Multi-Periodogram Velocity Estimation with Irregular Reference Signals for Robot-Aided ISAC
It addresses velocity estimation for mobile robots in ISAC without requiring new dedicated signals, offering a practical solution for 5G/6G networks.
The paper proposes a multi-periodogram velocity estimation algorithm for robot-aided ISAC that reuses irregular 5G/6G reference signals, achieving a 3 dB SNR gain at 10% missed-detection rate and 51% reduction in false alarms compared to conventional periodogram processing.
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%.