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。     

Optimal information fusion distributed smoother for discrete multichannel ARMA signals

Based on the multisensor optimal information fusion criterion weighted by matrices in the linear minimum variance sense, using estimators of white measurement noise, an optimal information fusion distributed Kalman smoother is given for discrete time multichannel autoregressive moving average (ARMA) signals with correlated noise. It has a three-layer fusion structure with a fault tolerant property. The first and the second fusion layers both have netted parallel structures to determine cross-covariance matrices between any two faultless sensors. The third fusion layer is the fusion centre to determine the optimal weights and obtain the optimal fusion smoother. The fusion smoother has higher precision than that of any local smoother. Its effectiveness is shown by applying it to a double-channel signal system with three sensors.     

Optimal linear estimation fusion .I. Unified fusion rules

This paper deals with data (or information) fusion for the purpose of estimation. Three estimation fusion architectures are considered: centralized, distributed, and hybrid. A unified linear model and a general framework for these three architectures are established. Optimal fusion rules based on the best linear unbiased estimation (BLUE), the weighted least squares (WLS), and their generalized versions are presented for cases with complete, incomplete, or no prior information. These rules are more general and flexible, and have wider applicability than previous results. For example, they are in a unified form that is optimal for all of the three fusion architectures with arbitrary correlation of local estimates or observation errors across sensors or across time. They are also in explicit forms convenient for implementation. The optimal fusion rules presented are not limited to linear data models. Illustrative numerical results are provided to verify the fusion rules and demonstrate how these fusion rules can be used in cases with complete, incomplete, or no prior information.     

A Bernoulli Optimal Kalman Filter for a Multi-sensor System with Random Data Packet Dropouts and Delays

Wireless Personal Communications - A distributed filtering method is proposed to solve the packet dropouts and delays in a multi-sensor wireless sensor network. For an asynchronous multi-sensor...     

A Novel Distributed Sensor Fusion Algorithm for RSSI-Based Location Estimation Using the Unscented Kalman Filter

We address the Bayesian sensor fusion approach for distributed location estimation in the wireless sensor network. Assume each sensor transmits local calculation of target position to a fusion center, which then generates under a Bayesian framework the final estimated trajectory. We study received signal strength indication-based approach using the unscented Kalman filter for each sensor to compute local estimation, and propose a novel distributed algorithm which combines the soft outputs sent from selected sensors and computes the approximated Bayesian estimates to the true position. Simulation results demonstrate that the proposed soft combining method can achieve similar tracking performance as the centralized data fusion approach. The computational cost of the proposed algorithm is less than the centralized method especially in large scale sensor networks. In addition, it is straightforward to incorporate the proposed soft combining strategy with other Bayesian filters for the general purpose of data fusion.

     

多级式多传感器信息融合中的状态估计

本文多级式传感器监视系统中的状态估计技术,基于背地里传感器Ｋａｌｍａｎ滤波方程,两级集中和分布估计解,本文提出多坐标系中多级式多传感器跟踪系统的三层集中估计方法,在不同笛卡尔坐标系中,本文提出了几种适合于三层多传感器信息融合系统的航迹级融合方法,其中既包括了集－分估计,也包括了分－分估计组合问题,在离散线性假设下,各层估计解都是最优的并且对同一问题的不同表现形式是等价的,另外,文中还给出多级式多传     

多传感器混合多模型估计的误差互相关性及其融合算法研究

 对局部节点状态估计间误差相关性的处理是分布式估计融合或航迹融合的关键要素;针对当前分布式融合理论中关于混合多模型估计融合研究的空白,首先推导得出了采用相同模型成分的各局部节点交互多模型状态估计的误差互协方差矩阵的递推计算方法;其次,讨论了所得非对称实误差互协方差矩阵的正定特性,并分析了此类误差相关性与混合多模型估计算法中模型过程噪声之间的变化关系;上述结果使得基于互协方差组合融合算法的交互多模型状态估计融合成为可能,仿真实验亦验证了其有效性,相对其它不考虑误差相关性的融合算法,融合结果也更为真实。     

Near-optimal range and depth estimation using a vertical array in acorrelated multipath environment

This paper proposes a near-optimal procedure to localize a single stationary source in a two-path underwater acoustic environment. The investigation is for an M-element vertical array with omnidirectional sensors. The range and depth estimators are developed using a linear least-squares technique when a set of auto- and cross-correlators is used for time difference of arrival (TDOA) estimates. A weighting matrix is derived to achieve the approximate maximum likelihood (ML) performance of the weighted least-squares range and depth estimators. The expressions for error variances and covariances of the range and depth estimates are derived with a small error analysis technique. It is verified analytically that the error covariance matrix of the weighted least-squares solutions reaches the Cramer-Rao lower bound in the small error region. The correlation of the range and depth estimation errors is investigated. Results show that the range and depth estimation errors are highly correlated in a multipath environment. The accuracy properties of the proposed multipath localization procedure are analyzed using different array configurations. The results show that the performances of the range and depth estimators are significantly improved if the linear-dependent TDOA estimates are included for localizing and that the unweighted range and depth estimators, using the entire set of TDOAs, are approximately optimal for most of the applications. The theoretical development of error variance and covariance expressions of the range and depth estimates, which incorporates the correlation in the TDOA estimates, is corroborated with Monte Carlo simulations     

