This paper presents a contactless method for the simultaneous monitoring of heart rate (HR) and respiratory rate (RR) using a Frequency Modulated Continuous Wave (FMCW) radar operating at 122.5 GHz. To address the challenges posed by spectral overlap and interference between cardiac and respiratory components, we introduce a signal separation and estimation framework based on Maximum Likelihood Estimation (MLE) combined with an orthogonal projection technique. The approach models the thoracic displacement captured by the radar as the superposition of periodic sources and applies spectral projection to isolate the fundamental frequencies and their harmonics. Theoretical performance is assessed through the derivation of the Cramér-Rao Lower Bound (CRLB) and extensive Monte Carlo simulations under varying SNR and sample size conditions. Experimental validation was conducted on 23 healthy volunteers, each providing two 20 second recordings, using a clinical-grade reference monitor. The method achieves high estimation accuracy, with a mean absolute error of 0.69 bpm for HR and 0.33 bpm for RR. The algorithm is capable of reconstructing the cardiac pulse waveform while preserving key morphological features such as the systolic peak and dicrotic notch. Furthermore, the use of the projection technique further enhanced estimation accuracy under nonphysiological conditions, including both elevated and reduced rates. These findings demonstrate the potential of the proposed method as a reliable tool for contactless monitoring of vital signs and support its future integration into biomedical applications.

Vital information wireless sensing is an interesting alternative to conventional techniques based on contact sensors. The evolution of radar technologies enables the proposal of cost-effective and compact radar sensors designed for diverse health monitoring, diagnostics, and Internet of Medical Things (IoMT) applications. It is possible to observe respiratory rate (RR), heart rate (HR), cardiac rhythm, blood pressure waveform (BPW), and emerging biometrics from the phase of radar echo signals. However, recognition and separation of the signals of interest from other interfering motion is a challenge, taking into account the diversity of signal patterns depending on subjects, health and observation conditions. In this context, we propose a novel wireless vital sensing system with a self-designed 120 GHz Frequency-Modulated Continuous Wave (FMCW) Radar. The developed sensor, based on a Radar System-on-Chip (RSoC), uses a real-time Repetitive Waveform Adaptive Matched Filter (RWAMF) maximizing Signal-to-Interference-plus-Noise Ratio (SINR) for reliable cardiac rhythm and BPW observations. The radar design has been optimized for vital sensing with a very short wavelength providing excellent sensitivity to micro-motion and a centimetric range and transversal resolutions to reject clutter and unwanted motions. The superior performance of our solution has been validated and evaluated experimentally on different subjects and measurement conditions with our own acquired vital signal database.

Synthetic Aperture Radar (SAR) data, which can penetrate the canopy under all-weather conditions, holds great potential for crop height estimation. However, due to the rapid morphological changes in crops and their rela tively low heights, current phase-or coherence-based methods require large spatial baselines and high temporal resolution data to estimate crop height. This paper proposes a novel SAR Height Index (SHI)-based crop height estimation algorithm that primarily relies on SAR dual-pol backscattering, while incorporating a logistic growth model to improve temporal consistency. The method avoids the need for interferometric spatial baselines and complex parameterization. First, SHI is introduced based on a Radial Basis Function (RBF) kernel with pixel-level adaptive sigma calculation. Second, phenologically filtered SHI is regressed against observed crop heights to establish fixed, scene-specific empirical linear models for height estimation. Third, daily crop height maps are generated by integrating the estimated height and a logistic growth model that covers most of the growth cycle. Experiments were conducted on corn and soybean plantations in Spain using fully polarimetric ground-based SAR (GB-PolSAR) data, on a three-year winter wheat plantation in Germany, and a two-year cotton plantation in the USA, using Sentinel-1 (S1) GRD data. All results demonstrated a good correlation between SHI and the measured crop height. Daily height estimates for wheat and cotton derived from S1 showed close agreement with in situ measurements, with R-2 values ranging from 0.81 to 0.91 and RMSE values between 8.9 cm and 18.4 cm. Although the application of SHI across different climatic regions may require lightweight transfer calibration, it demonstrates greater generalizability than conventional backscattering or radar vegetation index. The SHI-based algorithm significantly enhances flexibility in data selection and provides a promising solution for region-scale and cost-effective crop height estimation.

