🤖 AI Summary
This work addresses the challenge of velocity estimation in robot-assisted integrated sensing and communication (ISAC) systems, where mobile robots can only opportunistically reuse irregular 5G/6G reference signals, limiting the performance of conventional methods. The study is the first to reveal the structural characteristics of the velocity spectrum under such irregular reference signaling, decomposing it into a periodic peak component and an amplitude-weighted component. Building on this insight, the authors propose a multi-periodogram-based velocity estimation algorithm that requires no dedicated sensing signals or modifications to the 3GPP protocol, ensuring full compatibility with existing standards. Experimental results demonstrate that, at a 10% miss-detection rate, the proposed method achieves a 3 dB SNR gain over traditional periodogram approaches and reduces the false alarm rate by 51%.
📝 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%.