带有色观测噪声系统多传感器标量加权最优信息融合稳态Kalman滤波器

基于标量加权多传感器线性最小方差最优信息融合准则,对被多传感器观测的带有色观测噪声的离散线性随机控制系统,提出了一种具有两层融合结构的标量加权信息融合稳态Kalman滤波器,它等价于相应的带相关噪声系统的最优信息融合稳态Kalman预报器.最优信息融合稳态预报器可在所有局部预报器达到稳态时,通过一次融合获得,且任两个子系统之间的稳态预报误差互协方差阵可通过任选初值迭代求得,并证明了它的收敛性.通过将它应用到带三个传感器的雷达跟踪系统验证了其有效性.     

两种最优观测融合方法的功能等价性

对于基于K alm an滤波的多传感器数据融合,有两种最优观测融合方法:第一种是集中式观测融合方法,它是通过增加观测向量的维数合并多传感器数据,而第二种是分布式观测融合方法,它是在线性最小方差准则下,通过加权合并多传感器数据,但观测向量维数不变.在数据融合所用的传感器带有相同观测阵的情形下,本文用K alm an证明了两种观测融合方法是完全功能等价的,即用两种方法得到的K alm an估值器(滤波器,预报器,平滑器),信号估值器和白噪声估值器分别在数值上是相等的.在这种情形下,第二种方法不仅可给出像第一种方法一样的全局最优融合估计,而且可明显减小计算负担,便于实时应用.一个数值例子说明了其正确性.     

标量及对角阵加权的序贯估计融合算法研究

针对多传感器分布式估计融合系统,在最小化估计误差的协方差矩阵迹的准则下,采用标量加权及对角阵加权融合方法,提出了估计误差相关条件下的序贯处理式最优估计融合Kalman滤波器。该融合滤波器以两传感器估计融合算法为基础,对传感器采集信息依次进行融合计算,得到多传感器融合结果。比较两种算法与局部滤波器的估计精度,并进行了仿真。仿真结果表明了基于加权估计融合的序贯处理算法的可行性和有效性。     

多传感器时滞系统信息融合最优Kalman滤波器

基于线性最小方差最优加权融合估计算法,对多传感器的离散线性状态时滞随机系统,给出了一种非增广分布式加权融合最优Kalman滤波器.推导了状态时滞系统任两个传感器子系统之间的滤波误差互协方差阵的计算公式.它与状态增广加权融合滤波器具有相同的精度.与每个传感器的局部滤波器相比,分布式融合滤波器具有更高的精度.与状态和观测增广最优滤波器相比,具有较小的精度.但避免了增广所带来的高维计算和大的空间存储,可减小计算负担.仿真例子验证了其有效性.     

带有色观测噪声多传感器多重时滞系统分布式融合滤波器

基于新息分析方法, 对带有色观测噪声的多重时滞系统, 提出了一种带白噪声估值器的非增广的最优滤波器. 它等价于一个带相关白噪声多重时滞系统的一步预报器. 当系统带有多个传感器时, 推导了多重时滞系统的任意两个传感器子系统之间的估计误差互协方差阵. 基于线性最小方差最优加权融合估计算法, 给出了分布式加权融合最优滤波器. 分布式融合估计比基于每个传感器的局部估计具有更高的精度. 比增广的集中式最优滤波器具有更好的可靠性, 且避免了高维计算和大存储空间. 仿真例子验证了其有效性.     

分布式贝叶斯检测融合优化算法及其计算机仿真

该文研究了利用分布式多传感器获得全局决策的分布式信号检测问题。在这种检测系统中各传感器将其各自关于观测对象的决策传送至融合中心,融合中心根据融合规则给出全局决策。研究重点是基于贝叶斯准则的分布式并联检测融合系统的数据融合理论,给出了使系统全局最优的融合规则和传感器决策规则,提出了对融合规则和传感器决策规则进行优化计算的非线性高斯一赛德尔算法,具体讨论了两相同传感器、两个不同传感器和三个相同传感器在具有独立观测时的数据融合问题。给出了利用本文所提算法对上述几种情况进行计算机仿真的仿真实例。仿真结果表明：融合系统的性能相对传感器有显著改善,采用三个相同传感器的融合系统,其贝叶斯风险下降了26．5％。     

多传感器分布式区间估计融合

考虑了对未知参数θ的多传感器分布式区间估计融合问题. 建立了一种最优区间估计融合模型———凸线性组合融合, 并给出搜索最优权系数的Gauss Seidel迭代算法, 另外, 给出了一种近似的区间估计融合, 它能减少大量的计算量, 并且在某些情况下可以达到最优的估计性能. 最后采用计算机数值模拟, 用以上方法得到的融合区间估计均优于每个传感器的区间估计的性能.     

