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

TL;DR

This paper introduces a novel multi-periodogram velocity estimation method for robot-aided ISAC that effectively utilizes irregular 5G/6G reference signals, enhancing robustness and accuracy without requiring new signals.

Contribution

It proposes a standard-compliant multi-periodogram algorithm that decomposes velocity profiles and improves low-SNR performance in irregular reference signal scenarios.

Findings

Achieves a 3 dB SNR gain at 10% missed detection rate.

Reduces false alarms by 51%.

Does not require modifications to existing 3GPP standards.

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%.