【摘要】：在無線傳感器網(wǎng)絡(luò)(Wireless Sensor Networks,WSNs)多目標(biāo)定位中,受環(huán)境因素及信息提取技術(shù)的影響,用于定位的物理信息呈現(xiàn)很強(qiáng)的不完備性。非完備信息下的多目標(biāo)定位是目前無線傳感器網(wǎng)絡(luò)定位技術(shù)研究的重點(diǎn)和難點(diǎn)。利用壓縮感知(Compressed Sensing,CS)理論,本文提出一種非測(cè)距WSNs多目標(biāo)稀疏信息(Sparse Targets Information)定位方法--CS-STI。該方法通過確定傳感器節(jié)點(diǎn)感知范圍內(nèi)是否存在目標(biāo)以及存在多少目標(biāo)來得到測(cè)量值,并且在此過程中不依賴于任何硬件測(cè)量。傳感器節(jié)點(diǎn)對(duì)目標(biāo)進(jìn)行信息采集后,即可運(yùn)用基于CS的重構(gòu)算法對(duì)所有目標(biāo)進(jìn)行重構(gòu)。本文的主要內(nèi)容如下:(1)建立網(wǎng)絡(luò)模型:將無線傳感器網(wǎng)絡(luò)監(jiān)控區(qū)域劃分成多個(gè)小網(wǎng)格,傳感器節(jié)點(diǎn)與目標(biāo)隨機(jī)分布于網(wǎng)格中,確定出以目標(biāo)位置信息為元素的稀疏向量;(2)定義參數(shù)矩陣,通過確定傳感器節(jié)點(diǎn)感知范圍內(nèi)是否存在目標(biāo)以及存在多少目標(biāo)得到測(cè)量值,將測(cè)量值矩陣表示為壓縮感知理論中測(cè)量矩陣、稀疏矩陣和稀疏向量的乘積形式,并給出各個(gè)矩陣的物理意義與表達(dá)式;(3)對(duì)測(cè)量值矩陣進(jìn)行正交化預(yù)處理,使更加合理運(yùn)用壓縮感知理論高概率重構(gòu)目標(biāo),并運(yùn)用基追蹤(Basis Pursuit,BP)、正交匹配追蹤(Orthogonal Matching Pursuit,OMP)重構(gòu)算法進(jìn)行目標(biāo)位置信息向量的重構(gòu);(4)對(duì)算法進(jìn)行仿真分析,對(duì)基于CS理論的非測(cè)距多目標(biāo)稀疏信息定位方法進(jìn)行了仿真,將本文提出的算法與傳統(tǒng)算法進(jìn)行了比較,分析了傳感器節(jié)點(diǎn)感知半徑、待定位目標(biāo)數(shù)、傳感器節(jié)點(diǎn)數(shù)對(duì)目標(biāo)定位性能的影響。
[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.
【學(xué)位授予單位】：天津大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP212.9;TN929.5

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本文編號(hào)：2378363