粒子滤波的噪声相关下单步延迟无序量测更新算法

在目标跟踪系统中,因通信延迟等原因会出现传感器量测无序地到达融合中心的现象,将这些量测称为无序量测(OOSM).针对过程噪声、量测噪声相关的非线性系统中出现的无序量测问题,在现有算法基础上,提出了一种可处理单步延迟无序量测的新算法.在前向预测滤波框架下,对系统方程去相关化,并利用粒子滤波(PF)进行状态估计.仿真结果验证了算法的有效性.     

快速边缘粒子滤波在无序量测问题中的应用

在目标跟踪系统中,因通信延迟会出现传感器量测无序到达融合中心的现象,由此产生无序量测(OOSM)融合问题。针对非线性条件下的OOSM问题,在现有算法的基础上,提出了基于快速边缘粒子滤波(FMPF)的处理算法。新算法在FMPF框架下,结合前向预测滤波思想来处理OOSM问题。将目标运动状态向量分为线性和非线性2个子向量,并分别采用相应的无序处理算法进行估计。算法可以处理单步延迟和多步延迟的情形下的无序问题。最后理论分析和仿真实验表明:新算法能有效处理OOSM问题,且降低了算法的计算复杂度,提高了算法实时性。     

多目标无序量测OOSM-GMPHD滤波算法

针对无序量测条件下多目标跟踪问题,提出了一种适用于线性系统的单步滞后无序量测滤波算法(OOSM-GMPHD).在前向预测框架内,以高斯混合概率假设密度(GMPHD)滤波器为基础滤波算法,对每一高斯分量分别用延迟到达的量测与等价量测进行预测、更新,经剪枝与合并等步骤获得最终的目标数量与状态估计.仿真结果表明:算法可有效消除无序量测的影响,准确估计多目标数目和状态.     

基于伪测量的分布式最优单步延迟航迹融合估计

融合中心如何处理无序局部数据,对分布式多传感器系统的运行品质至关重要．本文将系统中的局部估计转化为伪测量,将分布式融合估计转化为二级集中式融合估计．将所得的伪测量兼分布式融合估计算法与单步延迟的无序测量数据（out-of-sequencemeasurements,OOSM）最优滤波-A1算法进行组合,得出了分布式多传感器系统的最优单步延迟无序航迹（out-of-sequence tacks,OOST）估计算法,适用于航迹无序局部数据融合估计．该算法具有最优估计性能．     

Globally Optimal Distributed Kalman Fusion With Local Out-of-Sequence-Measurement Updates

In a distributed multisensor fusion systems, observations produced by sensors can arrive at local processors out of sequence. The resulting problem at the central processor/fusion center-how to update current estimate using multiple local out-of-sequence-measurement (OOSM) updates - is a nonstandard distributed estimation problem. In this note, based on the centralized update algorithm with multiple asynchronous (1-step-lag) OOSMs see we firstly deduce the optimal distributed fusion update algorithm with multiple local asynchronous (1-step-lag) OOSM updates, which is proved, under some regularity conditions, to be equivalent to the corresponding optimal centralized update algorithm with all-sensor 1-step-lag OOSMs. Then, we propose an optimal distributed fusion update algorithm with multiple local arbitrary-step-lag OOSM updates.     

加权融合法处理无序量测问题

针对集中式多传感器目标跟踪系统中存在的无序量测问题,基于协方差加权融合的思想,在融合估计误差协方差矩阵迹最小意义下,建立了基于最优融合的多步延迟无序量测更新算法。该算法先将无序量测配准到最新状态估计的时刻,将其与之进行协方差加权融合。为进行无序量测与各传感器量测噪声相关性的计算,引入了等效量测。通过理论分析和仿真实验说明该算法能有效处理无序量测多延迟问题,其性能接近最优且随延迟步数增加性能下降非常小,而且有与最优的数据缓存法相同的滤波精度,以及较小的额外存储量。     

基于时间错序量测的多目标多传感器跟踪算法研究

在多传感器多目标跟踪系统中,经常有来自同一目标的量测到达融合中心时存在时间先后顺序上的混乱,被称为时间错序量测(Oosm);通常,现有的跟踪算法都是假设理想目标的观测值不混乱;现实中,可能错过的目标探测随意混乱,因而,滤波器不得不处理起因未知的量测,那么针对顺序量测的传统滤波器,例如KF,在此就不能直接使用;通过基于一些特殊矩阵非单一假设的经济存储和能效估计介绍了全局最优Oosm刷新算法,并结合概率数据关联PDA到Oosm刷新算法中;仿真结果显示Oosm刷新的PDA滤波器在性能上优于忽略Oosm的PDA滤波器,还就关于杂波中多目标跟踪如何通过JPDA结合oosm刷新算法展开讨论.     

