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Swarm intelligence based clustering and localizing methods for loitering munitions in a satellite denied environment

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Chinese Journal of Aeronautics 2023年第10期36卷 409-433页

作者： Hao WU Zhonghong WU Zhangsong SHI Shiyan SUN Pengfei WU Zhi WANG School of Ordnance Engineering Naval University of EngineeringWuhan 430033China

In the networking of loitering munitions during a battle,clustering and localizing algorithms become a major problem because of their highly dynamic topological structure,incomplete connectivity,and limited *** paper proposed swarm intelligence based collaborative localizing,clustering,and routing scheme for an ad hoc network of loitering munitions in a satellite denied environment.A hybrid algorithm was first devised by integrating an improved coyote optimization algorithm with a simplified grey wolf optimizer under the sinusoidal crossover *** performance of this algorithm was considerably improved thanks to *** this basis,a swarm intelligence based localizing algorithm was *** cubes were created to reduce the initial search space,which effectively lowered the localizing ***,an energysaving clustering algorithm based on the hybrid algorithm was put forward to enhance the clustering efficiency by virtue of grey wolf ***,an analysis model was developed to determine the optimal number of clusters using the lowest possible number of ***,a compressed sensing routing scheme based on the hybrid algorithm was proposed to transmit data from a cluster head to a base *** algorithm constructed an efficient routing tree from the cluster head to the base station,so as to reduce the routing delay and transmission *** revealed in the results of simulation experiments,the proposed collaborative localizing,clustering and routing algorithms achieved better performance than other popular algorithms employed in various scenarios.

关键词： Ammunition networking Ad hoc network Clustering collaborative localization Swarm intelligence

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Decentralized localization for Multi-agent Systems Based on Asynchronous Communication

Decentralized Localization for Multi-agent Systems Based on ...

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第40届中国控制会议

作者： Lei Lu Jinwen Hu Quan Pan Chunhui Zhao Zhao Xu Caijuan Jia School of Automation Northwestern Polytechnical University School of Electronics and Information Northwestern Polytechnical University ASN Technology Group Co. Ltd

This paper provides a fully distributed algorithm for localization problem of multi-agent systems based on the extended and iterated Kalman filter. The main challenge in distributed collaborative localization is how to accurately handle the information coupling problem between different agents. If the cross-covariance estimation of each agent is inaccurate or even directly ignored, it will lead to inconsistent in the final estimation results. It is even more difficult when sustained synchronous communication between the robots cannot be guaranteed. In this paper, an iterated Kalman filter method combined with relative measurement is studied. This algorithm is not only suitable for asynchronous communication and distributed scenarios, but also can greatly improve the accuracy of localization as compared to the method by only using private measurement. The solution consists of two parts. Firstly, each agents update their location information by motion and private measurements using extended Kalman filter estimator. Secondly, the agent uses the relative measurement information from neighbors to estimate the inter-robot correlations to further update its localization based on iterated Kalman filter estimator. Simulation and experimental results prove the effectiveness of our proposed algorithm.

关键词： collaborative localization Kalman Filter Multi-agent System

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