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。     

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     

带反馈信息的多传感器分层估计算法

为了改善局部节点和传感器级的跟踪性能,本文研究带反馈信息的多级式多传感器系统中的状态估计技术.在给出有反馈信息情况下传感器级状态估计解的基础上,本文提出多坐标系中有反馈信息的两层集中、分布和混合估计方程.在不同笛卡尔坐标系中,本文提出了几种带反馈信息的三层多传感器系统中的航迹级融合方法,其中包括集-分估计、分-分估计和混-分估计,并以定理的形式证明有、无反馈信息情况下的两类三层状态估计是等价的、最优的.仿真结果表明,在多传感器信息融合系统中引入反馈机制可以明显改善一些局部节点和传感器级的跟踪精度.     

分布式多传感器多目标跟踪方法综述

多传感器多目标跟踪是信息融合领域的热点问题,其通过融合多个局部传感器数据,提高目标跟踪精度和稳定性。多传感器多目标跟踪按融合体系可分为分布式、集中式、混合式3类,其中分布式融合结构对网络通信带宽要求低、可靠性和稳定性强,广泛应用于军事、民用领域。该文聚焦分布式多传感器多目标跟踪涉及的目标跟踪、传感器配准、航迹关联、数据融合4项关键技术,主要分析了各关键技术的理论原理与适用条件,重点介绍了不完整测量条件下的空间配准与航迹关联,并给出仿真结果。最后,该文总结了现有分布式多传感器多目标跟踪关键技术存在的问题,并指出了其未来发展趋势。      

Distributed target classification and tracking in sensor networks

The highly distributed infrastructure provided by sensor networks supports fundamentally new ways of designing surveillance systems. In this paper, we discuss sensor networks for target classification and tracking. Our formulation is anchored on location-aware data routing to conserve system resources, such as energy and bandwidth. Distributed classification algorithms exploit signals from multiple nodes in several modalities and rely on prior statistical information about target classes. Associating data to tracks becomes simpler in a distributed environment, at the cost of global consistency. It may be possible to filter clutter from the system by embedding higher level reasoning in the distributed system. Results and insights from a recent field test at 29 Palms Marine Training Center are provided to highlight challenges in sensor networks.     

测量噪声相关线性系统异类传感器航迹融合

研究了异类传感器航迹融合问题。在测量噪声相关的条件下,利用线性无偏最小方差估计的基本理论,通过对异类传感器的状态估计采用顺序滤波的方法,得到了相关测量噪声线性系统异类传感器测量融合算法和状态矢量融合算法。计算机数字仿真结果表明,由于考虑了测量噪声之间的相关性,该算法比噪声不相关融合算法具有更好的跟踪性能,航迹跟踪的精度得到了改善。     

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。     

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.     

Distributed Weighted Fusion Estimators with Random Delays and Packet Dropping

This paper is concerned with the distributed fusion estimation in sensor networks where local estimates are sent to a fusion centre for fusion estimation, with random delays and packet dropouts. Under the linear minimum variance sense, a distributed optimal weighted fusion estimator is given for discrete time-invariant stochastic linear systems with multiple distributed sensors. The algorithm involves a weighted fusion of local predictors with different prediction steps from different sensor sources. A recursive computation of the cross-covariance matrix of prediction errors between any two local estimates is derived. We present two fusion strategies. One is to fuse the latest local estimates that reach the fusion centre at the current time. The other is to fuse the latest local estimates that reach the fusion centre at the current time and the predicted estimates of those that do not have estimates received at the current time. Further, to reduce the computation cost, only the local estimates satisfying the given precision requirement are fused because those with longer delays or consecutive packet dropouts have large estimation errors. A strategy to select local estimators for fusion is presented based on gate thresholds of time delays or the numbers of consecutive packet dropouts for all local estimators. This method can be implemented offline. Simulation for a tracking system with four sensors shows the effectiveness of the proposed approaches.     

母体均值区间估计用于多传感器数据关联算法研究

为了提高多传感器数据融合的精度,数据或航迹关联成为对目标跟踪滤波的关键。分析了用于多传感器数据融合的集中式与分布式卡尔曼滤波器,提出了一种新的基于数理统计理论的数据关联算法——正态母体均值区间估计算法,当传感器性能及干扰噪声的统计特性发生突变时,能大大提高数据融合的精度和准确性。     

Implementation of a Decentralised Sensing Network aboard Multiple UAVs

This paper presents real-time results of a decentralised airborne data fusion system tracking multiple ground based targets. These target estimates are then used to construct a composite map of the environment. A decentralised communication strategy which is robust to communication latencies and dropouts results in each sensing node having a local estimate using global information. In addition, this paper describes the hardware and algorithms for the sensing nodes used in this demonstration. The problems introduced by locating the sensing nodes on air vehicles are both interesting and challenging.     

基于算术平均融合的分布式多伯努利扩展目标跟踪

在分布式传感网络中,由于同一扩展目标的方位角以及轴长等状态参数在不同传感器下估计结果不一致,因此多扩展目标估计关联困难,从而为后续密度信息融合带来了巨大挑战。相比于点目标后验密度信息,扩展目标后验密度同时包含了质心状态和外形信息。该文结合质心欧氏距离和外形矩阵非欧氏尺寸-形状度量提出了椭圆距离(ED),该椭圆距离同时考虑了扩展目标质心状态与外形信息,更好地实现了不同传感器下同一扩展目标后验密度关联。此外该文在算术平均(AA)融合规则下推导了融合空间密度的近似伽马高斯逆威沙特(GGIW)分布,实现了不同传感器下同一扩展目标后验信息AA融合。仿真实验表明,该文所提算法在分布式传感网络中能有效地进行多扩展目标跟踪。     

一种基于多传感器的复合量测IMM-EKF数据融合算法

为了提高多传感器系统的目标跟踪精度,且解决传感器数量多导致的耗时长的问题,提出了一种复合量测IMM-EKF（Interacting Multiple Model-Extended Kalman Filter）融合算法.该算法根据各传感器的测量精度,对各传感器关于同一目标的量测点迹进行加权融合,再将融合后的点迹进行IMM-EKF滤波处理.通过仿真及实验数据处理,将复合量测IMM-EKF融合算法与加权IMM-EKF融合算法、扩维IMM-EKF融合算法进行了对比分析,比较了三种算法的跟踪精度及耗时长度.结果表明,扩维IMM-EKF融合算法具有最优的跟踪精度,复合量测IMM-EKF融合算实时性最好.最后分别给出了三种算法的适用场合.     

