Sensor selection for parameterized random field estimation in wireless sensor networks

We consider the random field estimation problem with parametric trend in wireless sensor networks where the field can be described by unknown parameters to be estimated. Due to the limited resources, the network selects only a subset of the sensors to perform the estimation task with a desired performance under the D-optimal criterion. We propose a greedy sampling scheme to select the sensor nodes according to the information gain of the sensors. A distributed algorithm is also developed by consensus-based ...     

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％。     

LSFR算法在多传感器分布式检测中的优化研究

在多传感器分布式检测系统中,常规融合规则算法要求传感器误差概率已知,且系统中传感器和融合中心同时优化存在一定困难.提出最小二乘融合规则(LSFR)算法,算法不依赖噪声环境稳定性以及传感器的虚警概率与检测概率,融合中心根据各个传感器的硬决策,得到全局的硬决策,并在传感器和融合中心处理达到最优时,获得最佳全局性能.仿真结果表明:对比似然比融合决策算法与Neyman Pearson融合规则(NPFR)算法,LSFR算法全局检测概率显著提高,且在不同数量规模传感器和更多类型的分布式检测系统中具有较好兼容性.     

传感器网络中的分布式滚动时域状态估计

利用分布式滚动时域方法对无线传感器网络的状态估计问题进行研究,给出了基于量化测量值的滚动时域估计算法。在无线传感器网络的环境下处理分布式状态估计问题时,减少通信的成本是非常重要的一个环节,需要将观测值量化后再传送。以往的滚动时域估计方法无法处理量化观测值的状态估计问题,而本文的方法考虑了最严格的观测值量化情况即传感器只发送一个比特至融合中心的状态估计问题。与其它传感器网络中的状态估计方法相比,该方法减少了每一步的计算量。仿真结果验证了该算法的有效性。     

Optimal decision fusion given sensor rules

When all the rules of sensor decision are known,the optimal distributed decision fusion,which relies only on the joint conditional probability densities, can be derived for very general decision systems. They include those systems with interdependent sensor observations and any network structure. It is also valid for m-ary Bayesian decision problems and binary problems under the Neyman-Pearson criterion. Local decision rules of a sensor withfrom other sensors that are optimal for the sensor itself are also presented, which take the form of a generalized likelihood ratio test. Numerical examples are given to reveal some interesting phenomem that communication between sensors can improve performance of a senor decision,but cannot guarantee to improve the global fusion performance when sensor rules were given before fusing.     

Optimal decision f usion given sens or rules

When all the rules of sensor decision are known ,the optimal distributed decision fusion ,which relies only on the joint conditional probability densities , can be derived for very general decision systems. They include those systems with interdependent sensor observations and any network structure. It is also valid for m-ary Bayesian decision problems and binary problems under the Neyman- Pearson criterion. Local decision rules of a sensor with communication from other sensors that are optimal for the sensor itself are also presented ,which take the form of a generalized likelihood ratio test . Numerical examples are given to reveal some interesting phenomena that communication between sensors can improve performance of a senor decision ,but cannot guarantee to improve the global fusion performance when sensor rules were given before fusing.     

Approximate stochastic dynamic programming for sensor scheduling to track multiple targets

The problem of sensor scheduling is to select the number and combination of sensors to activate over time. The goal is usually to trade off tracking performance and sensor usage. We formulate a version of this problem involving multiple targets as a partially observable Markov decision process, and use this formulation to develop a nonmyopic sensor-scheduling scheme. Our scheme integrates sequential multisensor joint probabilistic data association and particle filtering for belief-state estimation, and use a simulation-based Q-value approximation method called completely observable rollout for decision making. We illustrate the effectiveness of our approach by an example with multiple sensors activated simultaneously to track multiple targets. We also explore the trade-off between tracking error and sensor cost using our nonmyopic scheme.     

Sensor Placement for Sensing Coverage and Data Precision in Wireless Sensor Networks

We present a novel paradigm of sensor placement concerning data precision and estimation. Multiple abstract sensors are used to measure a quantity of a moving target in the scenario of a wireless sensor network. These sensors can cooperate with each other to obtain a precise estimate of the quantity in a real-time manner. We consider a problem on planning a minimum-cost scheme of sensor placement with desired data precision and resource consumption. Measured data is modeled as a Gaussian random variable with a changeable variance. A gird model is used to approximate the problem. We solve the problem with a heuristic algorithm using branch-and-bound method and tabu search. Our experiments demonstrate that the algorithm is correct in a certain tolerance, and it is also efficient and scalable.     