基于LMS算法的多传感器数据加权融合方法

针对目前多传感器数据融合过程中,传感器观测噪声不易确定,提出了一种基于LMS算法的多传感器自适应加权数据融合方法。该方法将传感器最优加权系数的求解,转化为估计值的均方误差性能表面的最优解搜索,通过加入自适应阶段,采用自适应最小均方误差（LMS）算法调整传感器加权系数。仿真结果表明该方法的有效性。     

基于模糊特征属性参数最优融合的目标识别

为了实现在不确定环境下的自动目标识别,提高系统的性能和可靠性.首先采用模糊数来刻画传感器的输出报告,将每个传感器报告用三角模糊数来表示;然后提出基于模糊特征属性参数的最优融合算法来实现信息融合,并将其应用到多传感器目标自动识别系统.融合算法以模糊信息相似性测度为基础,最优融合准则是:融合后的数据与各个传感器输入数据冲突应该最小.通过最优准则确定了各个传感器的权重以及融合结果,且融合结果与初始传感器权重向量和传感器报告融合的次序无关.文中给出了具体的算法流程和一个应用实例.     

无线传感器网络中基于衰减信道的决策融合

无线传感器网络中的链路通信质量对上层应用有重要的影响.为此,本文研究了基于衰减信道的多传感器决策融合问题,提出了最优似然比融合规则,并推导出适用于无线传感器网络的二种次优融合规则,最后讨论了相同传感器和相同信道模型下的融合情况.仿真结果表明此方法能比较好的适应在无线传感器网络中信息融合时所遇到的链路衰减和噪声干扰等问题.     

Stochastic Event Capture Using Mobile Sensors Subject to a Quality Metric

Mobile sensors cover more area over a fixed period of time than do the same number of stationary sensors. However, the quality of coverage (QoC) achieved by mobile sensors depends on the velocity, mobility pattern, number of mobile sensors deployed, and the dynamics of the phenomenon being sensed. The gains attained by mobile sensors over static sensors and the optimal motion strategies for mobile sensors are not well understood. In this paper, we consider the following event capture problem: the events of interest arrive at certain points in the sensor field and disappear according to known arrival and departure time distributions. An event is said to be captured if it is sensed by one of the mobile sensors before it fades away. We analyze how the QoC scales with velocity, path, and number of mobile sensors. We characterize cases where the deployment of mobile sensors has no advantage over static sensors, and find the optimal velocity pattern that a mobile sensor should adopt. We also present algorithms for two motion planning problems: 1) for a single sensor, what is the sensor trajectory and the minimum speed required to satisfy a bound on the event loss probability and 2) for sensors with fixed speed, what is the minimum number of sensors required to satisfy a bound on the event loss probability. When the robots are restricted to move along a line or a closed curve, our algorithms return the optimal velocity for the minimum velocity problem. For the minimum sensor problem, the number of sensors used is within a factor of 2 of the optimal solution. For the case where the events occur at arbitrary points on a plane, we present heuristic algorithms for the aforementioned motion planning problems and bound their performance with respect to the optimal.     

基于最小二乘准则的多传感器参数估计数据融合

为了从含有加性测量噪声的线性测量数据中更加准确地估计未知的常值参数,测量噪声互不相关的多传感器测量系统得到广泛使用。在最小二乘准则下,提出了多传感器测量系统在多次同步测量时的集中式和分布式参数估计数据融合算法,两种算法完全等价,且都是全局最优的。数值仿真实验的结果表明,新算法可以明显改善传感器测量参数的估计精度。     

Multisensor fusion for linear control systems with asynchronous, Out-Of-Sequence and erroneous data

This paper presents a set of new centralized algorithms for estimating the state of linear dynamic Multiple-Input Multiple-Output (MIMO) control systems with asynchronous, non-systematically delayed and corrupted measurements provided by a set of sensors. The delays, which make the data available Out-Of-Sequence (OOS), appear when using physically distributed sensors, communication networks and pre-processing algorithms. The potentially corrupted measurements can be generated by malfunctioning sensors or communication errors. Our algorithms, designed to work with real-time control systems, handle these problems with a streamlined memory and computational efficient reorganization of the basic operations of the Kalman and Information Filters (KF & IF). The two versions designed to deal only with valid measurements are optimal solutions of the OOS problem, while the other two remaining are suboptimal algorithms able to handle corrupted data.     

Optimal sensor placement and motion coordination for target tracking

This work studies optimal sensor placement and motion coordination strategies for mobile sensor networks. For a target-tracking application with range sensors, we investigate the determinant of the Fisher Information Matrix and compute it in the 2D and 3D cases, characterizing the global minima in the 2D case. We propose motion coordination algorithms that steer the mobile sensor network to an optimal deployment and that are amenable to a decentralized implementation. Finally, our numerical simulations illustrate how the proposed algorithms lead to improved performance of an extended Kalman filter in a target-tracking scenario.     

