复杂环境中基于RSSI的无线传感网络定位算法研究

发布时间：2018-06-08 15:28

本文选题：WSN + 定位算法　；参考：《重庆大学》2014年硕士论文

【摘要】：为了解决复杂环境下无线传感器网络（Wireless Sensor Network, WSN）定位技术定位精度和计算量难以兼顾，，本文研究了复杂环境下的定位算法。文中分析了多种典型定位算法在复杂环境下应用的优缺点，研究了基于接收信号强度（Received Signal Strength Indicator, RSSI）距离测量模型的位置计算方法。基于测距定位算法，提出了改进的极大似然估计求解方法和概率质心两类定位算法。课题完成了以下研究工作，并取得了一定的研究成果。首先，基于模拟仿真实验平台，分析了几类经典定位算法在复杂环境下的定位性能，找出了影响定位算法定位性能的原因，明确了复杂环境下定位算法的改进方向。其次，通过对复杂环境下的极大似然估计定位算法研究，发现原求解方法因计算量大限制了该算法在复杂环境的应用，从而提出了一种新的极大似然估计极值点求解方法，该方法可直接求取未知节点估计位置，即极大似然估计的极值点。仿真验证了该定位算法的有效性，在复杂环境下获得的定位性能测试曲线显示，该算法不仅可降低计算量，而且具有很强的鲁棒性优势。最后，对三边质心定位算法和极大似然估计定位算法进行了比较研究，提出了概率质心定位算法。该算法以重叠区域概率密度函数作为其密度函数，即以概率质心坐标表示未知节点位置，改善了原三边质心定位算法等同考虑重叠区域的不足，提高了复杂环境下的定位精度。在复杂环境下概率质心定位算法获得的仿真定位曲线显示，该算法既继承了三边质心定位计算量低，也确保了较高的定位精度和强鲁棒性。课题研究表明，本文提出的改进的极大似然估计求解方法和概率质心定位算法可协调定位精度和计算量两者的矛盾，并具有强鲁棒性的优势，可适用于复杂环境下的无线传感器网络定位。
[Abstract]:In order to solve the problem of wireless sensor network (WSN) location in complex environment, the localization algorithm in complex environment is studied in this paper. In this paper, the advantages and disadvantages of several typical localization algorithms in complex environments are analyzed, and the position calculation method based on received signal strength (RSSI) distance measurement model is studied. Based on the range location algorithm, an improved maximum likelihood estimation (MLE) algorithm and a probabilistic centroid localization algorithm are proposed. The following research work has been completed, and some research results have been achieved. Firstly, based on the simulation experiment platform, the localization performance of several classical localization algorithms in complex environment is analyzed. The reasons that affect the localization performance of the localization algorithm are found out, and the improvement direction of the localization algorithm in the complex environment is defined. Secondly, the maximum likelihood estimation localization algorithm in the complex environment is studied. It is found that the original solution method limits the application of the algorithm in complex environments because of the large amount of computation. Thus, a new maximum likelihood estimation method is proposed, which can directly calculate the estimated position of unknown nodes. The extremum of maximum likelihood estimation. Simulation results show that the proposed algorithm is effective. The performance test curves obtained in complex environments show that the algorithm can not only reduce the computational complexity, but also has a strong robustness advantage. Three edge centroid localization algorithm and maximum likelihood estimation localization algorithm are compared, and probabilistic centroid localization algorithm is proposed. In this algorithm, the probability density function of overlapping region is used as its density function, that is, the position of unknown node is represented by the coordinate of probabilistic centroid, which improves the localization accuracy in complex environment. The simulation curve obtained by the probabilistic centroid localization algorithm in complex environment shows that the algorithm not only inherits the low computation amount of centroid localization, but also ensures high positioning accuracy and strong robustness. The improved maximum likelihood estimation method and the probabilistic centroid localization algorithm proposed in this paper can reconcile the contradiction between location accuracy and computational complexity, and have the advantage of strong robustness, and can be applied to wireless sensor networks in complex environments.
【学位授予单位】：重庆大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TN929.5;TP212.9

【参考文献】