利用不规则参考信号实现机器人智能感知中的高精度速度估计
Multi-Periodogram Velocity Estimation with Irregular Reference Signals for Robot-Aided ISAC
- 将不规则信号分解为周期峰与加权成分，设计多周期图算法
- 在低信噪比下提升3 dB性能，误检率10%时漏检减少51%
- 无需新信号或标准修改，适合移动机器人智能感知场景
本文针对机器人辅助集成感知与通信（ISAC）中的速度估计问题，研究移动机器人作为感知节点时仅能机会性复用不规则5G/6G参考信号（RSs）的场景。我们发现，此类不规则时域模式引起的速度剖面可分解为周期性峰值分量与幅度整形（加权）分量。基于该结构，提出一种符合标准的多周期图速度估计方法，无需新增专用感知参考信号或3GPP修改。仿真结果表明，相较于传统周期图处理，所提方法在10%漏检率下实现3 dB信噪比增益，并使误报率降低51%。
原文摘要 · Abstract (English)
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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