This article studies the distributed estimation problem of a multi-agent system with bounded absolute and relative range measurements. Parts of the agents are with high-accuracy absolute measurements, which are considered as anchors; the other agents utilize lowaccuracy absolute and relative range measurements, each derives an uncertain range that contains its true state in a distributed manner. Different from previous studies, we design a distributed algorithm to handle the range measurements based on extended constrained zonotopes, which has low computational complexity and high precision. With our proposed algorithm, agents can derive their uncertain range sequentially along the chain topology, such that agents with low-accuracy sensors can benefit from the high-accuracy absolute measurements of anchors and improve the estimation performance. Simulation results corroborate the effectiveness of our proposed algorithm and verify our method can significantly improve the estimation accuracy.

翻译：本文研究了具有有界绝对和相对测距的多智能体系统的分布式估计问题。部分智能体配备了高精度绝对测距设备，作为锚节点；其余智能体利用低精度绝对和相对测距，以分布式方式推导出包含其真实状态的不确定区域。与以往研究不同，我们设计了一种基于扩展约束区域多面体的分布式测距处理算法，具有低计算复杂度与高精度特性。通过所提算法，智能体可沿链式拓扑顺序推导其不确定区域，使得低精度传感器智能体能够受益于锚节点的高精度绝对测距，从而提升估计性能。仿真结果验证了所提算法的有效性，并证明该方法能显著提高估计精度。