Estimating the Doppler frequency shift caused by moving targets is one of the key objectives of Integrated Sensing And Communication (ISAC) systems, as it enables applications such as target classification, human activity recognition, and gait analysis. In practical scenarios, Doppler estimation is hindered by the movement of transmitter and receiver devices, and by the phase offsets caused by their clock asynchrony. Existing approaches haveseparatelyaddressed these two aspects, either assuming clock-synchronous moving devices or asynchronous static ones. In fact, jointly tackling device motion and clock asynchrony is extremely challenging, as the Doppler shift from device movement differs for each propagation path and the phase offsets are time-varying. In this work, we present AsyMov, a method to estimate the bistatic Doppler frequency of a target and its velocity in ISAC setups featuringmobile and asynchronousdevices. It leverages the channel impulse response at the receiver, by originally exploiting the invariance of phase offsets across propagation paths and the bistatic geometry, where the target Doppler and the device velocity are jointly estimated by a newly proposed alternating minimization algorithm. Moreover, it can be seamlessly integrated with device velocity measurements obtained from onboard sensors (if available), for enhanced reliability. Here, AsyMov is thoroughly characterized by way of theory (Cramér-Rao bound), simulation, and experiments, implementing it on an IEEE 802.11ay testbed and testing it on multiple setups in the 60 GHz and 28 GHz bands, including moving human subjects. Numerical and experimental results show superior performance against state-of-the-art methods and are on par with scenarios featuringstaticISAC devices.

Integrated Sensing And Communication (ISAC) systems are expected to perform accurate radar sensing while having minimal impact on communication. Ideally, sensing should only reuse communication resources, especially for spectrum which is contended by many applications. However, this poses a great challenge in that communication systems often operate on narrow subbands with low sensing resolution. Combining contiguous subbands has shown significant resolution gain in active localization. However, multiband ISAC remains unexplored due to communication subbands being highly sparse (non-contiguous) and affected by phase offsets that prevent their aggregation (incoherent). To tackle these problems, we design HiSAC, the first multiband ISAC system that combines diverse subbands across a wide frequency range to achieve super-resolved passive ranging. To solve the non-contiguity and incoherence of subbands, HiSAC combines them progressively, exploiting an anchor propagation path between transmitter and receiver in an optimization problem to achieve phase coherence. HiSAC fully reuses pilot signals in communication systems, applies to different frequencies, and can combine diverse technologies, e.g., 5G-NR and WiGig. We implement HiSAC on an experimental platform in the millimeter-wave unlicensed band and test it on objects and humans. Our results show it enhances the sensing resolution by up to 20 times compared to single-band processing while occupying the same spectrum.

In this work we present RAPID, the first joint communication and radar system based on next-generation IEEE 802.11ay WiFi networks operating in the 60 GHz band. Unlike existing approaches for human sensing at millimeter-wave frequencies, which rely on special-purpose radars, RAPID achieves radar-level sensing accuracy with IEEE 802.11ay access points, thus avoiding the burden of installing ad-hoc sensors. RAPID enables contactless human sensing applications, such as people tracking, Human Activity Recognition (HAR), and person identification without requiring modifications to the standard packet structure. Specifically, we leverage IEEE 802.11ay beam training to accurately localize and track multiple individuals within the same environment. Then, we propose a new way of using beam tracking to extract micro-Doppler signatures from the time-varying Channel Impulse Response (CIR) estimated fromreflectedpackets. Such signatures are fed to a deep learning classifier to perform HAR and person identification. RAPID is implemented on a cutting-edge IEEE 802.11ay-compatible FPGA platform with phased antenna arrays, and evaluated on a large dataset of CIR measurements. It is robust across different environments and subjects, and outperforms state-of-the-art sub-6 GHz WiFi sensing techniques. Using two access points, RAPID reliably tracks multiple subjects, reaching HAR and person identification accuracies of$94\%$and$90\%$, respectively.

Wideband millimeter-wave communication systems can be extended to provide radar-like sensing capabilities on top of data communication, in a cost-effective manner. However, the development ofjoint communication and sensingtechnology is hindered by practical challenges, such as occlusions to the line-of-sight path and clock asynchrony between devices. The latter introducestime-varyingtiming and frequency offsets that prevent the estimation of sensing parameters and, in turn, the use of standard signal processing solutions. Existing approaches cannot be applied to commonly used phased-array receivers, as they build on stringent assumptions about the multipath environment, and are computationally complex. We present JUMP, the first system enablingpracticalbistatic and asynchronous joint communication and sensing, while achieving accurate target tracking and micro-Doppler extraction in realistic conditions. Our system compensates for the timing offset by exploiting the channel correlation across subsequent packets. Further, it tracks multipath reflections and eliminates frequency offsets by observing the phase of a dynamically-selected static reference path. JUMP has been implemented on a 60 GHz experimental platform, performing extensive evaluations of human motion sensing, including non-line-of-sight scenarios. In our results, JUMP attains comparable tracking performance to a full-duplex monostatic system and similar micro-Doppler quality with respect to a phase-locked bistatic receiver.

A well established method to detect and classify human movements using Millimeter-Wave (mmWave) devices is the time-frequency analysis of the small-scale Doppler effect (termed micro-Doppler) of the different body parts, which requires a regularly spaced and dense sampling of the Channel Impulse Response (CIR). This is currently done in the literature either using special-purpose radar sen-sors, or interrupting communications to transmit dedicated sensing waveforms, entailing high overhead and channel utilization. In this work we present SPARCS, an integrated human sensing and commu-nication solution for mmWave systems. SPARCS is the first method that reconstructs high quality signatures of human movement from irregular and sparse CIR samples, such as the ones obtained during communication traffic patterns. To accomplish this, we formulate the micro-Doppler extraction as a sparse recovery problem, which is critical to enable a smooth integration between communication and sensing. Moreover, if needed, our system can seamlessly inject short CIR estimation fields into the channel whenever communication traffic is absent or insufficient for the micro-Doppler extraction. SPARCS effectively leverages the intrinsic sparsity of the mmWave channel, thus drastically reducing the sensing overhead with re-spect to available approaches. We implemented SPARCS on an IEEE 802.11ay Software Defined Radio (SDR) platform working in the 60 GHz band, collecting standard-compliant CIR traces matching the traffic patterns of real WiFi access points. Our results show that the micro-Doppler signatures obtained by SPARCS enable a typical downstream application such as human activity recognition with more than 7 times lower overhead with respect to existing methods, while achieving better recognition performance.

In this work, we investigate the use of backscattered mm-wave radio signals for the joint tracking and recognition of identities of humans as they move within indoor environments. We build a system that effectively works with multiple persons concurrently sharing and freely moving within the same indoor space. This leads to a complicated setting, which requires one to deal with the randomness and complexity of the resulting (composite) backscattered signal. The proposed system combines several processing steps: at first, the signal is filtered to remove artifacts, reflections, and random noise that do not originate from humans. Hence, a density-based classification algorithm is executed to separate the Doppler signatures of different users. The final blocks are trajectory tracking and user identification, respectively, based on Kalman filters and deep neural networks. Our results demonstrate that the integration of the last-mentioned processing stages is critical toward achieving robustness and accuracy in multiuser settings. Our technique is tested both on a single-target public data set, for which it outperforms state-of-the-art methods, and on our own measurements, obtained with a 77 GHz radar on multiple subjects simultaneously moving in two different indoor environments. The system works in an online fashion, permitting the continuous identification of multiple subjects with accuracies up to 98%, e.g., with four subjects sharing the same physical space, and with a small accuracy reduction when tested with unseen data from a challenging real-life scenario that was not part of the model learning phase.