Information fusion Wiener filter for the multisensor multichannel ARMA signals with time-delayed measurements

For the multisensor multichannel autoregressive moving average (ARMA) signals with time-delayed measurements, a measurement transformation approach is presented, which transforms the equivalent state space model with measurement delays into the state space model without measurement delays, and then using the Kalman filtering method, under the linear minimum variance optimal weighted fusion rules, three distributed optimal fusion Wiener filters weighted by matrices, diagonal matrices and scalars are presented, respectively, which can handle the fused filtering, prediction and smoothing problems. They are locally optimal and globally suboptimal. The accuracy of the fuser is higher than that of each local signal estimator. In order to compute the optimal weights, the formulae of computing the cross-covariances among local signal estimation errors are given. A Monte Carlo simulation example for the three-sensor target tracking system with time-delayed measurements shows their effectiveness.     

带反馈信息的分布多传感器航迹融合算法

为了改善传感器级的跟踪性能,本文研究带反馈信息的多传感器状态估计技术。在给出有、无反馈信息情况下的局部节点状态估计解的基础上,该文提出多坐标系中有、无反馈信息情况下的航迹融合方程。并指出有、无反馈信息情况下的两种融合解是等价的、最优的。仿真结果表明,在分布式多传感器信息融合系统中引入反馈机制可以明显改善局部节点估计精度,其性能已接近融合中心。在集中和雷达反隐身系统中,就空间重叠、覆盖而论,融合系统局部节点一般选2至4个为宜。     

Distributed fusion architectures and algorithms for target tracking

Modern surveillance systems often utilize multiple physically distributed sensors of different types to provide complementary and overlapping coverage on targets. In order to generate target tracks and estimates, the sensor data need to be fused. While a centralized processing approach is theoretically optimal, there are significant advantages in distributing the fusion operations over multiple processing nodes. This paper discusses architectures for distributed fusion, whereby each node processes the data from its own set of sensors and communicates with other nodes to improve on the estimates, The information graph is introduced as a way of modeling information flow in distributed fusion systems and for developing algorithms. Fusion for target tracking involves two main operations: estimation and association. Distributed estimation algorithms based on the information graph are presented for arbitrary fusion architectures and related to linear and nonlinear distributed estimation results. The distributed data association problem is discussed in terms of track-to-track association likelihoods. Distributed versions of two popular tracking approaches (joint probabilistic data association and multiple hypothesis tracking) are then presented, and examples of applications are given.     

An active sensing method using estimated errors for multisensorfusion systems

An active sensing method for multisensor fusion systems with actuators is proposed. To realize active sensing with multiple sensors: (i) where to position sensors; (ii) how to associate data; and (iii) how to fuse data should be determined. The authors propose a new method mainly concerning (i). The method utilizes estimated errors of estimates to determine optimal sensor locations where useful data are expected to be obtained and effectively associated. As examples, the active sensing method is applied to multitarget tracking by a system with two hand-eye cameras, and visual and tactile fusion in a system with a camera and a tactile sensor. By using this method, the sensing strategy is optimized for the object of measurement     

Optimal distributed fusion algorithm with one-step out-of-sequence estimates

The transmission modes of multi-hop and broadcasting for Wireless Sensor Networks（WSN）often make random and unknown transmission delays appear,so multisensor data fusion based on delayed systems attracts intense attention from lots of researchers.The existing achievements for the delayed fusion all focus on Out-Of-Sequence Measurements（OOSM）problem which has many disadvantages such as high communication cost,low computational efficiency,huge computational complexity and storage requirement,bad real-time performance and so on.In order to overcome these problems occurred in the OOSM fusion,the Out-Of-Sequence Estimates（OOSE）are considered to solve the delayed fusion for the first time.Different from OOSM which belongs to the centralized fusion,the OOSE scheme transmits local estimates from local sensors to the central processor and is thus the distributed fusion;thereby,the OOSE fusion can not only avoid the problems suffered in the OOSM fusion but also make the design of fusion algorithm highly simple and easy.Accordingly,a novel optimal linear recursive prediction weighted fusion method is proposed for one-step OOSE problem in this letter.As a tradeoff,its fusion accuracy is slightly lower than that of the OOSM method because the current OOSM fusion is a smooth estimate and OOSE gets a prediction estimate.But,the smooth result of the OOSE problem also has good fusion accuracy.Performance analysis and computer simulation show that the total performance of the proposed one-step OOSE fusion algorithm is better than the current one-step OOSM fusion in the practical tracking systems.     

