The WInd VElocity Radar Nephoscope (WIVERN) mission concept, a candidate for ESA’s Earth Explorer 11 program, aims at globally observing vertical profiles of reflectivity and line-of-sight (LoS) winds in cloudy and precipitating regions. WIVERN uses a 94-GHz dual-polarization Doppler radar with conical scanning to address the limited coherence duration between radar transmitted from low-Earth satellites with small antennas. This system transmits closely spaced pairs of horizontally and vertically polarized pulses, which are better correlated than pulses of the same polarization separated by longer intervals. The polarization diversity pulse pair (PDPP) technique is then used to estimate radar observables such as reflectivities, differential reflectivities, Doppler velocities, and differential phase. This article introduces an efficient method for generating H- and V-I and Q time series from the covariance matrix of the autocorrelation function. This method treats the signal as a nonstationary stochastic process, making it suitable for the PDPP pulse sequence from a rapidly rotating antenna and more computationally efficient than inverse fast Fourier transform techniques. It also accounts for interfering cross-polar signals and decorrelation from the scanning antenna. This method is included in the mission’s end-to-end simulator, which processes data from raw I and Q to Level 1 estimates of polarimetric variables. For scientific applications, averaging at least 5 km (40 polarization diversity (PD) pairs) is necessary to reduce noise in polarimetric variables and Doppler velocities. Under optimal conditions, uncertainties at 5-km integration are 0.7 dB for reflectivities, 0.3 dB for$Z_{\text {DR}}$, 0.4 m/s for Doppler velocities, and 1.9° for$\Phi _{\text {DP}}$.

WIVERN, a space mission proposed within the ESA Earth Explorer program, aims to provide new insight in the cloud and dynamical structure of tropical cyclones (TC). The mission hinges upon a 800 km swath conically scanning 3-mm Doppler radar. Through notional simulations of WIVERN observations based on the ClouSat nadir looking 3-mm radar, our study demonstrates that WIVERN could profile most of the TCs, particularly the glaciated part of the cloud above the freezing level and the precipitating stratiform regions. Because of its lower sensitivity, the WIVERN radar would provide 75% observations of clouds and 45% accurate horizontal winds in TCs in comparison to where CloudSat detects clouds. However, thanks to its rapid conical scanning, WIVERN would indeed provide ∼50 times more observations of clouds and 30 times more observations of horizontal winds in comparison to the number of clouds sampled by CloudSat.The proposed observing system has the potential to complement the sparse observations from aircraft-reconnaissance measurements and by ground-based and airborne Doppler radars, thus providing additional constraints into numerical weather prediction models and hopefully further improving forecasts of tropical cyclone intensity and track.

In this article, we propose a radar-based human action recognition system, capable of recognizing actions in real time. Range-Doppler maps extracted from a low-cost frequency-modulated continuous wave (FMCW) radar are fed into a deep neural network. The system is deployed on an edge device. The results show that the system can recognize five human actions with an accuracy of 93.2% and an inference time of 2.95 s. Raising an alarm when a harmful action happens is a crucial feature in an indoor safety application. Thus, the performance during the binary classification, i.e., fall vs nonfall actions, is also assessed, achieving an accuracy of 96.8% with a false-negative rate of 4%. To find the best tradeoff between accuracy and computational cost, the energy precision ratio of the system deployed on the edge is measured. The system achieves a 1.04 energy precision ratio value, where an ideal ratio would be close to zero.

The WIVERN (Wind VElocity Radar Nephoscope) mission, one of the four ESA Earth Explorer 11 candidate missions, aims at globally observing, for the first time, simultaneously vertical profiles of reflectivities and line of sight winds in cloudy and precipitating regions. WIVERN adopts a dual-polarization Doppler radar in order to overcome the short decorrelation time between successive radar pulses transmitted from low Earth-orbiting satellites with finite beamwidth antennas. WIVERN transmits a single polarization state at a time (H or V), receives in both polarization states, and uses the Polarization Diversity Pulse Pair (PDPP) technique to estimate the Doppler velocity. The weaker cross-polar signals can sometimes interfere with the co-polar ones, causing ghost signals in the measurements that hinder the system’s overall performance. Additionally, with the envisaged radar trigger mode, parameters such as Linear Depolarization Ratio (LDR) and Differential Reflectivity (ZDR) cannot be directly measured because of the nearly simultaneous transmission of H and V pulses. To overcome these challenges, this article presents a novel technique based on the Optimal Estimation (OE) algorithm for retrieving LDR, ZDR, and co-polar reflectivity for radars operated in PDPP mode. The performance of the proposed method is evaluated using a realistic climatology of profiles simulated from CloudSat data. Results demonstrate that co-polar reflectivity can be accurately retrieved in regions with a good signal-to-noise ratio and in the absence of simultaneous cross-talk interference in both channels (which occurs very rarely). The LDR retrieval on the other hand is typically driven by the a-priori with a substantial impact of measurements only for the surface returns. The impact of cross-talk is also assessed on the reduction of precise Doppler measurements. Findings confirm that a selection of the separation between the two polarization diversity pulses (THV) of 20 μs achieves a good balance between the large errors originated by the strong dependence on the Doppler phase noise at smallTHVs and those caused by the drop in correlation and unambiguous Nyquist velocity at largeTHV.

In this article, a new microwave-radar-based technique for short-range detection and classification of multiple human and vehicle targets crossing a monitored area is proposed. This approach, which can find applications in both security and infrastructure surveillance, relies upon the processing of the scattered-field data acquired by low-cost off-the-shelf components, i.e., a 24 GHz frequency-modulated continuous wave (FMCW) radar module and a Raspberry Pi mini-PC. The developed method is based on anad hocprocessing chain to accomplish the automatic target recognition (ATR) task, which consists of blocks performing clutter and leakage removal with an infinite impulse response (IIR) filter, clustering with a density-based spatial clustering of applications with noise (DBSCAN) approach, tracking using a Benedict-Bordner$\alpha $-$\beta $filter, features extraction, and finally classification of targets by means of a$k$-nearest neighbor ($k$-NN) algorithm. The approach is validated in real experimental scenarios, showing its capabilities in correctly detecting multiple targets belonging to different classes (i.e., pedestrians, cars, motorcycles, and trucks).