Millimeter-wave (mmWave) radar is widely recognized as a critical tool for contactless, continuous human sensing across multiple scenarios. Yet, there is a lack of high-quality datasets with synchronized reference measurements, especially at higher frequencies, which are essential for advancing signal processing methods and improving the retrieval of vital parameters. To address this gap, we introduce a mmWave radar vital signal dataset collected with synchronized reference recordings. The dataset is derived from measurements acquired using a custom-built, noncommercial radar system developed by CommSensLab-UPC specifically for biomedical applications. The implemented Frequency-Modulated Continuous Wave (FMCW) radar system operates at 120 GHz within the industrial, scientific, and medical (ISM) band. In parallel, a monitoring system records reference physiological signals, including electrocardiograms, respiratory traces, pulse waveforms, and blood pressure values. Under a predefined protocol, a professional clinician collected data from 24 healthy subjects under two scenarios. The release of this dataset aims to facilitate the development and validation of advanced radar signal processing algorithms, thereby enhancing the contribution of radar technologies to hemodynamic monitoring and autonomic nervous system assessment.

SAR satellite missions from geostationary orbits (GEOSAR) would offer the short revisit times required in the observation of fast phenomena. Ground-based multiple baseline interferometry is a technique under study for precise orbit determination of GEO satellites, a requirement for SAR processing echo data in GEOSAR missions. This interferometric technique can be complementary to GNSS-based orbit determination which is degraded for high-altitude missions above the MEO GNSS constellations. A multichannel coherent receiver is presented with two complementary functions: interferometric orbit determination and bistatic SAR imaging using present GEO telecommunication satellites transmissions. To improve orbit determination, large baselines are formed based on urban reflectors of opportunity. The obtained orbits are the basis for synthetic aperture focusing of received echo data allowing to form images from the urban and natural areas surrounding the interferometer site. In return, the images allow to select new reflectors of opportunity that can be used to increase the number of baselines and further improve the orbits. The article describes the developed system, the synergies between both orbit and imaging functions and preliminary experimental results with interferometric and polarimetric applications.

Most crops accumulate above-ground biomass (AGB) through photosynthesis, inspiring the development of the Photosynthetic Accumulation Model (PAM) and Simplified PAM (SPAM). Both models estimate AGB based on time-series optical vegetation indices (VIs) and canopy height. To further enhance the model performance and evaluate its applicability across different crop types, an improved PAM model (IPAM) is proposed with three strategies. They are as follows: (i) using numerical integration to reduce reliance on dense observations, (ii) introduction of Fibonacci sequence-based structural correction to improve model accuracy, and (iii) non-photosynthetic area masking to reduce overestimation. Results from both soybean and cotton demonstrate the strong performance of the PAM-series models. Among them, the proposed IPAM model achieved higher accuracy, with mean R2 and RMSE values of 0.89 and 207 g/m2 for soybean and 0.84 and 251 g/m2 for cotton, respectively. Among the vegetation indices tested, the recently proposed Near-Infrared Reflectance of vegetation (NIRv) and Kernel-based normalized difference vegetation index (Kndvi) yielded the most accurate results. Both Monte Carlo simulations and theoretical error propagation analyses indicate a maximum deviation percentage of approximately 20% for both crops, which is considered acceptable given the expected inter-annual variation in model transferability. In addition, this paper discusses alternatives to height measurements and evaluates the feasibility of incorporating synthetic aperture radar (SAR) VIs, providing practical insights into the model’s adaptability across diverse data conditions.

A dataset of sub-daily C-band data, acquired with a ground-based synthetic aperture radar, has been used to study soil and vegetation dynamics during a complete growing season in a controlled agricultural test site. The data have been exploited to analyse the rate and sources of decorrelation in the scene, as well as the consequences of the observation conditions of a sub-daily satellite (with either low, medium or geosynchronous orbit): short revisit times, availability of multiple acquisitions during a single day, and shallow observations at some incidence angles. Repeat-pass coherence is found to be less affected by temporal decorrelation when the primary image is acquired during nighttime or the last hours predawn. Regarding the incidence angle, VV has increased sensitivity to certain phenological stages as the incidence angle increases. Additionally, a periodic oscillation on a sub-daily scale is observed when creating coherence time series with increasing temporal baseline. Factors which strongly contribute to these oscillations are the daily cycles of temperature, soil moisture and vegetation water dynamics.

This paper presents a new approach to measuring eyelid movement using millimeter wave (mmW) radar technology. A two-step method is proposed, involving the observation of a small resolution cell corresponding to the monitored eye and the evaluation of the phase evolution over the measurement period. Simulations are conducted to support radar system optimization and data interpretation with a focus on detecting eyelid movement patterns and compensating for interference from other parts of the body. The feasibility of using this method with eyeglasses is also explored. The proposed technique’s advantages and limitations are discussed in comparison with existing measurement alternatives. The characteristics of eyelid dynamics, including blink frequency, regularity, duration, and velocity can be used to assess neurological conditions and driver drowsiness.

