面向低空智能网联系统中的低空动态感知与协同控制需求，针对无人机集群在未知障碍环境下对动态目标的持续跟踪任务，现有方法在面临密集障碍物遮挡或目标主动摆脱时，容易因局部观测缺失与通信受限发生目标失锁。为此，本文提出了一种基于无人机集群互联的“协同估计—分布式规划—失锁重捕”一体化编队追踪方法。首先，在协同感知与状态估计层，应用了基于高斯混合模型压缩通信的分布式粒子滤波框架，各无人机节点通过低带宽交流参数统计量与共识迭代，实现对目标状态后验分布的一致估计。其次，在规划与控制层，引入虚拟刚体坐标系描述编队构型，将跟踪与避障需求统一纳入前端动力学搜索与基于MINCO曲线的软约束轨迹优化中，并结合执行端检查形成“搜索—优化—执行”三级安全机制。最后，针对目标失锁工况，设计了一种分布式前沿搜索重捕策略：在观测丢失时停止常规滤波并继承历史粒子，融合局部负观测与障碍物信息，通过网络节点间的共识交互全局削弱无效区域的粒子权重，进而提取前沿热点并分配搜索方向承诺，驱动集群快速协同组网找回目标。多场景仿真实验表明，该方法在应对复杂障碍遮挡、目标摆脱机动等情况时，展现出较好的持续跟踪性能、编队维持能力与失锁重捕效果。

For the persistent tracking task of dynamic targets by UAV swarms in unknown obstacle environments, existing methods are prone to target loss when confronted with occlusions from dense obstacles or active evasion maneuvers of the target. To address this issue, this paper proposes an integrated formation tracking framework featuring “Cooperative Estimation–Distributed Planning–Target Reacquisition”. First, at the state estimation layer, a distributed particle filtering framework based on Gaussian mixture model is applied, where each UAV achieves consistent estimation of the target state posterior distribution through exchanging parameter statistics and consensus iteration. Secondly, at the planning and control layer, a virtual rigid-body coordinate system is introduced to describe the formation configuration. Tracking and obstacle avoidance requirements are concerned in both front-end dynamic search and soft-constrained trajectory optimization based on MINCO curves, forming a 'search–optimize–execute' three-level safety mechanism combined with execution-side verification. Finally, for target loss scenarios, a frontier search and re-acquisition strategy is designed. When observation is lost, conventional filtering is stopped and historical particles are retained; local negative observations and obstacle information are fused; the weights of particles in invalid areas are globally weakened through consensus; frontier hotspots are then extracted and search direction commitments are assigned, driving the swarm to quickly and cooperatively reacquire the target. Multi-scenario simulation results demonstrate that the proposed method exhibits superior performance in persistent tracking, formation maintenance, and target reacquisition under lock-loss conditions when dealing with complex obstacle avoidance and target evasive maneuvers.