AI 中文总结
研究低空无人机飞行中网络ISAC里非合作目标检测可靠性不足问题，提出安全感知前向检测设计，通过确定前向ROI、推导相关概率和缩放规律、制定资源优化问题，降低漏检概率和碰撞风险，且有限降低状态估计性能。
AI 中文摘要
网络一体化感知与通信（ISAC）利用多个地面基站（GBS）之间的协作来支持低空无线网络（LAWN）中的无人机安全飞行。现有研究主要集中在通信增强或目标参数估计上，而无人机前向区域中非合作目标的检测可靠性仍未得到充分研究。为了解决这个问题，本文提出了一种网络ISAC中的安全感知前向检测设计，其中多个GBS共同支持无人机下行通信、状态估计以及前向感兴趣区域（ROI）内的非合作目标检测。首先，通过无人机的位置、速度和安全制动距离确定前向ROI，并将其体素化以表征目标存在状态。然后，推导了无人机状态估计的克拉美罗下界（CRLB）和前向ROI漏检概率，并对其缩放规律进行了表征。具体而言，无人机状态估计CRLB随着协作GBS数量J大致以$\ln^{-2}J$的形式下降，而前向ROI漏检概率遵循指数形式的缩放规律$\lambda_{t}D_{f}\ln^{-2}J$。此外，还制定了一个安全感知资源优化问题，以联合配置感知导频比、发射功率和波束方向，在通信速率约束下平衡无人机状态估计性能和前向检测可靠性。仿真结果表明，与没有前向检测的基线方案相比，所提出的设计将平均漏检概率和相应的感知诱导碰撞风险降低了17.05%，同时仅引入了有限的状态估计性能下降，平均CRLB增加了14.82%。
英文摘要
Networked integrated sensing and communication (ISAC) exploits cooperation among multiple ground base stations (GBSs) to support safe uncrewed aerial vehicle (UAV) flight in low-altitude wireless networks (LAWNs). Existing studies mainly focus on communication enhancement or target parameter estimation, while the detection reliability of non-cooperative targets in the UAV forward region remains insufficiently investigated. To address this issue, this paper proposes a safety-aware forward detection design in networked ISAC, where multiple GBSs jointly support UAV downlink communication, state estimation, and non-cooperative target detection within the forward region of interest (ROI). First, the forward ROI is determined by the UAV position, velocity, and safe braking distance, and is voxelized to characterize target-existence states. Then, the Cramér-Rao lower bound (CRLB) for UAV state estimation and the forward-ROI miss-detection probability are derived, and their scaling laws are characterized: In detail, the UAV state-estimation CRLB approximately decreases as $\ln^{-2}J$ with the number of cooperative GBSs $J$, while the forward-ROI miss-detection probability follows an exponential-form scaling law as $λ_{t}D_{f}\ln^{-2}J$. Furthermore, a safety-aware resource optimization problem is formulated to jointly configure the sensing pilot ratio, transmit power, and beam direction, balancing UAV state-estimation performance and forward detection reliability under the communication-rate constraint. Simulation results show that, compared with the baseline scheme without forward detection, the proposed design reduces the average miss-detection probability and the corresponding sensing-induced collision risk by $17.05\%$, while introducing only limited state-estimation performance degradation, reflected by a $14.82\%$ increase in the average CRLB.
CommentsWe have identified fundamental issues in the theoretical analysis of the proposed algorithm, particularly in the convergence proof and the derivation of the ROI-level miss-detection probability. These issues affect the validity of the main claims. We are working on a thorough revision and will upload a corrected version once the analysis is complete
