Distributed Target Tracking in Multi-Agent Networks via Sequential Quadratic Alternating Direction Method of Multipliers
Distributed Target Tracking in Multi-Agent Networks via Sequential Quadratic Alternating Direction Method of Multipliers
复制标题
DOI：
10.23919/acc55779.2023.10156402
复制
发表时间：
2023-05
期刊：
影响因子：
--
通讯作者：
O. Shorinwa;M. Schwager
中科院分区：
文献类型：
--
作者：
O. Shorinwa;M. Schwager
提出了一种分布式多智能体目标跟踪算法，并将其作为最大后验优化问题。MAP估计通常是一种非凸优化，依赖于每个代理对目标的局部观察，因此需要分布式算法。在我们的算法中，每个智能体解决一系列局部优化问题来估计目标的轨迹，同时通过通信网络与它的一跳邻居通信。智能体不交流他们的原始观察，这可能是高维的（例如，图像），他们不依赖于中心协调节点或领导者，最大限度地减少了我们方法的通信带宽需求。我们利用序列二次规划（SQP）范式，并通过乘法器的一致交替方向方法（C-ADMM）实现后续子问题的分布式计算。我们的经验证明，与其他分布式方法相比，我们的算法收敛到局部最优解的速度更快。此外，我们的算法实现了与最佳竞争分布式算法相同的通信开销。
We present a distributed algorithm for multi-agent target tracking, posed as a maximum a-posteriori (MAP) optimization problem. MAP estimation is, in general, a non-convex optimization that depends on each agent’s local observation of the target, necessitating a distributed algorithm. In our algorithm, each agent solves a series of local optimization problems to estimate the target’s trajectory, while communicating with its one-hop neighbors over a communication network. The agents do not communicate their raw observations, which may be high dimensional (e.g., images), and they do not rely on a central coordinating node or leader, minimizing the communication bandwidth requirements of our approach. We utilize the sequential quadratic programming (SQP) paradigm, with distributed computation of the ensuing sub-problems achieved via the consensus alternating direction method of multipliers (C-ADMM). We empirically demonstrate faster convergence of our algorithm to a locally optimal solution compared to other distributed methods. In addition, our algorithm achieves about the same communication overhead as the best competing distributed algorithm.