The burgeoning low-altitude economy (LAE) necessitates integrated sensing and communication (ISAC) systems capable of high-accuracy multi-target localization and velocity estimation under hardware and coverage constraints inherent in conventional ISAC architectures. This paper addresses these challenges by proposing a cooperative bistatic ISAC framework within MIMO-OFDM cellular networks, enabling robust sensing services for LAE applications through standardized 5G New Radio (NR) infrastructure. We first develop a low-complexity parameter extraction algorithm employing CANDECOMP/PARAFAC (CP) tensor decomposition, which exploits the inherent Vandermonde structure in delay-related factor matrices to efficiently recover bistatic ranges, Doppler velocities, and angles-of-arrival (AoA) from multi-dimensional received signal tensors. To resolve data association ambiguity across distributed transmitter-receiver pairs and mitigate erroneous estimates, we further design a robust fusion scheme based on the minimum spanning tree (MST) method, enabling joint 3D position and velocity reconstruction. Comprehensive simulation results validate the framework's superiority in computational efficiency and sensing performance for low-altitude scenarios.

翻译：蓬勃发展的低空经济对一体化感知通信系统提出了迫切需求，要求其能够在传统ISAC架构固有的硬件与覆盖限制下，实现高精度的多目标定位与速度估计。本文通过提出一种基于MIMO-OFDM蜂窝网络的协作式双基地ISAC框架，利用标准化的5G新空口基础设施，为低空经济应用提供鲁棒的感知服务。我们首先开发了一种基于CANDECOMP/PARAFAC张量分解的低复杂度参数提取算法，该算法利用时延相关因子矩阵中固有的范德蒙结构，从多维接收信号张量中高效恢复双基地距离、多普勒速度与到达角。为解决分布式发射-接收节点间的数据关联模糊性并抑制估计误差，我们进一步设计了一种基于最小生成树方法的鲁棒融合方案，实现了联合三维位置与速度重构。综合仿真结果验证了该框架在低空场景下计算效率与感知性能方面的优越性。