AI 中文总结
研究已知位置的环境物联网反向散射设备在非视距蜂窝定位中作为低成本无源锚点的情况，通过推导不同校准状态下的等效 Fisher 信息矩阵，得出设备定位的相关结论及部署影响。
AI 中文摘要
已知位置的环境物联网反向散射设备可在蜂窝网络中创建几何锚定反射路径，作为低成本无源锚点。本文研究上行非视距定位中设备校准不完整时的定位信息，推导了校准、部分校准和完全未校准操作的闭式等效 Fisher 信息矩阵，分析了相关影响并得出定位所需设备数量等结论。
英文摘要
Ambient Internet-of-Things backscatter devices at known locations can act as low-cost passive anchors by creating geometrically anchored reflected paths in cellular networks. Unlike reconfigurable intelligent surfaces, practical backscatter devices are independently controlled and lack a common phase reference; their modulation signatures may be known, but their reflection gains and residual phases are generally uncalibrated. We study how much localization information survives this incomplete per-device calibration in uplink non-line-of-sight (NLOS) positioning, where the direct NLOS path and the backscatter-assisted paths share an unknown scatterer. Treating the common channel gain, the relative backscatter response, and the residual device phases as nuisance parameters, we derive closed-form equivalent Fisher information matrices for calibrated, partially calibrated, and fully uncalibrated operation. The analysis shows that unknown device phases remove carrier-phase information from the backscatter-assisted paths, whereas joint uncertainty in the common gain and relative response leaves the direct NLOS path with only bandwidth-dependent delay information. The resulting position-domain bounds show that device count alone is insufficient: the passive anchors must also observe the common scatterer from sufficiently diverse directions. For joint single-snapshot identification of the user equipment and scatterer, at least two devices in two dimensions and three in three dimensions are necessary. The results identify deployment implications for Ambient Internet-of-Things positioning and show which calibration losses also apply to separable subpanel-based reconfigurable-surface architectures.
