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 take the first step towards quantifying the fundamental performance trade-offs between imaging and communication supported simultaneously using the same network resources. We analyze an uplink system configuration with a full-duplex base station (BS) illuminating an imaging scene while receiving data from a communication user. Our main contributions are two-fold. First, we propose a unified signal space analysis framework based on the degrees of freedom metric to characterize the trade-offs between the two operations in the high signal-to-noise ratio regime. Second, we propose a dual-function joint processing scheme, decode-and-image, that allows the BS to simultaneously form an image of the scene while decoding the uplink user’s data. Our analysis and proposed scheme highlight the benefits of exploiting the uplink signals for imaging, at the cost of increased cooperation between the BS and uplink user. Moreover, our proposed scheme outperforms traditional schemes that enable dual-function operation via spatial or temporal isolation of imaging and communication signals.

We take the first step towards understanding the fundamental limits of blind wireless network inference performed by a distributed network of single-antenna adversary nodes. The distributed adversary nodes are assumed to be blind to the protocol parameters as well as the modulation, coding and encryption schemes used by the network being monitored. Focusing on the special case of inferring the channel access probabilities of the monitored nodes, we derive minimax bounds for blind inference. We show that blind inference is possible with similar sample complexity (asymptotically) as non-blind inference given certain network connectivity conditions are satisfied.

Multipath-assisted imaging algorithms have been shown to achieve super-resolution by incorporating multipath information into the imaging pipeline. In this paper, we derive the imaging degrees of freedom for multipath-assisted imaging systems to quantify the amount of super-resolution possible.