多传感器分布式融合白噪声反卷积滤波器

基于Kalman滤波方法和白噪声估计理论,在按矩阵加权线性最小方差最优融合准则下,提出了带ARMA有色观测噪声系统的多传感器分布式融合白噪声反卷积滤波器,其中推导出用Lyapunov方程计算最优加权的局部估计误差互协方差公式。与单传感器情形相比,可提高融合估值器精度。它可应用于石油地震勘探信号处理。一个三传感器分布式融合Bernoulli-Gauss白噪声反卷积平滑器的仿真例子说明了其有效性。     

多通道ARMA信号信息融合Wiener滤波器

应用Kalman滤波方法,基于白噪声估计理论,在线性最小方差最优信息融合准则下,提出了多通道ARMA信号的两传感器信息融合稳态最优Wiener滤波器、平滑器和预报器;给出了最优加权阵和最小融合误差方差阵.与单传感器情形相比,可提高滤波精度.一个雷达跟踪系统的仿真例子说明了其有效性.     

多通道ARMA信号的三种多传感器信息融合Wiener滤波器

应用现代时间序列分析方法,基于ARMA新息模型、白噪声估值器和观测预报器,对带白色观测噪声的多通道ARMA信号,在线性最小方差最优信息融合准则下,提出了统一的和通用的按矩阵加权、按标量加权和按对角阵加权的多传感器信息融合Wiener滤波器,可统一处理滤波、平滑和预报问题．提出了计算局部估计误差方差和协方差的公式,它们被用于计算最优加权．同单传感器情形相比,可提高滤波精度．一个目标跟踪仿真例子说明了其有效性,且说明了三种加权融合滤波器的精度无显著差异,因而利用按标量加权融合滤波器以轻微的精度损失提供一种快速融合估计算法,便于实时应用．     

带相关噪声的观测融合稳态Kalman滤波算法及其全局最优性

对于带相关的输入白噪声和观测白噪声及相关观测白噪声的多传感器线性离散定常随机系统,用加权最小二乘(WLS)法提出了一种加权观测融合稳态Kalman滤波算法,可处理状态、白噪声和信号融合滤波、平滑、预报问题。基于稳态信息滤波器证明了它完全功能等价于集中式观测融合稳态Kalman滤波算法,因而它具有渐近全局最优性,且可减少计算负担。一个跟踪系统仿真例子验证了它的功能等价性。     

自校正分布式观测融合Kalman滤波器

对于带未知噪声统计和带具有相同右因子的观测阵的多传感器系统,应用加权最小二乘(WLS)法可得到一个等价的融合观测方程。该文应用现代时间序列分析方法,基于新息模型参数的在线辨识,可估计未知噪声方差,进而提出了自校正加权观测融合Kalman滤波器。在新息模型参数估计是一致的和观测数据是有界的假设下,该文证明了自校正Kalman滤波器收敛于当噪声统计已知时的全局最优融合Kalman滤波器,因而它具有渐近全局最优性。最后给出了一个4传感器跟踪系统的仿真例子并验证了其有效性。     

多传感器分布式融合Kalman预报器

应用现代时间序列分析方法,基于ARMA新息模型,在线性最小方差最优信息融合准则下,对于输入噪声与观测噪声相关且观测噪声相关的多传感器系统,分别提出了按矩阵加权、按标量加权和按对角阵加权的3种分布式融合稳态Kalman 预报器。其中提出了基于Lyapunov方程的局部预报估值误差方差阵和协方差阵计算公式。它们被用于计算最优加权,与单传感器情形相比,可提高估值器的精度。一个跟踪系统的仿真例子说明了其有效性,且说明了3种加权融合预报器的精度无显著差别。但标量加权融合预报器可显著减小计算负担,提供一种快速实时信息融合估计算法。     

网络化多传感器系统的序贯式融合滤波——基于噪声估计方法

在网络化多传感器系统中,由于各传感器采集到的量测信息在经网络向融合中心传递的过程中,常会出现各种随时间变化的延迟现象,而处理该类系统融合滤波问题的现有方法又大都难以实现滤波过程实时性与滤波精度最优性的共赢.为此,本文在线性最小均方误差意义下,利用不同时刻状态间的递推关系和噪声估计方法,提出了一种实时、递归、最优的序贯式融合滤波器.首先利用状态间的递推关系,将不同时刻得到的量测信息转化为当前状态的伪量测信息.其次,利用新提出的噪声估计方法求解伪量测方程中增益噪声的估计值和用于滤波器设计的增益矩阵.然后,基于转化后的伪量测信息和求取的滤波增益矩阵实现对系统状态的最优估计.以此方法依次处理该融合周期内到达融合中心的各量测信息,建立起一种实时、递归、最优的序贯式融合滤波器.最后,用计算机仿真来验证新方法的有效性.     

