AI 中文总结
研究移动智能体中FMCW雷达与静态雷达传感器融合以降目标状态估计不确定性问题，通过坐标变换等推导出CRLB，揭示更新率与量化保真度权衡，数值模拟表明选参数对降不确定性很关键。
AI 中文摘要
分布式雷达传感通过结合场景的多个局部视图，实现移动机器人和车辆系统的安全与弹性运行。本文考虑一个配备调频连续波（FMCW）雷达的移动智能体，它将其局部测量与周围静态雷达传感器的测量相融合，以降低目标状态估计中的不确定性。在容量受限的无线链路上共享测量的好处，是以两种相互竞争的退化机制为代价的：量化失真，它会增加有效噪声基底；以及延迟，它会使接收到的信息过时。因此，需要一个统一的框架来量化在有限通信资源下，各个雷达如何有助于降低不确定性。为此，我们通过坐标变换组合局部费希尔信息矩阵（FIM），纳入率失真理论的失真界限，并通过状态转移模型考虑信息过时，推导出融合目标状态的克拉美罗下界（CRLB）。我们表明，所得表达式揭示了由可用信道容量控制的更新率和量化保真度之间的基本权衡。我们的数值模拟说明了这种权衡，并表明仔细选择系统参数（例如，所选雷达系统、量化、更新率）对于最大程度降低不确定性是必要的。
英文摘要
Distributed radar sensing enables safe and resilient operation in mobile robotic and vehicular systems by combining multiple local views of the scene. In this paper, we consider a moving agent equipped with a frequency modulated continuous wave (FMCW) radar that fuses its local measurements with those of surrounding static radar sensors to reduce the uncertainty in target state estimation. The benefit of sharing measurements over capacity-limited wireless links come at the expense of two competing degradation mechanisms: quantization distortion, which increases the effective noise floor, and latency, which causes the received information to age. A unified framework is thus needed to quantify how individual radars contribute to uncertainty reduction under limited communication resources. To this end, we derive the Cramer-Rao lower bound (CRLB) for the fused target state by combining local Fisher information matrices (FIMs) through coordinate transformations, incorporating distortion bounds from rate-distortion theory and information aging via a state transition model. We show that the resulting expression reveals a fundamental tradeoff between update rate and quantization fidelity governed by the available channel capacity. Our numerical simulations illustrate this tradeoff and demonstrate that a careful choice of system parameters (e.g., selected radar systems, quantization, update rate) is necessary for maximum uncertainty reduction.