Data obtained during a ground-based SAR experiment and an associated field campaign have been exploited to study the rate and sources of decorrelation in an agricultural test site in the conditions of observation of a geosynchronous SAR. It was found that the scene is less affected by temporal decorrelation when the primary image is acquired during night time or early morning. Additionally, a periodic oscillation on a sub-daily scale was observed when creating coherence time series with increasing temporal baseline. Two factors which strongly contribute to these oscillations are the daily cycles of soil moisture and evapotranspiration.

Current climate and weather changes urged the scientific community to find ways to observe and understand the relatively rapid land-atmosphere processes related to the water cycle. The HydroSoil project, funded by the European Space Agency, was proposed to experimentally assess the use of Synthetic Aperture Radar in future short revisit time missions based on Geosynchronous Orbital platforms. This article describes the HydroSoil facility providing continuous monitoring of an agricultural field with a 10-min temporal resolution and a spatial resolution in the order of 1 m2. Gathered data from the full Pol Ground-Based SAR are being used to investigate the capability of Geosynchronous SAR missions to retrieve soil moisture and to measure vegetation parameters. To achieve the project's objectives, intensive procurement of ground truth data encompassing continuous acquisition of relevant meteorological parameters such as air humidity, temperature, precipitation wind speed and direction, pressure, and solar irradiation together with other vegetation characteristics under study has been mandatory. The different densities, sizes, and water content of two different crops, barley and corn, together with the data generated, will enable the assessment of the missions’ ability to extract essential scene characteristics, such as soil moisture and crop water content, for use in future geostationary missions.

Geosynchronous synthetic aperture radar (GeoSAR) missions offer the advantage of near-continuous monitoring of specific regions on Earth, making them essential for applications that require continuous information. However, wind-induced motion along the inherent long integration time can result in image defocusing, with potential degradation of retrieved information. This article aims to investigate the impact of GeoSAR long integration time in synthetic aperture radar (SAR) imaging and derived products (time series of backscatter and coherence) required to extract agriculture-relevant soil or crop parameters of interest. The study is based on the extensive HydroSoil data acquisition campaign carried out over barley and corn crops, funded by the European Space Agency. The collected raw data are used to synthesize equivalent apertures with integration times of up to 4 h, similar to those acquired with a GeoSAR. These ultraslow apertures facilitate the assessment of the impact of agricultural scene decorrelation on the generation of images with extended integration times.

Future radar Earth observation missions, with reduced revisit times, will offer the capability to obtain valuable data applicable to agriculture and meteorology. They will contribute to a better understanding of some observed changes that exhibit time scales much shorter than present satellite revisit times. In the context of the preparatory studies in support of future radar mission proposals with reduced revisit time, such as the geosynchronous HydroTerra, the HydroSoil facility has been set up for C-band radar continuous monitoring of crops with a time resolution of 10 min and a spatial resolution of 1 m2. The data obtained for a corn crop observed along the life cycle has been analyzed to model radar backscattering dependence on crop development parameters. In this study, it is particularly noticeable that the interferometric coherence was measured over a 24-h interval, in contrast to previous studies. Coherence has shown a strong sensitivity to plant height and the phenological stage of the crop, which can be exploited to sense crop growth with radar measurements. However, due to the high density and size of the developed corn canopy, the relationship between coherence and crop height is limited to the first corn stages. For this reason, a new parameter named daily growth rate has been defined and found to be well correlated with the radar coherence.

Low Earth Orbit (LEO) remote sensing missions present a main limitation regarding their revisit time of several days or weeks. They cannot provide continuous monitoring over the same area of the planet. The on-going studies on Geosynchronous Synthetic Aperture Radar (GEOSAR), which have a revisit time of less than 24 h, aim to mitigate this limitation [1] . Since high altitude GEO missions are beyond MEO navigation satellites coverage, a ground based interferometric system (GEODE) has been developed for GEO Precise Orbit Determination, a requirement to form focused GEOSAR synthetic apertures [2] . Since no operational GEOSAR missions are available yet, present Geostationary telecommunications satellites can be used as transmitters of opportunity for GEODE experimental testing and validation. Recent results show the convenience of using ground reflectors to extend interferometric baselines lengths up to kilometers to achieve the required GEOSAR orbit precision. Locating stable reflectors of opportunity for interferometric orbit determination in urban areas is possible by complementing the ground interferometer with a multistatic SAR imaging processor capable to map the surrounding scattering centers.

