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Near-ground precipitation sensing plays a vital role in meteorology, hydrology, and climate science. Ground-based radars and distributed rain gauges are the workhorses of precipitation sensing, but due to their limited scalability, they provide very coarse near-ground precipitation sensing resolution. This article proposes a novel approach for near-ground precipitation sensing using communication-based full-duplex (FD) multiple-input multiple-output (MIMO) base stations, thereby enabling highly scalable precipitation sensing. We first derive a MIMO channel to characterize combined scattering from precipitation particles and backscatter from objects in the precipitation footprint. The precipitation model leverages the concept of resolution volume from the weather radar literature and captures different radio configurations to match modern base station architectures across diverse spectrum bands. Using the proposed channel models, we derive a suite of transmit/receive beamformers that optimize precipitation sensing under a diverse set of practical constraints. Finally, we use both simulation-based and measurement-based clutter analysis to quantify the performance of the proposed beamformers. In the process, we identify operational regimes to achieve high precipitation estimation accuracy. 

Radars are widely adopted for autonomous navigation and vehicular networking due to their robustness to weather conditions as compared to visible light cameras and lidars. However, radars currently struggle with differentiating static vs tangentially moving objects within a single radar frame since both yield the same Doppler along line-of-sight paths to the radar. Prior solutions deploy multiple radar or visible light camera modules to form a multi-“look” synthetic aperture for estimating the single-frame velocity vectors, to estimate tangential and radial velocity components of moving objects leading to higher system costs. In this paper, we propose to exploit multi-bounce scattering from secondary static objects in the environment, e.g., building pillars, walls, etc., to form an effective multi-“look” synthetic aperture for single-frame velocity vector estimation with a single multiple-input, multiple-output (MIMO) radar, thus reducing the overall system cost and removing the need for multi-module synchronization. We present a comprehensive theoretical and experiment evaluation of our scheme, demonstrating a 4.5× reduction in the error for estimating moving objects’ velocity vectors over comparable single-radar baselines. 

We explore synthetic aperture radar (SAR) 3D imaging capabilities in the sub-THz band using a novel 110-260 GHz experimental testbed. We propose RADar Implicit SHapes (RADISH), a post-processing method that leverages the SAR’s millimeter-level imaging resolution to estimate an object’s 3D shape. RADISH first c onverts high-resolution SAR images to a detailed point cloud of a scanned object. The point cloud is then used to fit an implicit neural representation to the object’s surface by approximating the signed distance function of the scene. We experimentally validate RADISH’s ability to represent the salient geometric features of real-world 3D objects. 

An emerging application of wireless sensing is locating and tracking humans in their living environments, a primitive that can be leveraged in both daily life applications and emergency situations. However, most proposed methods have limited spatial resolution when multiple humans are in close vicinity. The problem becomes exacerbated when there is no line-of-sight path to the humans. In this paper, we consider multi-person localization of humans in close vicinity of each other. We propose the use of synthetic aperture radar that combines both translation and rotation to increase effective aperture size, leveraging small rhythmic changes in the radar range due to human breathing. We experimentally evaluate the proposed algorithm in both line-of-sight and through-wall cases with three to five humans in the scene. Our experimental results show that: (i) larger synthetic apertures due to radar translation improve multi-person localization, e.g., by 1.42× when the aperture size is increased by a factor of 2×, and (ii) rotation can largely compensate for gains provided by translation, e.g., rotating the radar over 360° without changing the aperture size results in 1.22× gains over no rotation. Overall, maximal gains of 2.19× are achieved by rotating and translating over a 2× larger aperture. 