基于容积卡尔曼滤波的异质多传感器融合算法

针对机动目标跟踪系统建模中的非线性问题,提出一种基于容积卡尔曼滤波(CKF)的雷达与红外传感器融合算法。考虑到被估计系统对目标跟踪算法实时性与精度的要求,在容积滤波框架下构建了集中式量测融合(CMF)和分布式状态融合(DSF)两种结构形式。CMF结构采用最优加权方法,首先对雷达和红外两种异类传感器的方位角度量测信息进行融合,并将其与融合后的雷达径向距量测构建新的量测数据,进而通过CKF算法对机动目标进行跟踪。DSF结构则首先对雷达量测中径向距信息进行加权融合,并将融合结果作为红外传感器的虚拟径向距量测,以实现红外量测的扩维处理,进而对每组量测数据应用CKF进行分布式并行加权融合,获得目标运动状态的最终估计。仿真场景中,对两种融合方法的性能进行比较,理论分析与仿真实验验证了算法的可行性与有效性。     

两级混合多传感器信息融合中的状态估计

在混合式多传感器信息融合系统中,一部分传感器通过处理它们的数据产生局部航迹,另一部分传感器则只提供检测报告,这些航迹和检测报告被传送到融合中心完成航迹融合和组合滤波。本文提出适合于两级混合式多传感器系统的全局最优状态估计解。在这种结构中,融合中心首先需要融合来自L个传感器的局部估计,然后基于其它N-L个传感器的观测,利用Kalman滤波技术依次更新已融合的航迹。本文还考虑了各传感器分布在不同地理位置时的状态估计问题。     

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.     

Recursive least-squares quadratic smoothing from measurements with packet dropouts

The least-squares quadratic filtering and fixed-point smoothing problems of discrete-time stochastic signals from observations with multiple packet dropouts are addressed. It is assumed that the packet dropouts occur randomly and the latest measurement received successfully is processed for the estimation in case that the current measurement is dropped-out. This situation is modelled by introducing in the observation model a sequence of Bernoulli random variables whose values - one or zero - indicate if the current measurement is received or dropped-out, respectively, and whose probability distributions are known. A recursive estimation algorithm is deduced without requiring full knowledge of the state-space model generating the signal process, but only information about the dropout probabilities and the moments of the signal and noise processes involved. Defining a suitable augmented observation model, the quadratic estimation problem is reduced to the linear estimation problem based on the augmented observations, which is solved by using an innovation approach.     

基于分批估计的自适应加权数据融合算法

针对多传感器数据融合问题,文中提出了一种基于分批估计的自适应加权数据融合算法。该算法采用时间序列和空间序列对采集的数据分批求其方差,利用数据一致性检测对噪点进行剔除,进而得到自适应因子。随后采用自适应加权法对数据进行融合,得到预测值。文中模拟物联网数据进行仿真实验。结果表明,在处理数据时运用分批估计的自适应加权多传感器数据融合技术,能够提高传感器测量的精确度和系统的可靠性,基于分批估计的自适应加权平均法比传统自适应方法的均方根误差减少了10%,精度提高了2.3%。     

多传感器最优信息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,在线性最小方差按矩阵加权最优信息融合准则下,提出了带相关噪声系统多传感器信息融合白噪声反卷积滤波器.提出了各传感器滤波误差之间的协方差阵计算公式,可用于计算最优融合加权阵.同单传感器情形相比,可提高融合滤波精度.它可减少在线计算负担,便于实时应用.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Bernoulli-Gaussian白噪声反卷积滤波器的仿真例子说明了其有效性.     

多光电跟踪设备异步序贯分布式目标跟踪算法

针对多光电跟踪设备组网后出现的异步测量问题，提出了一种异步分布式序贯目标跟踪算法。该算法由局部滤波器和融合滤波器构成，先利用状态转换方法，将多光电跟踪设备节点及其邻节点的异步测量对齐到融合时刻，得到拟测量方程。随后，利用射影原理对拟测量方程和目标运动状态方程构成的目标跟踪系统，提出异步序贯局部滤波器来计算较为精确的局部滤波值。再以协方差交叉算法为基础，提出基于扩散策略的融合滤波器，对局部估计值进行融合计算，来提高目标跟踪精度，并降低组网后各光电跟踪设备节点融合估计值的差异程度。最后对所提出的算法进行了仿真实验，以验证其有效性。