Future Geosynchronous Synthetic Aperture Radar (GEOSAR) missions will provide permanent monitoring of continental areas of the planet with revisit times of less than 24 h. Several GEOSAR missions have been studied in the USA, Europe, and China with different applications, including water cycle monitoring and early warning of disasters. GEOSAR missions require unprecedented orbit determination precision in order to form focused Synthetic Aperture Radar (SAR) images from Geosynchronous Orbit (GEO). A precise orbit determination technique based on interferometry is proposed, including a proof of concept based on an experimental interferometer using three antennas separated 10–15 m. They provide continuous orbit observations of present communication satellites operating at GEO as illuminators of opportunity. The relative phases measured between the receivers are used to estimate the satellite position. The experimental results prove the interferometer is able to track GEOSAR satellites based on the transmitted signals. This communication demonstrates the consistency and feasibility of the technique in order to foster further research with longer interferometric baselines that provide observables delivering higher orbital precision.

The advent of geosynchronous remote sensing missions requires the development of precise orbit determination techniques at high orbits. Geosynchronous satellites require station-keeping manoeuvres to be performed periodically. Dynamical models do not consider artificial forces. Therefore, the tracking system must be able to detect them in order to keep track of the spacecraft autonomously. A compact baseline interferometer is deployed in order to track the signals of opportunity from the ASTRA 19.2°E geostationary constellation. The system is capable of autonomously estimate the trajectory of the satellite for a period of 22 days. Even after artificial orbital manoeuvres are performed. These results show that interferometry is a valid alternative for future geosynchronous remote sensing missions which require precise orbit determination.

The performance of a compact baseline interferometer for precise orbital tracking of SAR missions is assessed. An experimental campaign is carried out by tracking non-cooperative telecommunication geostationary satellites. While the main baseline operates in nominal conditions, the reflector of the second slave has been removed in order to compare how the system performs with an important SNR degradation. Due to the large processing gain of the correlator, the quality of the phase measurements remains unaltered. The results demonstrate the robustness of the system. It would be able to track very weak signals such as radar side lobes and telemetry transmissions.

Simulations of SAR images obtained from a Geosynchronous Synthetic Aperture Radar (GEOSAR) is performed. Different errors on the orbit used for the image SAR processor are introduced to show quantitative information about the loss on range and azimuth resolution. Some simulations for correcting the errors using autofocus are explained.

HydroSoil is a measurement campaign, funded by the European Space Agency (ESA), where the temporal evolutions of two crops, barley and corn, have been continuously monitored during the whole crop period by means of a C-band Fully Polarimetric C-band Ground-Based Synthetic Aperture Radar (GB-SAR). SAR data has been collected together with ancillary data, and both are being processed to demonstrate the retrieval of soil moisture and vegetation parameters in an agricultural field under controlled conditions, to simulate the frequent acquisitions of GeoSAR missions.

In the framework of a feasibility study in support of the Hydroterra mission, a high resolution radar has been setup to monitor continuously a crop field with high resolution synthetic aperture radar images. In parallel, ground-truth data is acquired such as soil roughness, soil moisture and crop biological parameters. From this experimental data set the possibility to monitor soil moisture and crop biological parameters from back-scattering radar measurements will be assessed. To use the phase information of SAR images it is interesting to compensate the atmospheric phase screen induced by tropospheric refractive index. The paper presents a study of the atmosphetric phase drift prediction and compensation based on the local measurement of meteorological parameters.

The experimental result reported in this chapter review the application of (high resolution) Synthetic Aperture Radar (SAR) data to extract valuable information for monitoring urban environments in space and time. Full polarimetry is particularly useful for classification, as it allows the detection of built-up areas and to discriminate among their different types exploiting the variation of the polarimetric backscatter with the orientation, shape, and distribution of buildings and houses, and street patterns. On the other hand, polarimetric SAR data acquired in interferometric configuration can be combined for 3-D rendering through coherence optimization techniques. If multiple baselines are available, direct tomographic imaging can be employed, and polarimetry both increases separation performance and characterizes the response of each scatterer. Finally, polarimetry finds also application in differential interferometry for subsidence monitoring, for instance, by improving both the number of resolution cells in which the estimate is reliable, and the quality of these estimates.