基于Kalman滤波的自回归滑动平均信号信息融合Wiener滤波器

应用Kalman滤波方法,在按矩阵加权线性最小方差最优信息融合规则下,提出了带白色观测噪声的多通道ARMA信号的多传感器信息融合Wiener滤波器.它可统一处理信息融合滤波、平滑和预报问题.为了计算最优加权阵,提出了计算局部滤波误差互协方差阵的公式.同单传感器情形相比,可提高估计精度.一个带三传感器的目标跟踪系统的仿真例子说明了其有效性.     

自适应Kalman滤波器及其应用

对于带未知噪声统计的单输出系统,本文提出了一种新的自适应Kalman滤波器.应用 现代时间序列分析方法,基于ARMA新息模型的滑动平均(MA)参数的在线辨识,提出了 稳态最优Kalman滤波器增益估计的一种新算法,比Mehra的算法简单.同时还提出了辨 识滑动平均(MA)模型参数的一种新的自适应Kalman滤波算法.此外,给出了在雷达跟 踪系统中的应用,且仿真结果说明了本文算法的有效性.     

Robust estimation and filtering in the presence of bounded noise

Optimal algorithms for robust estimation and filtering are constructed. The noise is considered a deterministic variable belonging to a set described by a norm. Previous results, obtained for complete (one-to-one) and approximate information by M. Milanese and R. Tempo (ibid., vol.30, p.730-8, 1985), are extended to partial and approximate information. This information is considered useful in dealing with dynamic systems not completely identifiable and/or with two different sources of noise, such as process and measurement noise. For different norms characterizing the noise, optimal algorithms (in a min-max sense) are shown. In particular, for Hilbert norms a linear optimal algorithm is the well-known minimum variance estimator. For l_∞ norm an optimal algorithm, computable by linear programming, is presented. State estimation is formalized in the context of the general theory. For stable systems, an approximate state estimation is obtained by neglecting higher order powers. An upper bound determining the approximation introduced is derived. A numerical example illustrates the application of the theory     

Optimal state estimation for networked systems with random parameter matrices,correlated noises and delayed measurements

In this paper, the optimal least-squares state estimation problem is addressed for a class of discrete-time multisensor linear stochastic systems with state transition and measurement random parameter matrices and correlated noises. It is assumed that at any sampling time, as a consequence of possible failures during the transmission process, one-step delays with different delay characteristics may occur randomly in the received measurements. The random delay phenomenon is modelled by using a different sequence of Bernoulli random variables in each sensor. The process noise and all the sensor measurement noises are one-step autocorrelated and different sensor noises are one-step cross-correlated. Also, the process noise and each sensor measurement noise are two-step cross-correlated. Based on the proposed model and using an innovation approach, the optimal linear filter is designed by a recursive algorithm which is very simple computationally and suitable for online applications. A numerical simulation is exploited to illustrate the feasibility of the proposed filtering algorithm.     

含未知参数的自校正融合Kalman滤波器及其收敛性

对于带未知模型参数和噪声方差的多传感器系统,基于分量按标量加权最优融合准则,提出了自校正解耦融合Kalman滤波器,并应用动态误差系统分析(Dynamic error system analysis,DESA)方法证明了它的收敛性.作为在信号处理中的应用,对带有色和白色观测噪声的多传感器多维自回归(Autoregressive,AR)信号,分别提出了AR信号模型参数估计的多维和多重偏差补偿递推最小二乘(Bias compensated recursive least-squares,BCRLS)算法,证明了两种算法的等价性,并且用DESA方法证明了它们的收敛性.在此基础上提出了AR信号的自校正融合Kalman滤波器,它具有渐近最优性.仿真例子说明了其有效性.     

Distributed fusion filter for multi-sensor systems with finite-step correlated noises

This paper addresses the distributed fusion filtering problem for multi-sensor systems with finite-step correlated noises. The process noise and observation noises of different sensors are finite-step auto- and cross-correlated, respectively. Based on the optimal local filtering algorithms that we presented before, the filtering error cross-covariance matrices between any two local filters are derived based on an innovation analysis approach. A distributed fusion filter is put forward by using matrix-weighted fusion estimation algorithm in the linear unbiased minimum variance sense. Finally, the proposed algorithms are extended to systems with random parameter matrices. Two simulation examples are given to show the effectiveness of the proposed algorithms.     

Self-tuning weighted measurement fusion white noise deconvolution estimator and its convergence analysis

White noise deconvolution or input white noise estimation has a wide range of applications including oil seismic exploration, communication, signal processing, and state estimation. For the multisensor linear discrete time-invariant stochastic systems with correlated measurement noises, and with unknown ARMA model parameters and noise statistics, the on-line AR model parameter estimator based on the Recursive Instrumental Variable (RIV) algorithm, the on-line MA model parameter estimator based on Gevers–Wouters algorithm and the on-line noise statistic estimator by using the correlation method are presented. Using the Kalman filtering method, a self-tuning weighted measurement fusion white noise deconvolution estimator is presented based on the self-tuning Riccati equation. It is proved that the self-tuning fusion white noise deconvolution estimator converges to the optimal fusion steady-state white noise deconvolution estimator in a realization by using the dynamic error system analysis (DESA) method, so that it has the asymptotic global optimality. The simulation example for a 3-sensor system with the Bernoulli–Gaussian input white noise shows its effectiveness.     

