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%.

翻译：本文研究了机器人辅助集成感知与通信（ISAC）中的速度估计问题，其中移动机器人作为感知节点，但只能机会主义地重用不规则的5G/6G参考信号（RS）。我们表明，由这种不规则时域模式引起的速度轮廓可以分解为周期性峰值分量和幅度整形（加权）分量。利用这一结构，我们提出了一种符合标准的多周期图速度估计算法，该算法不需要新增专用感知RS或修改3GPP协议。仿真结果表明，与传统的周期图处理方法相比，所提方法在10%漏检率下实现了3dB信噪比增益，并将虚警率降低了51%，从而提高了低信噪比下的鲁棒性。