多传感器异步线性测量系统的数据融合

由于采样速率和传送数据到融合中心的通信延迟的不同,现代工业生产过程中用于对未知的常值或缓变参数进行估计的多传感器通常是异步工作的,且受到加性测量噪声的干扰。在最小二乘估计意义下,对于测量噪声互不相关的多传感器异步线性测量系统,提出了集中式和分布式递推参数估计数据融合算法,两种算法完全等价,且都是全局最优的。数值仿真实验的结果表明,通过利用多传感器的测量数据,增大了对参数测量的数据流和数据率,传感器测量参数的估计准确度得到明显改善。     

The sufficient condition for lossless linear transformation for distributed estimation with cross-correlated measurement noises

In most distributed fusion algorithms, the measurement noises in different sensors are often assumed to be uncorrelated, but in practical occasions the assumption may not be met and the measurement noises are often cross-correlated between sensors. So the lossless distributed fusion algorithms with the assumption of uncorrelated measurement noises usually cannot keep their lossless performance in practical applications. Therefore, in the case of cross-correlated measurement noises, the lossless compression rule for distributed estimation is proposed. We prove in theory that the sufficient condition of the lossless compression is the transformation matrix is of full column rank. Using the transformation matrix constructed by the proposed rule, the distributed fusion can achieve the performance of the centralized one. In addition, under this rule two optimal fusion algorithms are proposed and their performances are analyzed.     

On efficient sensor scheduling for linear dynamical systems

Consider a set of sensors estimating the state of a process in which only one of these sensors can operate at each time-step due to constraints on the overall system. The problem addressed here is to choose which sensor should operate at each time-step to minimize a weighted function of the error covariances of the state estimates. This work investigates the development of tractable algorithms to solve for the optimal and suboptimal sensor schedules. A condition on the non-optimality of an initialization of the schedule is developed. Using this condition, both an optimal and a suboptimal algorithm are devised to prune the search tree of all possible sensor schedules. The suboptimal algorithm trades off the quality of the solution and the complexity of the problem through a tuning parameter. The performance of the suboptimal algorithm is also investigated and an analytical error bound is provided. Numerical simulations are conducted to demonstrate the performance of the proposed algorithms, and the application of the algorithms in active robotic mapping is explored.     

Approximate Nonmyopic Sensor Selection via Submodularity and Partitioning

As sensors become more complex and prevalent, they present their own issues of cost effectiveness and timeliness. It becomes increasingly important to select sensor sets that provide the most information at the least cost and in the most timely and efficient manner. Two typical sensor selection problems appear in a wide range of applications. The first type involves selecting a sensor set that provides the maximum information gain within a budget limit. The other type involves selecting a sensor set that optimizes the tradeoff between information gain and cost. Unfortunately, both require extensive computations due to the exponential search space of sensor subsets. This paper proposes efficient sensor selection algorithms for solving both of these sensor selection problems. The relationships between the sensors and the hypotheses that the sensors aim to assess are modeled with Bayesian networks, and the information gain (benefit) of the sensors with respect to the hypotheses is evaluated by mutual information. We first prove that mutual information is a submodular function in a relaxed condition, which provides theoretical support for the proposed algorithms. For the budget-limit case, we introduce a greedy algorithm that has a constant factor of (1 - 1/e) guarantee to the optimal performance. A partitioning procedure is proposed to improve the computational efficiency of the algorithms by efficiently computing mutual information as well as reducing the search space. For the optimal-tradeoff case, a submodular-supermodular procedure is exploited in the proposed algorithm to choose the sensor set that achieves the optimal tradeoff between the benefit and cost in a polynomial-time complexity.     

具有反馈延迟特性系统的最优控制器设计及其算法实现

针对反馈传感器具有延迟特性的一类系统,提出了三自由度Wiener- Hopf最优控制器设计方法,并给出了状态空间实现算法．最后通过仿真实例证明了该方法的有效性．     

Asymptotically efficient parameter estimation using quantized output observations

This paper studies identification of systems in which only quantized output observations are available. An identification algorithm for system gains is introduced that employs empirical measures from multiple sensor thresholds and optimizes their convex combinations. Strong convergence of the algorithm is first derived. The algorithm is then extended to a scenario of system identification with communication constraints, in which the sensor output is transmitted through a noisy communication channel and observed after transmission. The main results of this paper demonstrate that these algorithms achieve the Cramér-Rao lower bounds asymptotically, and hence are asymptotically efficient algorithms. Furthermore, under some mild regularity conditions, these optimal algorithms achieve error bounds that approach optimal error bounds of linear sensors when the number of thresholds becomes large. These results are further extended to finite impulse response and rational transfer function models when the inputs are designed to be periodic and full rank.