The bases of distributed parallel network data storage technology(DPNDS) are high speed network tech-nology, database technology, artificial intelligence and multimedia technology. It will become the foundation of mod...The bases of distributed parallel network data storage technology(DPNDS) are high speed network tech-nology, database technology, artificial intelligence and multimedia technology. It will become the foundation of mod-ern information society because of the excellent management efficiency, reliability, availability, expansibility and lowcost. In this paper, we discuss the key technology of DPNDS which includes data distributing, parallel data process-ing, data network storage, object-oriented data storage and then give some suggestions about the new research direc-tion.展开更多
稀疏和随机动态变化是实际无线传感器网络(wireless sensor network,WSN)中普遍共同存在的两种通信拓扑不稳定因素,使基于一致性算法的分布式无迹信息滤波(distributed unscented information filter,DUIF)算法适用于稀疏动态WSN,将极...稀疏和随机动态变化是实际无线传感器网络(wireless sensor network,WSN)中普遍共同存在的两种通信拓扑不稳定因素,使基于一致性算法的分布式无迹信息滤波(distributed unscented information filter,DUIF)算法适用于稀疏动态WSN,将极大提高其实用性.为此,本文提出一种并行融合DUIF(parallel fusion DUIF,PF–DUIF)算法.在PF–DUIF算法中,通过将实时局部后验估计均值和协方差用于局部无迹信息滤波器(local unscented information filter,LUIF)的Sigma点采样,使LUIF和加权平均一致性滤波器(weighted average consensus filter,WACF)得以并行运行,从而有效抵制由通信拓扑随机动态变化带来的较大一致跟踪误差的困扰;同时,WACF通过对LUIF输出的无偏局部信息矩阵和向量分别进行平均一致性滤波,最终得到不包含由稀疏通信拓扑引起的平均一致误差的分布式后验估计结果;进而,建立即时更新机制有效抑制随机动态通信拓扑引起的PF–DUIF算法滤波异步问题,同时,基于稀疏动态WSN的平均网络模型,在通信能量消耗受限条件下优化WACF均方收敛速率,从而提高PF–DUIF算法的整体滤波效率.仿真实验结果表明,PF–DUIF算法能够有效应用于稀疏动态WSN机动目标跟踪.展开更多
文摘The bases of distributed parallel network data storage technology(DPNDS) are high speed network tech-nology, database technology, artificial intelligence and multimedia technology. It will become the foundation of mod-ern information society because of the excellent management efficiency, reliability, availability, expansibility and lowcost. In this paper, we discuss the key technology of DPNDS which includes data distributing, parallel data process-ing, data network storage, object-oriented data storage and then give some suggestions about the new research direc-tion.
文摘稀疏和随机动态变化是实际无线传感器网络(wireless sensor network,WSN)中普遍共同存在的两种通信拓扑不稳定因素,使基于一致性算法的分布式无迹信息滤波(distributed unscented information filter,DUIF)算法适用于稀疏动态WSN,将极大提高其实用性.为此,本文提出一种并行融合DUIF(parallel fusion DUIF,PF–DUIF)算法.在PF–DUIF算法中,通过将实时局部后验估计均值和协方差用于局部无迹信息滤波器(local unscented information filter,LUIF)的Sigma点采样,使LUIF和加权平均一致性滤波器(weighted average consensus filter,WACF)得以并行运行,从而有效抵制由通信拓扑随机动态变化带来的较大一致跟踪误差的困扰;同时,WACF通过对LUIF输出的无偏局部信息矩阵和向量分别进行平均一致性滤波,最终得到不包含由稀疏通信拓扑引起的平均一致误差的分布式后验估计结果;进而,建立即时更新机制有效抑制随机动态通信拓扑引起的PF–DUIF算法滤波异步问题,同时,基于稀疏动态WSN的平均网络模型,在通信能量消耗受限条件下优化WACF均方收敛速率,从而提高PF–DUIF算法的整体滤波效率.仿真实验结果表明,PF–DUIF算法能够有效应用于稀疏动态WSN机动目标跟踪.