise compliance with dynamic resource allocation requirements. In processors, these PLLs manage dynamic voltage frequency scaling. Moreover, the fast-settling PLLs expedite the scanning of frequency spectra in sophisticated electronic radar set-ups, proving particularly advantageous for imaging and scanning radar applications. The rapid response exhibited by these PLLs is also harnessed in quantum technologies, catering to the urgent need for precise frequency adjustments to manipulate qubit states effectively. The strategies employed to attain fast-settling PLLs are primarily classified into five broad techniques in this article: enhanced phase frequency detection, hybrid multiple subsystems, VCO start-up, gear shift, and look-up table or finite state machine. This article explores the fundamental operational principles encompassing these techniques and presents optimal settling times for each method reported in the literature. Finally, the architectures utilizing these techniques will be evaluated based on their figure of merit (FoM), settling time, and tuning range. [ABSTRACT FROM AUTHOR]

With the increased carrier frequencies envisioned for future radar and communication systems, transmissions in the near-field propagation region have raised an increased interest. In order to evaluate the performance of such systems, accurate propagation models are required to predict efficiently the received powers at each node, and draw correct conclusions. In this paper, we develop new radar propagation models based on ElectroMagnetic (EM) theory, helping to clarify in which cases popular models from the literature, namely the radar equation and the Geometrical Optics (GO) approximation, are valid. The target is modelled as a flat square or curved rectangular plate, enabling to compute analytically the scattered EM fields, and evaluate accurate Radar Cross-Sections (RCS). With a flat plate, the novel model directly highlights the link between the radar equation and the GO approximation, used e.g. in ray-tracing applications. Radar cross-section measurements are then performed in order to validate the models with multiple target geometries, e.g. square plates, sphere and cylinder. Finally, the impact of such modelling is evaluated by considering popular automotive scenarios, within the stochastic geometry framework.

In China's Tianwen-2 mission, a dual-frequency radar will be carried out to detect the asteroid 2016 HO3. The dual-frequency radar can acquire the information of the asteroid's surface and internal structure. There are few studies about the radar detection of the asteroid 2016 HO3 and it is unclear what information can be acquired from the radar images. In this article, the radar images of the asteroid under nadir and oblique observations are simulated with the two-scale model of the Kirchhoff approximation/small-perturbation method. The radar images at 900 and 150 MHz are simulated to study the backscattering from the asteroid's surface and the detectability of the internal structure in the asteroid. The influences of the asteroid characteristics, such as the shape, surface roughness, internally embedded object, and the loss tangent of the media, on the radar images and the detectability of internal structure are discussed under nadir and oblique observations. The simulations provide a reference for future data analysis in the Tianwen-2 mission.

Current methods for fall risk assessment rely on Quantitative Gait Analysis (QGA) using costly optical tracking systems, which are often only available at specialized laboratories that may not be easily accessible to rural communities. Radar placed in a home or assisted living facility can acquire continuous ambulatory recordings over extended durations of a subject's natural gait and activity. Thus, radar-based QGA has the potential to capture day-to-day variations in gait, is time efficient and removes the burden for the subject to come to a clinic, providing a more realistic picture of older adults’ mobility. Although there has been research on gait-related health monitoring, most of this work focuses on classification-based methods, while only a few consider gait parameter estimation. On the one hand, metrics that are accurately and easily computable from radar data have not been demonstrated to have an established correlation with fall risk or other medical conditions; on the other hand, the accuracy of radar-based estimates of gait parameters that are well-accepted by the medical community as indicators of fall risk have not been adequately validated. This paper provides an overview of emerging radar-based techniques for gait parameter estimation, especially with emphasis on those relevant to fall risk. A pilot study that compares the accuracy of estimating gait parameters from different radar data representations – in particular, the micro-Doppler signature and skeletal point estimates – is conducted based on validation against an 8-camera, marker-based optical tracking system. The results of pilot study are discussed to assess the current state-of-the-art in radar-based QGA and potential directions for future research that can improve radar-based gait parameter estimation accuracy.

