针对现有的格上身份基线性同态签名方案密钥存储量大、结构复杂导致方案实际运行效率相对偏低的问题,提出了一个NTRU(Number Theory Research Unit)格上高效的身份基线性同态签名方案。首先在密钥生成阶段利用NTRU密钥生成算法产生主密...针对现有的格上身份基线性同态签名方案密钥存储量大、结构复杂导致方案实际运行效率相对偏低的问题,提出了一个NTRU(Number Theory Research Unit)格上高效的身份基线性同态签名方案。首先在密钥生成阶段利用NTRU密钥生成算法产生主密钥,接着采用格基委派算法给出身份签名私钥,最后运行NTRU格上原像抽样算法产生出线性同态签名。对方案的安全性证明与性能分析结果表明,新方案满足正确性,具有弱内容隐私性。在随机预言机模型下,该方案在小整数解问题困难性条件下满足适应性选择身份和选择消息的存在性不可伪造性。同时,由于采用NTRU格的特殊结构,新方案在密钥量与运行效率方面与已有方案相比较均具有显著的优势,这对于计算资源受限环境的同态认证中具有重要的应用价值。展开更多
The task of simultaneous localization and mapping （SLAM） is to build environmental map and locate the position of mobile robot at the same time. FastSLAM 2.0 is one of powerful techniques to solve the SLAM problem. ...The task of simultaneous localization and mapping （SLAM） is to build environmental map and locate the position of mobile robot at the same time. FastSLAM 2.0 is one of powerful techniques to solve the SLAM problem. However, there are two obvious limitations in FastSLAM 2.0, one is the linear approximations of nonlinear functions which would cause the filter inconsistent and the other is the ＂particle depletion＂ phenomenon. A kind of PSO ＆ Hjj-based FastSLAM 2.0 algorithm is proposed. For maintaining the estimation accuracy, H~ filter is used instead of EKF for overcoming the inaccuracy caused by the linear approximations of nonlinear functions. The unreasonable proposal distribution of particle greatly influences the pose state estimation of robot. A new sampling strategy based on PSO （particle swarm optimization） is presented to solve the ＂particle depletion＂ phenomenon and improve the accuracy of pose state estimation. The proposed approach overcomes the obvious drawbacks of standard FastSLAM 2.0 algorithm and enhances the robustness and efficiency in the parts of consistency of filter and accuracy of state estimation in SLAM. Simulation results demonstrate the superiority of the proposed approach.展开更多
文摘针对现有的格上身份基线性同态签名方案密钥存储量大、结构复杂导致方案实际运行效率相对偏低的问题,提出了一个NTRU(Number Theory Research Unit)格上高效的身份基线性同态签名方案。首先在密钥生成阶段利用NTRU密钥生成算法产生主密钥,接着采用格基委派算法给出身份签名私钥,最后运行NTRU格上原像抽样算法产生出线性同态签名。对方案的安全性证明与性能分析结果表明,新方案满足正确性,具有弱内容隐私性。在随机预言机模型下,该方案在小整数解问题困难性条件下满足适应性选择身份和选择消息的存在性不可伪造性。同时,由于采用NTRU格的特殊结构,新方案在密钥量与运行效率方面与已有方案相比较均具有显著的优势,这对于计算资源受限环境的同态认证中具有重要的应用价值。
基金Project(ZR2011FM005)supported by the Natural Science Foundation of Shandong Province,China
文摘The task of simultaneous localization and mapping （SLAM） is to build environmental map and locate the position of mobile robot at the same time. FastSLAM 2.0 is one of powerful techniques to solve the SLAM problem. However, there are two obvious limitations in FastSLAM 2.0, one is the linear approximations of nonlinear functions which would cause the filter inconsistent and the other is the ＂particle depletion＂ phenomenon. A kind of PSO ＆ Hjj-based FastSLAM 2.0 algorithm is proposed. For maintaining the estimation accuracy, H~ filter is used instead of EKF for overcoming the inaccuracy caused by the linear approximations of nonlinear functions. The unreasonable proposal distribution of particle greatly influences the pose state estimation of robot. A new sampling strategy based on PSO （particle swarm optimization） is presented to solve the ＂particle depletion＂ phenomenon and improve the accuracy of pose state estimation. The proposed approach overcomes the obvious drawbacks of standard FastSLAM 2.0 algorithm and enhances the robustness and efficiency in the parts of consistency of filter and accuracy of state estimation in SLAM. Simulation results demonstrate the superiority of the proposed approach.
