AsyMov: Integrated Sensing and Communications with Asynchronous Moving Devices

TL;DR

AsyMov tackles the challenging problem of bistatic Doppler estimation in ISAC systems with asynchronous moving devices by exploiting path- and time-domain invariances in the CIR phase measurements. The authors develop an alternating-minimization framework that jointly estimates the target Doppler $f_{ m D,t}$ and the TX velocity $v^{ m tx}$, aided by a closed-form η initialization and an AoA-based simplification to reduce unknowns. They provide a CRB analysis, extensive simulations across 5/28/60 GHz bands, and real-world experiments on an IEEE 802.11ay testbed, showing accurate Doppler estimates with as few as two static multipaths and robustness to irregular sampling. The work demonstrates clear practical impact for high-resolution sensing (e.g., gait, activity) in mobile and asynchronous ISAC deployments and offers a flexible integration path with onboard IMU data when needed.

Abstract

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 have separately addressed 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 featuring mobile and asynchronous devices. 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 featuring static ISAC devices.

Figures (15)

• Figure 1: Overview of AsyMov.
• Figure 2: Reference scenario and multipath geometry. The time dependency is omitted for better visualization.
• Figure 3: Example estimated target Doppler frequency with AsyMov, the combination of JUMP pegoraro2024jump (for to and cfo removal) and music schmidt1986multiple (for Doppler estimation), and Mobi2Sense zhang2022mobi2sense in our experimental evaluation.
• Figure 4: Block diagram of the proposed phase measurements steps and algorithm.
• Figure 5: Target Doppler frequency estimation error varying the number of available static scatterers, the duration of the processing window, the snr, and the aoa measurements error, with $f_{\rm c}=60GHz$, $f_{\rm c}=28GHz$, and $f_{\rm c}=5GHz$.