Radio detection and ranging-based (radar) sensing offers unique opportunities for biomedical monitoring and can help overcome the limitations of currently established solutions. Due to its contactless and unobtrusive measurement principle, it can facilitate the longitudinal recording of human physiology and can help to bridge the gap from laboratory to real-world assessments. However, radar sensors typically yield complex and multidimensional data that are hard to interpret without domain expertise. Machine learning (ML) algorithms can be trained to extract meaningful information from radar data for medical experts, enhancing not only diagnostic capabilities but also contributing to advancements in disease prevention and treatment. However, until now, the two aspects of radar-based data acquisition and ML-based data processing have mostly been addressed individually and not as part of a holistic and end-to-end data analysis pipeline. For this reason, we present a tutorial on radar-based ML applications for biomedical monitoring that equally emphasizes both dimensions. We highlight the fundamentals of radar and ML theory, data acquisition and representation and outline categories of clinical relevance. Since the contactless and unobtrusive nature of radar-based sensing also raises novel ethical concerns regarding biomedical monitoring, we additionally present a discussion that carefully addresses the ethical aspects of this novel technology, particularly regarding data privacy, ownership, and potential biases in ML algorithms.

Monopulse methods have been successfully used in radar to determine the direction of arrival of a reflected signal, giving good results when a single isolated target is involved. However, when two nearby targets need to be detected and their angular position determined, this method fails. We show that it is possible to train and use a neural network to recognize two nearby targets. This neural network is then used to determine the angle of arrival of the reflected signal with sufficient accuracy, even when the distance of the targets is less than the beamwidth. We show the results on an eight-beam antenna model by measuring the elevation using an amplitude monopulse and neural network method. While using monopulse, we can reliably discriminate and determine the angular position of two targets only when their distance is larger than one beamwidth. The neural network method works reliably even for targets at distances much smaller.

The passive and active L-band system (PALS) is an airborne microwave instrument suite that combines a separate radar and radiometer airborne instrument with a common antenna. PALS has been used for salinity, wind speed, and soil moisture (SM) measurements, which is critical for calibration and validation of the SM active passive satellite brightness temperature measurements. The combined 1.413 GHz radiometer and 1.26 GHz radar measurements can provide high-accuracy SM measurements over vegetated areas. The new upgraded version of the PALS instrument employs a novel single-chain receiver topology and combines the radar and radiometer instruments. The reduced-noise single-chain receiver PALS instrument was deployed on a DC-3 aircraft for SM measurement in the summer of 2019.

This study introduces the novel communication topology, namely RadRCom, integrating radar and relay-assisted communication systems for single antenna configuration as a proof of concept. While simultaneous radar and communication operations within the same spectrum gain momentum, our work advances this concept by incorporating relay assistance, particularly crucial in applications like vehicle-to-anything (V2X) communication. The inclusion of relays significantly enhances communication system performance, addressing challenges such as interference management between radar, relay, and communication nodes. This topology attracts three design challenges such as optimal radar waveform, relay parameters and communication system parameters. However, the key bottlenecks are the interference from radar to the relay and communication receiver and similarly the one from communication transmitter and relay node to the radar. Therefore, the work addresses these challenges simultaneously meeting the quality of service. Our proposed RadRCom system optimizes radar waveform and relay parameters to improve signal-to-interference noise ratio (SINR) at the radar and mean square error (MSE) of data transmission. We introduce two frameworks for parameter design, i.e, radar-centric and relay-centric ones. We take a sub-optimal iterative approach to address the computational complexity. Numerical simulations are performed to evaluate the performances of the proposed RadRCom system with the proposed algorithms.

As an increasing number of people suffer from sleep disorders, such as insomnia or sleep apnea, sleep monitoring and management using consumer devices have gained increasing attention from research communities. As sleep quality is closely related to sleep structure based on hypnograms, the classification of sleep stages over the course of the night is important for accurate sleep monitoring. We present sleep stage classification using a smartphone equipped with ultra-wideband (UWB) radar. We focused on the development of easily accessible sleep monitoring system for the general population by placing the smartphone on a table near a bed, which is commonly used during sleep. We collected 509 nights of UWB radar and nocturnal in-laboratory polysomnography (PSG) data from various participants, including patients with apnea, using a customized Samsung Galaxy smartphone with a UWB radar chip placed on a table near the bed. A combination of 1D convolutional neural network and transformer architecture was proposed in this study, and a domain adaptation technique was applied to train the model with both large-scale respiratory signals from open database PSGs and UWB radar data to boost the performance by overcoming the lack of UWB radar data. With 5-fold validation, an epoch-by-epoch comparison between the predicted and expert-annotated four sleep stages (Wake, REM sleep, light sleep, and deep sleep) resulted in 0.76 of accuracy and 0.64 of Cohen’s kappa. This study demonstrated that sleep stages can be monitored with substantial accuracy by simply placing a smartphone on a bedtable, making it highly usable and reliable in real use cases.

