Multi-Periodogram Velocity Estimation with Irregular Reference Signals for Robot-Aided ISAC

Yi Geng; Pan Cao; Ting Zeng; Yongqian Deng

cs.ROApr 28, 2026

Multi-Periodogram Velocity Estimation with Irregular Reference Signals for Robot-Aided ISAC

Authors: Yi Geng, Pan Cao, Ting Zeng, Yongqian Deng

Organizations: Cictmobile, China · University of Hertfordshire, UK · Beihang University, China · CMZII, China

Abstract

This paper addresses velocity estimation within robot-aided integrated sensing and communications (ISAC), where mobile robots act as sensing nodes but can only opportunistically reuse irregular 5G/6G reference signals (RSs). We show that the velocity profile induced by such irregular time-domain patterns can be decomposed into a periodic-peak component and an amplitude-shaping (weighting) component. Leveraging this structure, we propose a multi-periodogram velocity estimation algorithm that is standard-compliant and does not require new sensing-dedicated RSs or 3GPP modifications. Simulation results demonstrate that, compared with conventional periodogram processing, the proposed method improves low-SNR robustness by achieving a 3 dB SNR gain at the 10% missed-detection rate and reducing false alarms by 51%.

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Jul 21, 2026 · cs.ITJ/K move · Enter open · S save

CRB-Driven Beamforming and Trajectory Optimization for UAV-assisted ISAC System

Yi Yang, Qianqian Zhang, Huaxia Wang

Department of Electrical and Computer Engineering, Rowan University, NJ, USA

In this paper, we study an unmanned aerial vehicle (UAV)-assisted integrated sensing and communication (ISAC) system, where a UAV enhances the sensing capability of a base station (BS) towards a target while ensuring reliable communication towards a downlink user. This architecture is practically attractive for future wireless networks due to the UAV's controllable mobility and adaptive sensing coverage in wireless environments. The sensing performance is characterized by the average Cramér-Rao bound (CRB), which quantifies the minimum variance of the unbiased angle-of-arrival estimation. To enhance the sensing performance, the UAV trajectory and beamforming parameters are jointly optimized under power and mobility constraints, while satisfying communication requirements to the downlink user. To address the resulting non-convex problem, we employ null-space projection for beamforming design and adopt deep reinforcement learning for the trajectory optimization over a discrete-time scale. In each time slot, beamforming is optimized based on the channel state information to improve CRB performance while mitigating interference between the BS and the communication user. Simulation results demonstrate that the proposed method significantly reduces the time-averaged CRB by over 10%, compared with the ISAC system without UAV assistance, and also achieves a higher sensing accuracy than both the fixed-UAV-trajectory and the maximum-ratio-transmission-based beamforming benchmarks.

Integrated Sensing And Communication Unmanned Aerial Vehicle Trajectories Beamforming Wireless Networks Trajectory Optimization Sensing

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Abstract

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