Signal-Path Diversity Enhanced Cooperative Multi-BS ISAC for Robust Dynamic UAV Tracking

In 6G networks, Integrated Sensing and Communication (ISAC) is a key technology owing to its capability in enhancing spectral efficiency. Yet, in urban wireless environments, characterized by persistent random interference, maintaining stringent environmental prerequisites is infeasible. This bottleneck renders robust sensing under non-ideal conditions difficultly. In this paper, we propose a paradigm for multi-Base stations (BSs) cooperative sensing. In this paradigm, we simultaneously consider both reflected and scattered signals during the target UAV movement to improve sensing accuracy. As for the multipath signals arising from such paradigm, we have developed a mathematical model for the echo signals received at the ISAC BSs and illustrated how channel time-variability affects the receiving signals. Based on analysis for the time-varying nature of the real channel environment, we introduce a range power gain algorithm based on fuzzy positioning and name it Fuzzy Range Enhancement-Transmission Spatial Gain (FRE-TSG) inspired by Linearly Constrained Minimum Variance (LCMV). Furthermore, to robustly use multiple sensing results, we design a result-level fusion method Data-Driven-Maximum Consensus ISAC (2D-MaCeS) matching algorithm. This algorithm fully exploits the spatiotemporal diversity of multiple sensing results and reduces dependence on prior information. Finally, we validate the efficacy and robustness of the proposed algorithm through extensive simulation experiments.