New approach to information fusion steady-state Kalman filtering

By the modern time series analysis method, based on the autoregressive moving average (ARMA) innovation model, a unified and general information fusion steady-state Kalman filtering approach is presented for the general multisensor systems with different local dynamic models and correlated noises. It can handle the filtering, smoothing, and prediction fusion problems for state or signal. The optimal fusion rule weighted by matrices is re-derived as a weighted least squares (WLS) fuser, and is reviewed. An optimal fusion rule weighted by diagonal matrices is presented, which is equivalent to the optimal fusion rule weighted by scalars for components, and it realizes a decoupled fusion. The new algorithms of the steady-state Kalman estimator gains are presented. In order to compute the optimal weights, the formulas of computing the cross-covariances among local estimation errors by Lyapunov equations are presented. The exponential convergence of the iterative solution of Lyapunov equation is proved. It is proved that the optimal fusion estimators under three weighted fusion rules are locally optimal, but are globally suboptimal. The proposed steady-state Kalman fusers can reduce the on-line computational burden, and are suitable for real-time applications. A simulation example for the 3-sensor steady-state Kalman tracking fusion estimators shows their effectiveness and correctness, and gives the accuracy comparison of the fusion rules.     

Self-tuning weighted measurement fusion Kalman filter and its convergence

For multisensor systems, when the model parameters and the noise variances are unknown, the consistent fused estimators of the model parameters and noise variances are obtained, based on the system identification algorithm, correlation method and least squares fusion criterion. Substituting these consistent estimators into the optimal weighted measurement fusion Kalman filter, a self-tuning weighted measurement fusion Kalman filter is presented. Using the dynamic error system analysis (DESA) method, the convergence of the self-tuning weighted measurement fusion Kalman filter is proved, i.e., the self-tuning Kalman filter converges to the corresponding optimal Kalman filter in a realization. Therefore, the self-tuning weighted measurement fusion Kalman filter has asymptotic global optimality. One simulation example for a 4-sensor target tracking system verifies its effectiveness.     

基于EKF的集中式融合估计研究

以一类非线性多传感器动态系统为对象, 基于扩展Kalman滤波器(Extend Kalman filter, EKF)介绍三种典型非线性集中式融合算法, 并以此为基础研究部分线性动态系统融合理论在非线性系统中的推广与完善. 首先,利用EKF的一种信息滤波器形式(Extend information filter, EIF)给出测量值扩维融合、测量值加权融合和顺序滤波融合算法公式, 进而研究三种非线性融合算法的估计性能比较以及测量值融合更新次序是否满足可交换性. 结果表明: 当各传感器的测量特性相同时, 集中式测量值扩维和测量值加权融合算法的估计精度功能等价;非线性顺序滤波融合与其他两种融合算法之间不再具备线性多传感器系统中估计功能的完全等价特性;在融合精度不变前提下非线性顺序滤波融合中, 各传感器观测更新次序不再完全满足可交换性. 4个基于纯方位目标跟踪的数值仿真被用来验证文中所得结论的有效性和正确性.     

广义系统Wiener状态滤波新算法

应用时域上的现代时间序列分析方法，基于ARMA新息模型和白噪声估计理论，由一种新的非递推最优状态估值器的递推变形，提出了广义系统Wiener状态滤波的一种新算法，它可统一处理滤波、平滑和预报问题，且具有渐近稳定性。同某些算法相比，它避免了求解Riccati方程和Diophantine方程，且避免了计算伪逆，因而减小了计算负担。仿真例子说明了其有效性。     

多传感器最优信息融合白噪声反卷积滤波器

应用现代时间序列分析方法和白噪声估计理论,基于线性最小方差意义下按标量加权最优信息融合准则,对于带白色和有色观测噪声的多传感器单通道系统,提出了分布式融合白噪声反卷积滤波器.它由局部白噪声反卷积滤波器加权构成.可统一处理融合滤波、平滑和预报问题.给出了计算局部滤波误差互协方差公式,可用于计算最优加权.同单传感器情形相比,可提高融合滤波器精度.它可应用于石油地震勘探信号处理.一个3传感器信息融合Bernou lli-Gaussian白噪声反卷积滤波器的仿真例子说明了其有效性.     

A new information fusion white noise deconvolution estimator

The white noise deconvolution or input white noise estimation problem has important applications in oil seismic exploration, communication and signal processing. By the modern time series analysis method, based on the autoregressive moving average （ARMA） innovation model, a new information fusion white noise deconvolution estimator is presented for the general multisensor systems with different local dynamic models and correlated noises. It can handle the input white noise fused filtering, prediction and smoothing problems, and it is applicable to systems with colored measurement noises. It is locally optimal, and is globally suboptimal. The accuracy of the fuser is higher than that of each local white noise estimator. In order to compute the optimal weights, the formula computing the local estimation error cross-covariances is given. A Monte Carlo simulation example for the system with Bernoulli-Gaussian input white noise shows the effectiveness and performances.