Motion estimation of 3-D objects using multisensor data fusion

This article presents an approach to estimate the general 3-D motion of a polyhedral object using multiple sensor data some of which may not provide sufficient information for the estimation of object motion. Motion can be estimated continuously from each sensor through the analysis of the instantaneous state of an object. The instantaneous state of an object is specified by the rotation, which is defined by a rotation axis and rotation angle, and the displacement of the center of rotation. We have introduced a method based on Moore-Penrose pseudoinverse theory to estimate the instantaneous state of an object, and a linear feedback estimation algorithm to approach the motion estimation. The motion estimated from each sensor is fused to provide more accurate and reliable information about the motion of an unknown object. The techniques of multisensor data fusion can be categorized into three methods: averaging, decision, and guiding. We present a fusion algorithm which combines averaging and decision. With the assumption that the motion is smooth, our approach can handle the data sequences from multiple sensors with different sampling times. We can also predict the next immediate object position and its motion. The simulation results show our proposed approach is advantageous in terms of accuracy, speed, and versatility.     

Optimal two-sensor scheduling under duty cycle constraint

We consider periodic sensor scheduling in this paper. A system is observed by two sensors. The two sensors communicate their data with a remote state estimator via a bandwidth-limited network which allows only one sensor to send its data at each time. We derive the optimal duty cycle pair and a corresponding sensor data schedule to minimize the trace of the average estimation error covariance. Simulations are provided to demonstrate the results.     

基于信息量的分布式协同自组织算法

无线传感器网络中节点协同自组织主要涵盖传感器管理和状态估计,也就是如何选择传感器节点、设置传感器参数并估计被监测系统的状态,因此协同白组织为决策与估计的联合优化．本文提出了一种自适应动态协同自组织算法,以量测所提供的信息量和节点自身的剩余能量做为节点选择的综合指标,根据设定的感知精度,自适应地选择参与感知任务的节点集合,在信息滤波的融合框架下完成状态的分布式估计．相比信息驱动传感器查询（information-driven sensor querying,IDSQ）,算法具有精度可调、强鲁棒,同时尽可能地延长了网络的生命周期．以目标跟踪为应用背景,其仿真结果表明：以跟踪精度、失跟率和网络生命周期作为评价指标,该算法优于IDSQ．     

无线传感器网络中基于区域决策的距离均值融合算法

研究了无线传感器网络中基于距离的决策融合算法,并针对它们的不足,提出了一种基于区域决策的距离均值融合算法.该算法首先采用目标定位运算估计目标源的位置,并划定以目标源为中心的一定区域为有效区域,然后根据距离均值模型计算有效区域内传感节点的可信度权重,并将其决策和相应的权重上传到融合中心进行决策融合.仿真实验表明,该算法比其它基于距离的融合算法具有更高的检测概率和较好的抗噪性能,适合中大规模无线传感器网络.     

An efficient sensor quantization algorithm for decentralized estimation fusion

In this paper, we consider the design problem of optimal sensor quantization rules (quantizers) and an optimal linear estimation fusion rule in bandwidth-constrained decentralized random signal estimation fusion systems. First, we derive a fixed-point-type necessary condition for both optimal sensor quantization rules and an optimal linear estimation fusion rule: a fixed point of an integral operation. Then, we can motivate an iterative Gauss–Seidel algorithm to simultaneously search for both optimal sensor quantization rules and an optimal linear estimation fusion rule without Gaussian assumptions on the joint probability density function (pdf) of the estimated parameter and observations. Moreover, we prove that the algorithm converges to a person-by-person optimal solution in the discretized scheme after a finite number of iterations. It is worth noting that the new method can be applied to vector quantization without any modification. Finally, several numerical examples demonstrate the efficiency of our method, and provide some reasonable and meaningful observations how the estimation performance is influenced by the observation noise power and numbers of sensors or quantization levels.     

Covering a line segment with variable radius discs

The paper addresses the problem of locating sensors with a circular field of view so that a given line segment is under full surveillance, which is termed as the disc covering problem on a line. The cost of each sensor includes a fixed component f, and a variable component that is a convex function of the diameter of the field-of-view area. When only one type of sensor or, in general, one type of disc, is available, then a simple polynomial algorithm solves the problem. When there are different types of sensors, the problem becomes hard. A branch-and-bound algorithm as well as an efficient heuristic are developed for the special case in which the variable cost component of each sensor is proportional to the square of the measure of the field-of-view area. The heuristic very often obtains the optimal solution as shown in extensive computational testing.     