In recent years, the Poisson multi-Bernoulli mixture (PMBM) filter has been established among the state-of-the art methods in target tracking. We present a method for including target-provided measurements in said filter, both when using it to track extended objects and point targets. We use messages from the Automatic identification system as an example of target-provided measurements, and radar and LiDAR as examples of exteroceptive sensors. In the point target case, we utilize several different kinematic models in parallel through the interacting multiple models framework, and compare the presented method to several common trackers and other PMBM filter configurations. The results show that our method outperforms similar methods when target-provided measurements are available. The point target variant is also shown to work in a closed-loop collision avoidance experiment in a maritime environment, demonstrating its feasibility for use in real-world applications. For the extended object tracking case, we expand upon the Gaussian process PMBM filter. The extended object method is evaluated on both simulated and experimental data, and is shown to improve the tracking performance when including target-provided measurements in comparison to when it only uses exteroceptive measurements.

For the problem of power allocation between a distributed multistatic radar network and a smart jammer, the application of non-cooperative game theory is employed to address the issue in this paper. Consequently, three scenarios of power allocation games are examined. The first two game scenarios, characterized by information asymmetry, are categorized under the Stackelberg game framework, while the final scenario, with information symmetry, is classified as a non-cooperative game. Through the power allocation analyses of the three game scenarios, it is observed that both the radar system and the jammer possess a first-mover advantage. Additionally, the existence and uniqueness of the Nash equilibrium in the games are demonstrated. Based on the best response strategies within the games, three corresponding power allocation game algorithms are proposed. Ultimately, the convergence and performance comparison of the three power allocation game algorithms are validated through simulation experiments.

This paper develops a novel stochastic geometry model to investigate design tradeoffs in a large-scale radar-aided millimeter-wave cellular network to eliminate the beam training overhead. In the proposed system, each base station comprises two sub-systems: the sensing sub-system, where radar localizes users, and the communication sub-system, where directional antennas serve the detected users. Both sub-systems operate simultaneously to eliminate the beam training overhead and reduce misalignment errors. The system is modeled under realistic fading conditions with scatterers and interferers in the environment. System parameters, such as the density of mobile users and undesired clutter, radar cross-section fluctuations, radar search duration, antenna directivity, and bandwidth are considered in the analytical model. Two scenarios are studied; the first considers antenna misalignment for the communication sub-system, where error magnitude depends on the radar sub-system accuracy. The other scenario considers perfect alignment for benchmarking. It is demonstrated that the radar search duration, which controls the radar antenna beamwidth, is a crucial parameter for optimizing the system throughput. Moreover, the beamwidth of the communication antenna exhibits a tradeoff in which a narrow beam yields higher gain and reduces interference but increases the misalignment error impact. The results demonstrate that if the beamwidths of both sub-systems are carefully chosen, the proposed system can eradicate the beam training overhead without compromising the system’s performance, in addition to significantly enhancing the average total system throughput.

In this paper, we propose an S/X-band shared-aperture array antenna with a mutual complementary design to improve the electromagnetic (EM) transparent characteristics. A unit-cell of the proposed antenna includes one dual-loop element for the S-band and $3\times 3$ dual-loop elements for the X-band. To configure the shared-aperture structure in a limited space, the S-band element is stacked on top of the X-band elements. To solve the practical engineering problems of the shared-aperture antennas, novel design techniques such as using a mutual complementary structure, a coupling compensation array, an interface layer, and an antenna modularization are employed. To verify the antenna feasibility, the fabricated unit-cell extends into a $4\times 4$ unit-cell array. The fractional bandwidth of the reflection coefficients for the proposed array are 14.7% and 15% in the S- and X-bands, respectively. In the S-band, as the steering direction of the main beam increases from 0° to 45°, the maximum gain decreases from 14.6 dBi to 11.8 dBi. In the X-band under the same conditions, the maximum gain varies from 26.6 dBi to 25.3 dBi.