多传感器分布式推广卡尔曼滤波算法及其在雷达红外目标跟踪中的应用

本文提出了一种用于非线性系统的多传感器分布式推广卡尔曼滤波算法,该算法中系统的动态方程和传感器的观测方程分别围绕全局估计和全局预测线性化,融合中心基于所有传感器观测的全局估计由各传感器基于自身观测的局部估计来重构。算法分析说明,全局估计的精度高、误差小。最后介绍了文中算法在雷达和红外两种传感器跟踪机动目标中的应用,仿真结果验证了该算法的有效性。     

基于传感器网络的多目标跟踪和特征管理方法

针对传感器网络下多目标跟踪时目标数量不断变化这一复杂情况,文中对多目标的跟踪和特征管理方法进行了研究。该方法由数据关联、多目标跟踪、特征管理,和信息融合所组成。其中未知数量多目标的跟踪和数据关联通过马尔科夫蒙特卡罗数据关联实现。通过信息融合来整合本地信息,获取所有相邻传感器的本地一致性,最终实现特征管理。试验证明,本方法能够在分布式的传感器网络环境下对多目标进行准确有效地跟踪和特征管理。     

含状态等式约束的分布式估计融合

在假设各传感器间观测噪声不相关的基础上,将最优线性无偏估计意义下的分布式最优估计融合公式推广至含状态等式约束的分布式估计融合问题中,并在此约束条件下,先将该约束系统直接转换为投影系统,再利用投影系统中的Kalman滤波估计,分别讨论了两种信息融合方式:中心式与分布式融合。进一步地指出中心式估计融合优于分布式估计融合,并在分布式融合结构下,得到了约束的Kalman滤波估计优于投影的Kalman滤波估计的结论。通过数值模拟论证了两种融合方式下的性能差异。     

分布式无线传感网络的协作目标跟踪策略

基于图像序列的目标跟踪是目标跟踪的重要研究问题之一.由于受图像解析度和跟踪范围限制,单视角跟踪准确性和鲁棒性不足.本文提出了分布式无线传感网络测量环境下的多视角协作融合跟踪方法,并引入了渐进分布式数据融合,采用基于能耗参数和信息有效性参数的综合优化函数动态选择融合节点,规划融合过程,平衡融合精度与网络能耗.通过目标跟踪准确性、网络能耗及传输延时对比实验表明:基于渐进分布式数据融合的协作信号处理方法提高了分布式多视角跟踪的准确性与实时性,减少了网络拥塞,降低了通讯能耗及延时.     

Decentralized State Initialization with Delay Compensation for Multi-modal Sensor Networks

Decentralized processing algorithms are attractive alternatives to centralized algorithms for target tracking applications in smart sensor networks since they provide the ability to scale, reduce vulnerability, reduce communication, and share processing responsibilities among individual nodes. Sharing the processing responsibilities allows parallel processing of raw data at the individual nodes. However, this introduces other difficulties in multi-modal smart sensor networks, such as non-observability of the targets’ states at any individual node and various delays such as varying processing delays, communication delays and signal propagation delays for the different modalities. In this paper, we provide a novel algorithm to determine the initial probability distribution of multiple targets’ states in a decentralized manner. The targets’ state vectors consist of the targets’ positions and velocities on the 2D plane. Our approach can determine the state vector distribution even if the individual sensors alone are not capable of observing it. Our approach can also compensate for varying delays among the assorted modalities. The resulting distribution can be used to initialize various tracking algorithms. Our approach is based on Monte Carlo methods, where the state distributions are represented by a weighted set of discrete state realizations. A robust weighting strategy is formulated to account for missed detections, clutter and estimation delays. To demonstrate the effectiveness of the algorithm, we simulate a network with direction-of-arrival nodes and range-Doppler nodes.     

Data fusion for ballistic targets tracking using least squares

The paper deals with the problem of state estimation of continuous-time nonlinear system using discrete-time measurements from multiple sensors. In particular, the problem of multi-radar tracking of artillery ballistic objects is considered. A batch estimator based on the iterative least squares approach is developed using simplified and accurate models of ballistic flight. The estimator is applied to process the sequences of measurements from radars tracking the same ballistic target. Estimates of the target state over time are computed and their accuracy is compared to the estimates yielded by the extended Kalman filter. Partial estimates from multiple radars are combined using track fusion approach and propagated using the 3 degree of freedom model of ballistic flight. Accuracy of target's firing point estimation is also analysed with respect to the data rates and locations of the radars with respect to the target. Practical aspects of the proposed method are also discussed.     

基于LMMSE估计的多传感器目标航迹融合

介绍一种基于线性最小均方差（LMMSE）估计的多传感器目标航迹融合方法。这种方法适于分布式多传感器目标跟踪系统，它具有LMMSE估计器的许多优点。同时，由于它考虑了因公共的目标动态过程噪声引起的估计误差的非独立性的影响，因此，它比基于独立性假设的方法更精确。