In this paper, we ask, "Can millimeter-wave (mmWave) radars sense objects not directly illuminated by the radar - for instance, objects located outside the transmit beamwidth, behind occlusions, or placed fully behind the radar?" Traditionally, mmWave radars are limited to sense objects that are directly illuminated by the radar and scatter its signals directly back. In practice, however, radar signals scatter to other intermediate objects in the environment and undergo multiple bounces before being received back at the radar. In this paper, we present Hydra, a framework to explicitly model and exploit multi-bounce paths for sensing. Hydra enables standalone mmWave radars to sense beyond-field-of-view objects without prior knowledge of the environment. We extensively evaluate the localization performance of Hydra with an off-the-shelf mmWave radar in five different environments with everyday objects. Exploiting multi-bounce via Hydra provides 2×-10× improvement in the median beyond-field-of-view localization error over baselines. 

In this paper, we propose a method to repurpose the multi-user MIMO downlink transmission for joint wireless communication and imaging. The key idea is to introduce the concept of virtual users in the communication coverage area and use the existing MUMIMO beamforming methods to jointly beamform towards real and virtual users. The virtual users are placed to complement the locations of actual users, with the objective to illuminate the scene as uniformly as possible. We study a single-parameter tradeoff, introduced by a power split parameter between real and virtual users. We demonstrate via simulated examples that the virtual user concept is effective in providing a scalable imaging and communications performance tradeoff for cases where the real users are clustered in small geographical areas. 

Multiple-input, multiple-output (MIMO) radars can estimate radial velocities of moving objects, but not their tangential velocities. In this paper, we propose to exploit multi-bounce scattering in the environment to form an effective multi-“look” synthetic aperture and enable estimation of a moving object's entire velocity vector - both tangential and radial velocities. The proposed approach enables instantaneous velocity vector estimation with a single MIMO radar, without additional sensors or assumptions about the object size. The only requirement of our approach is the existence of at least one resolvable multi-bounce path to the object from a static landmark in the environment. The approach is validated both in theory and simulation. 

Multiple-input, multiple-output (MIMO) radars can estimate radial velocities of moving objects, but not their tangential velocities. In this paper, we propose to exploit multi-bounce scattering in the environment to form an effective multi-“look” synthetic aperture and enable estimation of a moving object's entire velocity vector - both tangential and radial velocities. The proposed approach enables instantaneous velocity vector estimation with a single MIMO radar, without additional sensors or assumptions about the object size. The only requirement of our approach is the existence of at least one resolvable multi-bounce path to the object from a static landmark in the environment. The approach is validated both in theory and simulation. 

This article presents a new notch steering scheme for hybrid beamforming transmitters (TXs) aimed at suppressing spatial interference, thereby enhancing the signal-to-interference-plus-noise ratio (SINR) to support spatial multiplexing. Built upon existing phased arrays, this scheme integrates an auxiliary-path vector modulator (VM) into each antenna element, which in turn, forms an interference-canceling beam. By spatially combining the array factors (AFs) of the main beam and the interference-canceling beam, a deep spatial notch is created while ensuring minimal main-beam power degradation. Unlike the conventional zero-forcing method that requires matrix inversion in digital for spatial notch creation, our scheme enables the computation of antenna weights in analog, significantly reducing the computational cost and latency. Leveraging this new notch steering scheme, we develop a 28-GHz four-element fully connected (FC) hybrid beamforming TX array using the GlobalFoundries 45-nm CMOS Silicon-on-Insulator (SOI) process. It is capable of simultaneously transmitting two independent, wideband data streams (DSs) in the same polarization toward two directions. In probing-based measurements, each TX channel delivers 19.7-dBm OP1 dB, 20.4-dBm PSAT , and 30.6% peak power-added efficiency (PAE) at 29 GHz, demonstrating state-of-the-art TX linearity and efficiency. In over-the-air (OTA) measurements, the packaged TX array achieves 29.8-dBm EIRP1 dB and is able to steer a spatial notch outside the −10-dB beamwidth of the main beam, with a notch depth of >35 dB and a main-beam power degradation of < 0.8 dB. Moreover, in spatial multiplexing demonstrations, the TX array is capable of transmitting a 400-MHz 64-quadrature amplitude modulation (QAM) signal to the intended receiver (RX) in the first DS, while suppressing the co-channel continuous-wave or wideband modulated interference created by the second DS with a high SINR.