Dear Editor,Through distributed machine learning,multi-UAV systems can achieve global optimization goals without a centralized server,such as optimal target tracking,by leveraging local calculation and communication w...Dear Editor,Through distributed machine learning,multi-UAV systems can achieve global optimization goals without a centralized server,such as optimal target tracking,by leveraging local calculation and communication with neighbors.In this work,we implement the stochastic gradient descent algorithm(SGD)distributedly to optimize tracking errors based on local state and aggregation of the neighbors'estimation.However,Byzantine agents can mislead neighbors,causing deviations from optimal tracking.We prove that the swarm achieves resilient convergence if aggregated results lie within the normal neighbors'convex hull,which can be guaranteed by the introduced centerpoint-based aggregation rule.In the given simulated scenarios,distributed learning using average,geometric median(GM),and coordinate-wise median(CM)based aggregation rules fail to track the target.Compared to solely using the centerpoint aggregation method,our approach,which combines a pre-filter with the centroid aggregation rule,significantly enhances resilience against Byzantine attacks,achieving faster convergence and smaller tracking errors.展开更多
Radio-frequency(RF) tomography is an emerging technology which derives targets location information by analyzing the changes of received signal strength(RSS) in wireless links. This paper presents and evaluates a nove...Radio-frequency(RF) tomography is an emerging technology which derives targets location information by analyzing the changes of received signal strength(RSS) in wireless links. This paper presents and evaluates a novel RF tomography system which is capable of detecting and tracking a time-varying number of targets in a cluttered indoor environment. The system incorporates an observation model based on RSS attenuation histogram and a multi-target tracking-by-detection filtering approach based on probability hypothesis density(PHD) filter. In addition, the sequential Monte Carlo method is applied to implement the multi-target filtering. To evaluate the tracking system, the experiments involving up to 3 targets were performed within an obstructed indoor area of 70 m2. The experimental results indicate that the proposed tracking system is capable of tracking a time-varying number of targets.展开更多
基金supported By Guangdong Major Project of Basic and Applied Basic Research(2023B0303000009)Guangdong Basic and Applied Basic Research Foundation(2024A1515030153,2025A1515011587)+1 种基金Project of Department of Education of Guangdong Province(2023ZDZX4046)Shen-zhen Natural Science Fund(Stable Support Plan Program 20231122121608001),Ningbo Municipal Science and Technology Bureau(ZX2024000604).
文摘Dear Editor,Through distributed machine learning,multi-UAV systems can achieve global optimization goals without a centralized server,such as optimal target tracking,by leveraging local calculation and communication with neighbors.In this work,we implement the stochastic gradient descent algorithm(SGD)distributedly to optimize tracking errors based on local state and aggregation of the neighbors'estimation.However,Byzantine agents can mislead neighbors,causing deviations from optimal tracking.We prove that the swarm achieves resilient convergence if aggregated results lie within the normal neighbors'convex hull,which can be guaranteed by the introduced centerpoint-based aggregation rule.In the given simulated scenarios,distributed learning using average,geometric median(GM),and coordinate-wise median(CM)based aggregation rules fail to track the target.Compared to solely using the centerpoint aggregation method,our approach,which combines a pre-filter with the centroid aggregation rule,significantly enhances resilience against Byzantine attacks,achieving faster convergence and smaller tracking errors.
文摘Radio-frequency(RF) tomography is an emerging technology which derives targets location information by analyzing the changes of received signal strength(RSS) in wireless links. This paper presents and evaluates a novel RF tomography system which is capable of detecting and tracking a time-varying number of targets in a cluttered indoor environment. The system incorporates an observation model based on RSS attenuation histogram and a multi-target tracking-by-detection filtering approach based on probability hypothesis density(PHD) filter. In addition, the sequential Monte Carlo method is applied to implement the multi-target filtering. To evaluate the tracking system, the experiments involving up to 3 targets were performed within an obstructed indoor area of 70 m2. The experimental results indicate that the proposed tracking system is capable of tracking a time-varying number of targets.
