Distributed Extended Object Tracking Filter Through Embedded ADMM Technique

Zhifei Li, Hongyan Wang, Shi Yan, Hongxia Zou, Mingyang Du
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Abstract

This work is concerned with the distributed extended object tracking system over a realistic network, where all nodes are required to achieve consensus on both the extent and kinematics. To this end, we first exploit an aligned velocity model to establish a tight relation between the orientation and velocity vector. Then, we use the moment-matching method to give two separate models to match the information filter (IF) framework. Later, we resort to the two models to propose a centralized IF and extend it to the distributed scenario based on the embedded alternating direction method of multipliers (ADMM) technique. To keep an agreement between nodes, an optimization function is given, followed by a consensus-based constraint. Numerical simulation together with theoretical analysis verifies the convergence and consensus of the proposed filter.
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基于嵌入式ADMM技术的分布式扩展目标跟踪滤波器
本文研究了现实网络上的分布式扩展目标跟踪系统,要求所有节点在范围和运动学上达成一致。为此,我们首先利用对准速度模型来建立方向和速度矢量之间的紧密关系。然后,我们使用矩匹配方法给出两个独立的模型来匹配信息过滤(IF)框架。随后,我们利用这两个模型提出了一个集中式中频,并基于嵌入式乘法器交替方向方法(ADMM)技术将其扩展到分布式场景。为了保持节点之间的一致性,给出了一个优化函数,然后给出了一个基于共识的约束。数值仿真和理论分析验证了该滤波器的收敛性和一致性。
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