传统的基于位置信息的服务(LBS)的隐私保护需要LBS提供者(简称LSP)与用户之间通过第三方作为中介来进行信息交换,但这种模式极易遭到攻击者攻击。为此提出一种基于K-匿名机制的隐形空间算法KABSCA(k-anonymity based spatial cloaking a...传统的基于位置信息的服务(LBS)的隐私保护需要LBS提供者(简称LSP)与用户之间通过第三方作为中介来进行信息交换,但这种模式极易遭到攻击者攻击。为此提出一种基于K-匿名机制的隐形空间算法KABSCA(k-anonymity based spatial cloaking algorithm),通过移动设备独立建立一个分布式网络直接与LSP通讯进而避免了第三方的安全威胁。仿真实验显示:使用这种算法,用户可以享受到高质量的信息服务以及高度的隐私保护。展开更多
The inertial navigation system(INS),which is frequently used in emergency rescue operations and other situations,has the benefits of not relying on infrastructure,high positioning frequency,and strong real-time perfor...The inertial navigation system(INS),which is frequently used in emergency rescue operations and other situations,has the benefits of not relying on infrastructure,high positioning frequency,and strong real-time performance.However,the intricate and unpredictable pedestrian motion patterns lead the INS localization error to significantly diverge with time.This paper aims to enhance the accuracy of zero-velocity interval(ZVI)detection and reduce the heading and altitude drift of foot-mounted INS via deep learning and equation constraint of dual feet.Aiming at the observational noise problem of low-cost inertial sensors,we utilize a denoising autoencoder to automatically eliminate the inherent noise.Aiming at the problem that inaccurate detection of the ZVI detection results in obvious displacement error,we propose a sample-level ZVI detection algorithm based on the U-Net neural network,which effectively solves the problem of mislabeling caused by sliding windows.Aiming at the problem that Zero-Velocity Update(ZUPT)cannot suppress heading and altitude error,we propose a bipedal INS method based on the equation constraint and ellipsoid constraint,which uses foot-to-foot distance as a new observation to correct heading and altitude error.We conduct extensive and well-designed experiments to evaluate the performance of the proposed method.The experimental results indicate that the position error of our proposed method did not exceed 0.83% of the total traveled distance.展开更多
文摘传统的基于位置信息的服务(LBS)的隐私保护需要LBS提供者(简称LSP)与用户之间通过第三方作为中介来进行信息交换,但这种模式极易遭到攻击者攻击。为此提出一种基于K-匿名机制的隐形空间算法KABSCA(k-anonymity based spatial cloaking algorithm),通过移动设备独立建立一个分布式网络直接与LSP通讯进而避免了第三方的安全威胁。仿真实验显示:使用这种算法,用户可以享受到高质量的信息服务以及高度的隐私保护。
基金supported in part by National Key Research and Development Program under Grant No.2020YFB1708800China Postdoctoral Science Foundation under Grant No.2021M700385+5 种基金Guang Dong Basic and Applied Basic Research Foundation under Grant No.2021A1515110577Guangdong Key Research and Development Program under Grant No.2020B0101130007Central Guidance on Local Science and Technology Development Fund of Shanxi Province under Grant No.YDZJSX2022B019Fundamental Research Funds for Central Universities under Grant No.FRF-MP-20-37Interdisciplinary Research Project for Young Teachers of USTB(Fundamental Research Funds for the Central Universities)under Grant No.FRF-IDRY-21-005National Natural Science Foundation of China under Grant No.62002026。
文摘The inertial navigation system(INS),which is frequently used in emergency rescue operations and other situations,has the benefits of not relying on infrastructure,high positioning frequency,and strong real-time performance.However,the intricate and unpredictable pedestrian motion patterns lead the INS localization error to significantly diverge with time.This paper aims to enhance the accuracy of zero-velocity interval(ZVI)detection and reduce the heading and altitude drift of foot-mounted INS via deep learning and equation constraint of dual feet.Aiming at the observational noise problem of low-cost inertial sensors,we utilize a denoising autoencoder to automatically eliminate the inherent noise.Aiming at the problem that inaccurate detection of the ZVI detection results in obvious displacement error,we propose a sample-level ZVI detection algorithm based on the U-Net neural network,which effectively solves the problem of mislabeling caused by sliding windows.Aiming at the problem that Zero-Velocity Update(ZUPT)cannot suppress heading and altitude error,we propose a bipedal INS method based on the equation constraint and ellipsoid constraint,which uses foot-to-foot distance as a new observation to correct heading and altitude error.We conduct extensive and well-designed experiments to evaluate the performance of the proposed method.The experimental results indicate that the position error of our proposed method did not exceed 0.83% of the total traveled distance.