This article aimed to monitor vegetation using C-band radar data at a subdaily time step. To this end, radar measurements using tower-mounted antennas with a 15-min time step, along with physiology-related information (sapflow and micrometric dendrometry), were acquired quasi-continuously from March 2020 to December 2021 in an olive orchard located near Marrakech, Morocco. The article focused on temporal coherence, whose clear diurnal cycle (highest at night and lowest at the end of the afternoon) had been highlighted over tropical and boreal forests in previous studies. The results showed that coherence was highly sensitive to: wind-induced movement of scatterers, since coherence was lowest when wind speed was highest in late afternoon, and vegetation activity, especially its water dynamics, since the morning coherence drop coincided with the onset of sapflow and the daily evapotranspiration cycle, as well as the good agreement between the temporal drop rate of coherence and the daily residual variation in trunk circumference (i.e., deviation from long-term trend). Finally, coherence remained high for temporal baselines of several days, showing that sentinel-1 data (when both satellites are operational) may be well suited for such studies, especially with acquisitions made during morning passes, when wind speed is low. These results open perspectives for monitoring tree crop physiology using high-revisit-time radar observations.

Incoherent backscattering of mm-waves from natural rough surfaces is considered. A novel method is proposed to determine the statistical properties of surface scattering from range profile measurements. The method is based on modeling the road surface as a grid of uncorrelated scattering elements, described by normalized scattering matrices. Using this model, expressions are derived to estimate the mean value and covariance matrix of surface scattering from measurement data. This procedure is then applied to measurement data of four road surface types, namely: 1) dry asphalt, 2) wet asphalt, 3) asphalt covered by basalt gravel, and 4) old asphalt. Using the derived statistical normalized radar cross-section models, two novel applications are proposed. First, a procedure for synthesizing/simulating surface clutter is proposed. This procedure is subsequently used to simulate received power from surfaces comprising patches of one or multiple road surface conditions. Excellent agreement between simulation and measurement results is demonstrated. Second, a method for determining the optimal polarization of the electromagnetic sensing waves used in a single-polarized radar system is proposed. This method is based on factorizing the antenna polarization vector into two bounded parameters, allowing for numerical evaluation of the minima and maxima for targets with a specified scattering matrix. This method is further extended to work with statistical descriptions of scattering matrices by means of Monte Carlo simulations.

A promising waveform candidate for future joint sensing and communication systems is orthogonal frequency-division multiplexing (OFDM). For such systems, supporting multiple transmit antennas requires multiplexing methods for the generation of orthogonal transmit signals, where equidistant subcarrier interleaving (ESI) is the most popular multiplexing method. In this work, we analyze a multiplexing method called Doppler-division multiplexing (DDM). This method applies a phase shift from OFDM symbol to OFDM symbol to separate signals transmitted by different Tx antennas along the velocity axis of the range-Doppler map. The main focus of this work lies on the implications of DDM on the communication task. It will be shown that for DDM, the channels observed in the communication receiver are heavily time-varying, preventing any meaningful transmission of data when not taken into account. In this work, a communication system designed to combat these time-varying channels is proposed, which includes methods for data estimation, synchronization, and channel estimation. Bit error ratio (BER) simulations demonstrate the superiority of this communications system compared to ESI-based systems.

Non-contact vital sign monitoring has been an important research topic recently due to the ability to monitor patients for an extended period especially during sleep without requiring uncomfortable attachments. Radar is a popular sensor for vital sign monitoring research. Various algorithms have been proposed for estimating respiration rate and heart rate from the radar data. But many algorithms rely on Fast Fourier Transform (FFT) to convert time domain signal to the frequency domain and estimate vital signs, despite FFT having limitation of frequency resolution being inverse of the time interval of data sample. However, there are other spectral estimation algorithms, which have not been much researched into the suitability of vital sign estimation using radar signals. In this paper, we compared eight different types of spectral estimation algorithms, including FFT, for respiration rate and heart rate estimation of stationary subjects in a controlled environment. The evaluation is based on extensive data consisting of different stationary subject positions. Considering the results, the eligibility of algorithms other than FFT for respiration rate and heart rate estimation is demonstrated. Using this work, researchers can get an overview on which algorithm is suitable for their work without the need to review individual algorithms separately. [ABSTRACT FROM AUTHOR]

Current methods for fall risk assessment rely on Quantitative Gait Analysis (QGA) using costly optical tracking systems, which are often only available at specialized laboratories that may not be easily accessible to rural communities. Radar placed in a home or assisted living facility can acquire continuous ambulatory recordings over extended durations of a subject's natural gait and activity. Thus, radar-based QGA has the potential to capture day-to-day variations in gait, is time efficient and removes the burden for the subject to come to a clinic, providing a more realistic picture of older adults' mobility. Although there has been research on gait-related health monitoring, most of this work focuses on classification-based methods, while only a few consider gait parameter estimation. On the one hand, metrics that are accurately and easily computable from radar data have not been demonstrated to have an established correlation with fall risk or other medical conditions; on the other hand, the accuracy of radar-based estimates of gait parameters that are well-accepted by the medical community as indicators of fall risk have not been adequately validated. This paper provides an overview of emerging radar-based techniques for gait parameter estimation, especially with emphasis on those relevant to fall risk. A pilot study that compares the accuracy of estimating gait parameters from different radar data representations - in particular, the micro-Doppler signature and skeletal point estimates - is conducted based on validation against an 8-camera, marker-based optical tracking system. The results of pilot study are discussed to assess the current state-of-the-art in radar-based QGA and potential directions for future research that can improve radar-based gait parameter estimation accuracy., Competing Interests: The authors declare that they have no commercial or financial relationships that could be construed as a potential conflict of interest., (© 2024 The Authors.)

