Multi-reflection time-of-flight mass spectrometer (MR-TOF-MS) is one of the new experimental devices developed in recent years. As the flight path extended several orders of magnitude as the analyzer is traversed many...Multi-reflection time-of-flight mass spectrometer (MR-TOF-MS) is one of the new experimental devices developed in recent years. As the flight path extended several orders of magnitude as the analyzer is traversed many times by the ions, it allows achieving higher mass resolving power than the conventional time-of-flight mass spectrometry.展开更多
The acquisition,tracking,and pointing(ATP)system is widely used in target tracking,counter-UAV operations,and other related fields.As UAV technology develops,there is a growing demand to enhance the tracking capabilit...The acquisition,tracking,and pointing(ATP)system is widely used in target tracking,counter-UAV operations,and other related fields.As UAV technology develops,there is a growing demand to enhance the tracking capabilities of ATP systems.However,in practical applications,ATP systems face various design constraints and functional limitations,making it infeasible to indefinitely improve hardware performance to meet tracking requirements.As a result,tracking algorithms are required to execute increasingly complex tasks.This study introduces a multi-rate feedforward predictive controller to address issues such as low image feedback frequency and significant delays in ATP systems,which lead to tracking jitter,poor tracking performance,low precision,and target loss.At the same time,the pro-posed approach aims to improve the tracking capabilities of ATP systems for high-speed and highly maneuverable targets under conditions of low sampling feedback rates and high feedback delays.The method suggested is also characterized by its low order,fast response,and robustness to model parameter variations.In this study,an actual ATP system is built for target tracking test,and the proposed algorithm is fully validated in terms of simulation and actual system application verification.Results from both simulations and experiments demonstrate that the method effectively compensates for delays and low sampling rates.For targets with relative angular velocities ranging from 0 to 90°/s and angular accelerations between 0 and 470°/s^(2),the system improved tracking accuracy by 70.0%-89.9%at a sampling frequency of 50 Hz and a delay of 30 m s.Moreover,the compensation algorithm demonstrated consistent performance across actuators with varying characteristics,further confirming its robustness to model insensitivity.In summary,the proposed algorithm considerably enhances the tracking accuracy and capability of ATP systems for high-speed and highly maneuverable targets,reducing the probability of target loss from high speed.This approach offers a practical solution for future multi-target tracking across diverse operational scenarios.展开更多
This study investigates a consistent fusion algorithm for distributed multi-rate multi-sensor systems operating in feedback-memory configurations, where each sensor's sampling period is uniform and an integer mult...This study investigates a consistent fusion algorithm for distributed multi-rate multi-sensor systems operating in feedback-memory configurations, where each sensor's sampling period is uniform and an integer multiple of the state update period. The focus is on scenarios where the correlations among Measurement Noises(MNs) from different sensors are unknown. Firstly, a non-augmented local estimator that applies to sampling cases is designed to provide unbiased Local Estimates(LEs) at the fusion points. Subsequently, a measurement-equivalent approach is then developed to parameterize the correlation structure between LEs and reformulate LEs into a unified form, thereby constraining the correlations arising from MNs to an admissible range. Simultaneously, a family of upper bounds on the joint error covariance matrix of LEs is derived based on the constrained correlations, avoiding the need to calculate the exact error cross-covariance matrix of LEs. Finally, a sequential fusion estimator is proposed in the sense of Weighted Minimum Mean Square Error(WMMSE), and it is proven to be unbiased, consistent, and more accurate than the well-known covariance intersection method. Simulation results illustrate the effectiveness of the proposed algorithm by highlighting improvements in consistency and accuracy.展开更多
现有行人重识别技术主要关注水平视角下的图像。在例如无人超市这类特定场景下,摄像头以俯视角度拍摄,仅能获得有限的行人信息。针对此问题,将多模态视觉Transformer应用于俯视图行人重识别任务,利用俯视数据集中额外的深度模态来提高...现有行人重识别技术主要关注水平视角下的图像。在例如无人超市这类特定场景下,摄像头以俯视角度拍摄,仅能获得有限的行人信息。针对此问题,将多模态视觉Transformer应用于俯视图行人重识别任务,利用俯视数据集中额外的深度模态来提高俯视图的检索精度。具体而言,提出一种基于RGB(red,green,blue)与深度多模态视觉Transformer的特征提取方法,利用双流网络提取数据集的深度信息,自集成多个自注意力层的特征输出,以此作为最终的图像特征,并对损失函数进行改进,从而提高了模型的检索效果。通过在俯视图数据集TVPR(top-view person re-identification)和TVPR2上开展实验,结果表明:所提方法能有效提升检索效果,且超过了几种先进的俯视图行人重识别方法。展开更多
文摘Multi-reflection time-of-flight mass spectrometer (MR-TOF-MS) is one of the new experimental devices developed in recent years. As the flight path extended several orders of magnitude as the analyzer is traversed many times by the ions, it allows achieving higher mass resolving power than the conventional time-of-flight mass spectrometry.