多传感器信息融合稳态最优Wiener反卷积滤波器

应用现代时间序列分析方法,基于ARMA新息模型和Lyapunov方程,提出了单通道ARMA信号的多传 感器信息融合稳态最优Wiener反卷积滤波器。它避免了Riccati方程,可用于设计含未知模型参数和含未知噪声方 差系统的自校正信息融合滤波器。一个仿真例子说明了其有效性。     

多传感器自适应滤波融合算法

该文提出了一种在线调整权值的多传感器自适应滤波数据融合跟踪算法,用于解决复杂背景下机动目标跟踪问题。首先自适应寻找各个传感器所对应的最优加权因子,确定融合后某一时刻目标最优观测值;其次,以输入信号作为相关自适应滤波器的观测信号,通过新息相关自适应滤波算法根据状态方程及观测方程中误差的变化,实时动态地调整增益矩阵,同时依据自适应滤波状态偏差输出信号及当前观测数据,应用模糊推理在线调整各传感器权值,最终系统输出即为测量轨迹在两级自适应调整融合下最优轨迹。仿真结果证明了算法有效性。     

具有网络攻击的多传感器系统融合滤波研究

文章研究了具有纵向相关噪声和网络攻击的多传感器系统的信息融合滤波。针对子系统应用状态增广的方法,首先设计出单传感器滤波,其次推导出任意两个子系统滤波误差协方差阵,最后以仿真例子说明了算法的有效性。     

Information fusion steady-state white noise deconvolution estimators with time-delayed measurements and colored measurement noises

White noise deconvolution or input white noise estimation problem has important application backgrounds in oil seismic exploration, communication and signal processing. By the modern time series analysis method, based on the Auto-Regressive Moving Average (ARMA) innovation model, under the linear minimum variance optimal fusion rules, three optimal weighted fusion white noise deconvolution estimators are presented for the multisensor systems with time-delayed measurements and colored measurement noises. They can handle the input white noise fused filtering, prediction and smoothing problems. The accuracy of the fusers is higher than that of each local white noise estimator. In order to compute the optimal weights, the formula of computing the local estimation error cross-covariances is given. A Monte Carlo simulation example for the system with 3 sensors and the Bernoulli-Gaussian input white noise shows their effectiveness and performances.     

Self-tuning weighted measurement fusion white noise deconvolution estimator

For the multi-sensor linear discrete time-invariant stochastic systems with correlated measurement noises and unknown noise statistics,an on-line noise statistics estimator is obtained using the correlation method.Substituting it into the optimal weighted fusion steady-state white noise deconvolution estimator based on the Kalman filtering,a self-tuning weighted measurement fusion white noise deconvolution estimator is presented.By the Dynamic Error System Analysis(DESA) method,it proved that the self-tunin...     

Self-tuning distributed measurement fusion Kalman estimator for the multi-channel ARMA signal

For the multisensor multi-channel autoregressive moving average (ARMA) signal with white measurement noises and a common disturbance measurement white noise, when the model parameters and the noise variances are all unknown, a multi-stage information fusion identification method is presented, where the consistent fused estimates of the model parameters and noise variances are obtained by the multi-dimension recursive instrumental variable (RIV) algorithm, correlation method and Gevers-Wouters algorithm with a dead band. Substituting these estimates into the optimal distributed measurement fusion Kalman signal estimator, a self-tuning distributed measurement fusion Kalman signal estimator is presented. Its convergence is proved by the dynamic error system analysis (DESA) method, so that it has asymptotical global optimality. In order to reduce computational load, a fast recursive inversion algorithm for a high-dimension matrix is presented by the inversion formula of partitioned matrix. Especially, when the process and measurement noise variance matrices are all diagonal matrices, the inversion formula of a high-dimension matrix is presented, which extends the formula of the inverse of Pei-Radman matrix. Applying the proposed inversion algorithm, the computation of the fused measurement and fused noise variance is simplified and their computational burden is reduced. A simulation example shows effectiveness of the proposed method.     

H<Subscript>∞</Subscript> Deconvolution Filters for Stochastic Systems with Interval Uncertainties

This paper is concerned with the robust H_∞ deconvolution filtering problem for continuous- and discrete-time stochastic systems with interval uncertainties. The matrices of the system describing the signal transmissions are assumed to be uncertain within given intervals, and the stochastic perturbation is in the form of multiplicative Gaussian white noise with constant variance. The purpose of the addressed problem is to design a robust H_∞ deconvolution filter such that the input signal distorted by the transmission channel could recover to a specified extent γ. By using stochastic analysis techniques and the Lyapunov stability theory, sufficient conditions are first derived for ensuring the asymptotical stability of the filtering error system. Then the filter parameters are characterized in terms of the solution to linear matrix inequalities, which can be easily solved by using available software packages. Two simulation examples are exploited to demonstrate the effectiveness of the proposed design procedures, respectively, for continuous- and discrete-time systems.     

基于卷积神经网络的红外图像融合算法

红外图像与可见光图像融合的目的是为人类观察或其他计算机视觉任务生成信息更加丰富的图像。本文针对深度学习近年来在计算机视觉领域取得的巨大成功,提出一种基于卷积神经网络的红外与可见光图像融合算法。首先,使用引导滤波和高斯滤波器组成的尺度感知边缘保护滤波器对输入的源图像进行多尺度分解,基础层利用像素强度分布的加权平均融合规则进行融合,细节层借助卷积神经网络对空间细节进行提取融合。实验结果表明,本文算法可以较好的将特定尺度信息进行保存,并减小滤波对边缘细节带来的光晕影响,融合后图像噪声较少,细节呈现的更加自然,并且适合人类视觉感知。