On a stochastic sensor selection algorithm with applications in sensor scheduling and sensor coverage

In this note we consider the following problem. Suppose a set of sensors is jointly trying to estimate a process. One sensor takes a measurement at every time step and the measurements are then exchanged among all the sensors. What is the sensor schedule that results in the minimum error covariance? We describe a stochastic sensor selection strategy that is easy to implement and is computationally tractable. The problem described above comes up in many domains out of which we discuss two. In the sensor selection problem, there are multiple sensors that cannot operate simultaneously (e.g., sonars in the same frequency band). Thus measurements need to be scheduled. In the sensor coverage problem, a geographical area needs to be covered by mobile sensors each with limited range. Thus from every position, the sensors obtain a different view-point of the area and the sensors need to optimize their trajectories. The algorithm is applied to these problems and illustrated through simple examples.     

Decentralized estimation of regression coefficients in sensor networks

Consider a wireless sensor network with a fusion center deployed to estimate a common non-random parameter vector. Each sensor obtains a noisy observation vector of the non-random parameter vector according to a linear regression model. The observation noise is correlated across the sensors. Due to power, bandwidth and complexity limitations, each sensor linearly compresses its data. The compressed data from the sensors are transmitted to the fusion center, which linearly estimates the non-random parameter vector. The goal is to design the compression matrices at the sensors and the linear unbiased estimator at the fusion center such that the total variance of the estimation error is minimized. In this paper, we provide necessary and sufficient conditions for achieving the performance of the centralized best linear unbiased estimator. We also provide the optimal compression matrices and the optimal linear unbiased estimator when these conditions are satisfied. When these conditions are not satisfied, we propose a sub-optimal algorithm to determine the compression matrices and the linear unbiased estimator. Simulation results are provided to illustrate the effectiveness of the proposed algorithm.     

Decentralized robust set-valued state estimation in networked multiple sensor systems

This paper addresses a decentralized robust set-valued state estimation problem for a class of uncertain systems via a data-rate constrained sensor network. The uncertainties of the systems satisfy an energy-type constraint known as an integral quadratic constraint. The sensor network consists of spatially distributed sensors and a fusion center where set-valued state estimation is carried out. The communications from the sensors to the fusion center are through data-rate constrained communication channels. We propose a state estimation scheme which involves coders that are implemented in the sensors, and a decoder–estimator that is located at the fusion center. Their construction is based on the robust Kalman filtering techniques. The robust set-valued state estimation results of this paper involve the solution of a jump Riccati differential equation and the solution of a set of jump state equations.     

基于层次超立方体模型的对偶密钥预置算法

通过所建立对偶密钥，传感器网络节点之间可使用加密技术进行通信，从而保障了通信的安全性。在KDC和基于多项式池的密钥预置模型基础上，提出了一种用于密钥预置的层次超立方体模型及其密钥预置算法。理论与实验分析表明，与基于多项式和基于多项式池的对偶密钥预置算法相比，该算法具有更好的安全性能与更高的对偶密钥建立概率。     

基于图信号处理的无线传感器网络异常节点检测算法

针对无线传感器网络（WSN）中传感器自身安全性低、检测区域恶劣及资源受限造成节点采集数据异常的问题,提出一种基于图信号处理的WSN异常节点检测算法。首先,依据传感器位置特征建立K-近邻（KNN）图信号模型;然后,基于图信号在低通滤波前后的平滑度之比构建统计检验量;最后,通过统计检验量与判决门限实现异常节点存在性的判断。通过在公开的气温数据集与PM2.5数据集上的仿真验证,实验结果表明,与基于图频域异常检测算法相比,在单个节点异常情况相同条件下,所提算法检测率提升7个百分点;在多个节点异常情况相同条件下,其检测率均达到98%,并且在网络节点异常偏离值较小时仍具有较高的检测率。     

基于粒子滤波的分布式故障诊断

针对非线性、非高斯环境下多传感器的系统故障诊断问题,提出了一种新的基于粒子滤波的分布式故障诊断方法。通过粒子滤波得到的状态估计值的全概率分布信息可用于故障检测。首先建立系统分布式故障诊断模型,由于通信限制,假设各传感器只能向信息融合中心传输二进制数。在各观测值独立同分布的条件下,提出了分布式故障诊断算法,包括本地判决的设计和融合中心的准则设计。仿真结果表明了所提出算法的有效性和优越性。