基金supported by the National Natural Science Foun-dation of China(Grant No.52275099).
文摘The acquisition,tracking,and pointing(ATP)system is widely used in target tracking,counter-UAV operations,and other related fields.As UAV technology develops,there is a growing demand to enhance the tracking capabilities of ATP systems.However,in practical applications,ATP systems face various design constraints and functional limitations,making it infeasible to indefinitely improve hardware performance to meet tracking requirements.As a result,tracking algorithms are required to execute increasingly complex tasks.This study introduces a multi-rate feedforward predictive controller to address issues such as low image feedback frequency and significant delays in ATP systems,which lead to tracking jitter,poor tracking performance,low precision,and target loss.At the same time,the pro-posed approach aims to improve the tracking capabilities of ATP systems for high-speed and highly maneuverable targets under conditions of low sampling feedback rates and high feedback delays.The method suggested is also characterized by its low order,fast response,and robustness to model parameter variations.In this study,an actual ATP system is built for target tracking test,and the proposed algorithm is fully validated in terms of simulation and actual system application verification.Results from both simulations and experiments demonstrate that the method effectively compensates for delays and low sampling rates.For targets with relative angular velocities ranging from 0 to 90°/s and angular accelerations between 0 and 470°/s^(2),the system improved tracking accuracy by 70.0%-89.9%at a sampling frequency of 50 Hz and a delay of 30 m s.Moreover,the compensation algorithm demonstrated consistent performance across actuators with varying characteristics,further confirming its robustness to model insensitivity.In summary,the proposed algorithm considerably enhances the tracking accuracy and capability of ATP systems for high-speed and highly maneuverable targets,reducing the probability of target loss from high speed.This approach offers a practical solution for future multi-target tracking across diverse operational scenarios.
基金supported by the National Natural Science Foundation of China (Nos. 62276204, 62203343)。
文摘This study investigates a consistent fusion algorithm for distributed multi-rate multi-sensor systems operating in feedback-memory configurations, where each sensor's sampling period is uniform and an integer multiple of the state update period. The focus is on scenarios where the correlations among Measurement Noises(MNs) from different sensors are unknown. Firstly, a non-augmented local estimator that applies to sampling cases is designed to provide unbiased Local Estimates(LEs) at the fusion points. Subsequently, a measurement-equivalent approach is then developed to parameterize the correlation structure between LEs and reformulate LEs into a unified form, thereby constraining the correlations arising from MNs to an admissible range. Simultaneously, a family of upper bounds on the joint error covariance matrix of LEs is derived based on the constrained correlations, avoiding the need to calculate the exact error cross-covariance matrix of LEs. Finally, a sequential fusion estimator is proposed in the sense of Weighted Minimum Mean Square Error(WMMSE), and it is proven to be unbiased, consistent, and more accurate than the well-known covariance intersection method. Simulation results illustrate the effectiveness of the proposed algorithm by highlighting improvements in consistency and accuracy.
文摘现有行人重识别技术主要关注水平视角下的图像。在例如无人超市这类特定场景下,摄像头以俯视角度拍摄,仅能获得有限的行人信息。针对此问题,将多模态视觉Transformer应用于俯视图行人重识别任务,利用俯视数据集中额外的深度模态来提高俯视图的检索精度。具体而言,提出一种基于RGB(red,green,blue)与深度多模态视觉Transformer的特征提取方法,利用双流网络提取数据集的深度信息,自集成多个自注意力层的特征输出,以此作为最终的图像特征,并对损失函数进行改进,从而提高了模型的检索效果。通过在俯视图数据集TVPR(top-view person re-identification)和TVPR2上开展实验,结果表明:所提方法能有效提升检索效果,且超过了几种先进的俯视图行人重识别方法。
