Through-the-wall radar imaging (TTWRI) techniques are a promising research area for the radar community. These techniques are useful for detecting and identifying objects that are hidden behind walls making them a valuable tool for a variety of applications. From one hand frequency domain approach as diffraction tomography heavily rely on extensive data acquisition through multiple points in space, demanding significant resources and time. In the other hand, time domain techniques that can be performed by using one SAR line as our 3D backprojection method. The contributions of these two different techniques are studied and compared in this article.

Complex-Valued (CV) Synthetic Aperture Radar (SAR) image generation and augmentation is an important pillar to enhance deep learning performance for SAR applications such as detection, classification, segmentation, super-resolution etc. Usual transformations (flip, rotation, translation, scaling, etc.) are mostly inapplicable to SAR images due to the radar characteristics and the processing pipeline. In this paper, we explore the applicability of Wasserstein Generative Adversarial Networks (WGANs) to SAR data. The latter, being complex, require CV generator and a discriminator taking as input a complex-valued signal, to capture the underlying distribution. In particular, we show the applicability of CV-WGANs for the synthesis of (i) Fourier spectrum of various MNIST like datasets as toy example and (ii) L-band UAVSAR dataset.

In around the corner radar, particle filter tracking can help to improve the estimation accuracy of a Non-Line of Sight target position. However, in case of using one single receiving antenna, the measurement model only contains multipath delay information. Due to the lack of information in the model, particles corresponding to ambiguous positions can be assigned high weight values. This can leads the particle cloud to diverge from the actual target trajectory and requires several time steps to converge on it again. In this work, our proposed solution consists in exploiting additional Direction-of Arrival information provided by an array of receiving antennas in the particle filter framework. By simulation and experimentation, we will show that the proposed solution allows to solve the aforementioned problem, and thus achieves better localization results compared to single antenna-based tracking as well as non-tracking localization algorithm.

Temporal and spatial information is exploited in order to improve road detection performance in a forested area. Speckle is filtered using an FBR-based SAR time-series speckle filter, thus preserving temporal information. The additional spatial information is provided by the ascending and descending directions of acquisition of the satellite. Instead of a detection being made if the pixel value is lower than the threshold, we require the threshold to be met for every date in the time-series, and from both directions at once. This corresponds to both a temporal and spatial (generally East-West) stability requirements, and tends to reduce false positives as forest is not a stable environment. The technique was applied on a Sentinel-1 dataset over an area in northern Belize.

Radar is an extremely valuable sensing technology for detecting moving targets and measuring their range, velocity, and angular positions. When people are monitored at home, radar is more likely to be accepted by end-users, as they already use WiFi, is perceived as privacy-preserving compared to cameras, and does not require user compliance as wearable sensors do. Furthermore, it is not affected by lighting conditions nor requires artificial lights that could cause discomfort in the home environment. So, radar-based human activities classification in the context of assisted living can empower an aging society to live at home independently longer. However, challenges remain as to the formulation of the most effective algorithms for radar-based human activities classification and their validation. To promote the exploration and cross-evaluation of different algorithms, our dataset released in 2019 was used to benchmark various classification approaches. The challenge was open from February 2020 to December 2020. A total of 23 organizations worldwide, forming 12 teams from academia and industry, participated in the inaugural Radar Challenge, and submitted 188 valid entries to the challenge. This paper presents an overview and evaluation of the approaches used for all primary contributions in this inaugural challenge. The proposed algorithms are summarized, and the main parameters affecting their performances are analyzed.

The Doppler frequency signature and the cadence frequency of a couple of people moving simultaneously into the forest are presented in this paper. A brisk walk and a run have been analyzed in L-band. The results are based on measured data that have been collected by a bistatic radar working in continuous wave (CW). The purpose of the analysis is to assess the reliability of the short-time Fourier transform (STFT) and of the cadence frequency diagram to distinguish two physical activities performed simultaneously in a forested area and recorded at 1 GHz. Obstructing trees and small movements of the undergrowth largely impacted the spectrogram, making it difficult to interpret. Instead, the cadence frequency looks reliable for detecting the two activities and distinguishing them from the surrounding vegetation.

The Doppler frequency signature of a man walking into the forest is presented in this paper. The results are based on measured data that have been collected by a bistatic radar working in continuous wave (CW) in the UHF-band. VV and HH polarizations have been employed for our study. The spectrograms have been generated at 1 GHz and 435 MHz by using a Short-Time Fourier Transform (STFT). The purpose of the analysis is to highlight the impact of both the chosen frequencies and the VV and HH polarizations on the Doppler spectrum of a target moving in a cluttered environment. The Doppler signal has been detected at both used frequencies, but it has appeared strongly fragmented by using the VV polarization nevertheless. The HH polarization instead has proved to be more efficient in detecting the Doppler return of the subject moving around the obstructing trees.