面向无人机集群协同导航场景，提出了一种结合置信传播和信息滤波的协同导航方法。该方法可以分布式地融合卫星、惯性、机间测距等多源量测信息，有效提升无人机群的绝对定位性能。针对传统置信传播算法的复杂度过高的问题，引入信息滤波对协同导航系统线性部分进行解算，将概率分布乘积运算转换为正则参数加法运算，简化了采样和消息传递过程，实现了集群间协同消息的高效迭代传递与更新。户外飞行测试结果表明，所提出的协同导航方法在性能相近的情况下计算负载显著低于传统置信传播算法。

Facing the scenario of cooperative navigation for UAV swarm, a cooperative navigation method combining belief propagation and information filter is proposed. The method can distributively fuse multi-source measurements such as GNSS, inertial measurement and inter-UAV ranging, which effectively improves the absolute positioning performance of UAV swarm. To address the excessive computational complexity of the traditional belief propagation algorithm, information filter is introduced to solve the linear part of the cooperative navigation system. It transforms the multiplication operation of probability distributions into the addition operation of regular parameters, simplifies the sampling and message transmission procedures, and realizes efficient iterative transmission and updating of cooperative messages among clusters. Outdoor flight test results demonstrate that the proposed cooperative navigation method achieves significantly lower computational load than the traditional belief propagation algorithm while maintaining comparable navigation performance.