Radio detection and ranging-based (radar) sensing offers unique opportunities for biomedical monitoring and can help overcome the limitations of currently established solutions. Due to its contactless and unobtrusive measurement principle, it can facilitate the longitudinal recording of human physiology and can help to bridge the gap from laboratory to real-world assessments. However, radar sensors typically yield complex and multidimensional data that are hard to interpret without domain expertise. Machine learning (ML) algorithms can be trained to extract meaningful information from radar data for medical experts, enhancing not only diagnostic capabilities but also contributing to advancements in disease prevention and treatment. However, until now, the two aspects of radar-based data acquisition and ML-based data processing have mostly been addressed individually and not as part of a holistic and end-to-end data analysis pipeline. For this reason, we present a tutorial on radar-based ML applications for biomedical monitoring that equally emphasizes both dimensions. We highlight the fundamentals of radar and ML theory, data acquisition and representation and outline categories of clinical relevance. Since the contactless and unobtrusive nature of radar-based sensing also raises novel ethical concerns regarding biomedical monitoring, we additionally present a discussion that carefully addresses the ethical aspects of this novel technology, particularly regarding data privacy, ownership, and potential biases in ML algorithms., Competing Interests: The authors declare that they have no commercial or financial relationships that could be construed as a potential conflict of interest., (© 2024 The Authors.)

Human eye activity has been widely studied in many fields such as psychology, neuroscience, medicine, and human-computer interaction engineering. In previous studies, monitoring of human eye activity mainly depends on electrooculogram (EOG) that requires a contact sensor. This article proposes a novel eye movement monitoring method called continuous wave doppler oculogram (cDOG). Unlike the conventional EOG-based eye movement monitoring methods, cDOG based on continuous wave doppler radar sensor (cDRS) can remotely measure human eye activity without placing electrodes on the head. To verify the feasibility of using cDOG for eye movement monitoring, we first theoretically analyzed the association between the radar signal and the corresponding eye movements measured with EOG. Afterward, we conducted an experiment to compare EOG and cDOG measurements under the conditions of eyes closure and opening. In addition, different eye movement states were considered, including right-left saccade, up-down saccade, eye-blink, and fixation. Several representative time domain and frequency domain features obtained from cDOG and from EOG were compared in these states, allowing us to demonstrate the feasibility of using cDOG for monitoring eye movements. The experimental results show that there is a correlation between cDOG and EOG in the time and frequency domain features, the average time error of single eye movement is less than 280.5 ms, and the accuracy of cDOG in eye movement detection is higher than 92.35%, when the distance between the cDRS and the face is 10 cm and eyes is facing the radar directly.

Low-cost and portable electromagnetic (EM) stroke diagnostic systems are of great interest due to the increasing demand for early on-site detection or long-term bedside monitoring of stroke patients. Biosensor antennas serve as crucial hardware components for EM diagnostic systems. This article presents a detect capability enhanced biosensor antenna with a planar and compact configuration for portable EM stroke detection systems, overcoming the problem of limited detection capability in existing designs for this application. The proposed antenna is developed based on multiple dipoles, exhibiting multi-mode resonances and complementary interaction. In the frequency domain, the simulated and measured results with the presence of head phantoms show that this compact planar antenna achieves improved performance in both impedance bandwidth and near-field radiation inside the head tissues, which all contribute to enhancing its stroke detection capability in radar-based EM diagnosis. An array of 12 elements is numerically and experimentally tested in a lab-setting EM stroke diagnostic system to validate the detection capability of the proposed antenna. The reconstructed 2-D images inside the head demonstrate successful detection of different stroke-affected areas, even as small as 3 mm in radius, significantly smaller than those of reported relevant works under the same validation setting, confirming the enhanced detection capability of the proposed antenna.