无线传感器网络多目标稀疏信息定位机制
发布时间：2018-12-14 09:15
【摘要】：在无线传感器网络(Wireless Sensor Networks,WSNs)多目标定位中,受环境因素及信息提取技术的影响,用于定位的物理信息呈现很强的不完备性。非完备信息下的多目标定位是目前无线传感器网络定位技术研究的重点和难点。利用压缩感知(Compressed Sensing,CS)理论,本文提出一种非测距WSNs多目标稀疏信息(Sparse Targets Information)定位方法--CS-STI。该方法通过确定传感器节点感知范围内是否存在目标以及存在多少目标来得到测量值,并且在此过程中不依赖于任何硬件测量。传感器节点对目标进行信息采集后,即可运用基于CS的重构算法对所有目标进行重构。本文的主要内容如下:(1)建立网络模型:将无线传感器网络监控区域划分成多个小网格,传感器节点与目标随机分布于网格中,确定出以目标位置信息为元素的稀疏向量;(2)定义参数矩阵,通过确定传感器节点感知范围内是否存在目标以及存在多少目标得到测量值,将测量值矩阵表示为压缩感知理论中测量矩阵、稀疏矩阵和稀疏向量的乘积形式,并给出各个矩阵的物理意义与表达式;(3)对测量值矩阵进行正交化预处理,使更加合理运用压缩感知理论高概率重构目标,并运用基追踪(Basis Pursuit,BP)、正交匹配追踪(Orthogonal Matching Pursuit,OMP)重构算法进行目标位置信息向量的重构;(4)对算法进行仿真分析,对基于CS理论的非测距多目标稀疏信息定位方法进行了仿真,将本文提出的算法与传统算法进行了比较,分析了传感器节点感知半径、待定位目标数、传感器节点数对目标定位性能的影响。
[Abstract]:In the multi-target location of wireless sensor network (Wireless Sensor Networks,WSNs), the physical information used for location is very incomplete due to the influence of environmental factors and information extraction technology. Multi-target localization under incomplete information is the focus and difficulty in the research of wireless sensor network localization technology. Based on the theory of compressed sensing (Compressed Sensing,CS), this paper presents a non-ranging WSNs multi-target sparse information (Sparse Targets Information) localization method-CS-STI. The method obtains the measured value by determining whether and how many targets exist in the sensor node's perceptual range and does not depend on any hardware measurement in the process. After the sensor node collects the information of the target, the reconstruction algorithm based on CS can be used to reconstruct all the targets. The main contents of this paper are as follows: (1) establish the network model: the monitoring area of wireless sensor network is divided into several small grids, sensor nodes and targets are randomly distributed in the grid, and the sparse vector with the target location information as the element is determined; (2) the parameter matrix is defined. The measurement value matrix is expressed as the measurement matrix in the compressed sensing theory by determining whether or not the target exists in the sensor node perception range and how many targets exist. The product form of sparse matrix and sparse vector, and the physical meaning and expression of each matrix are given. (3) preprocessing the measured value matrix by orthogonalization, so that the compressed perception theory can be used more reasonably to reconstruct the target with high probability, and the basis tracking (Basis Pursuit,BP) and orthogonal matching tracking (Orthogonal Matching Pursuit,) are also used. OMP) reconstruction algorithm is used to reconstruct the target location information vector. (4) the algorithm is simulated and analyzed, and the non-ranging multi-target sparse information location method based on CS theory is simulated. The proposed algorithm is compared with the traditional algorithm, and the sensor node perceptual radius is analyzed. The effect of the number of targets to be located and the number of sensor nodes on the performance of target location.
【学位授予单位】：天津大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TP212.9;TN929.5
本文编号：2378363
[Abstract]:In the multi-target location of wireless sensor network (Wireless Sensor Networks,WSNs), the physical information used for location is very incomplete due to the influence of environmental factors and information extraction technology. Multi-target localization under incomplete information is the focus and difficulty in the research of wireless sensor network localization technology. Based on the theory of compressed sensing (Compressed Sensing,CS), this paper presents a non-ranging WSNs multi-target sparse information (Sparse Targets Information) localization method-CS-STI. The method obtains the measured value by determining whether and how many targets exist in the sensor node's perceptual range and does not depend on any hardware measurement in the process. After the sensor node collects the information of the target, the reconstruction algorithm based on CS can be used to reconstruct all the targets. The main contents of this paper are as follows: (1) establish the network model: the monitoring area of wireless sensor network is divided into several small grids, sensor nodes and targets are randomly distributed in the grid, and the sparse vector with the target location information as the element is determined; (2) the parameter matrix is defined. The measurement value matrix is expressed as the measurement matrix in the compressed sensing theory by determining whether or not the target exists in the sensor node perception range and how many targets exist. The product form of sparse matrix and sparse vector, and the physical meaning and expression of each matrix are given. (3) preprocessing the measured value matrix by orthogonalization, so that the compressed perception theory can be used more reasonably to reconstruct the target with high probability, and the basis tracking (Basis Pursuit,BP) and orthogonal matching tracking (Orthogonal Matching Pursuit,) are also used. OMP) reconstruction algorithm is used to reconstruct the target location information vector. (4) the algorithm is simulated and analyzed, and the non-ranging multi-target sparse information location method based on CS theory is simulated. The proposed algorithm is compared with the traditional algorithm, and the sensor node perceptual radius is analyzed. The effect of the number of targets to be located and the number of sensor nodes on the performance of target location.
【学位授予单位】：天津大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TP212.9;TN929.5
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