基于WLAN和ZigBee的室内定位技术研究

发布时间：2018-06-04 03:45

本文选题：WLAN/ZigBee + 室内定位　；参考：《辽宁工业大学》2014年硕士论文

【摘要】：如今，室内定位技术在室内导航、社交、市场推广、物流跟踪等各种位置服务的应用中发挥着极其重要的作用。由于WLAN网络已大量部署且成本低廉，ZigBee凭借功耗小、成本低和可靠性高的优点，因此基于泛在网络WLAN及ZigBee的室内定位已经成为定位技术研究的热点。为满足更高的精度要求，室内定位技术的研究趋势正由同构网络向异构网络协同定位方向延伸。论文主要做以下三方面的研究。首先，研究了不同权值的加权质心定位算法并进行对比分析。针对加权质心定位算法存在的边缘区域定位误差大的问题，提出了一种ZigBee网络下基于距离比值的加权质心定位算法。该方法结合信号强度距离损耗简化模型，采用未知节点到信标节点的距离比值作为权值因子，相对于传统加权质心算法，可以减小某个加权因子值偏大对定位结果产生的影响。仿真表明，该方法不仅明显降低了算法的平均定位误差，而且对加权质心算法的边缘劣势有较明显的改善。然后，针对基于粒子群优化算法的室内定位方法中迭代次数过多，容易陷入局部最优解的缺点，利用了基于线性时变惯性权重的粒子群优化算法，根据迭代次数调整惯性权重的值。针对测距误差越大对定位误差影响越大的问题，，提出了改进的适应度函数，采用残差加权的最小二乘法适应度函数代替均方误差的适应度函数。仿真结果表明，改进的算法不仅能避免陷入局部最优值，减少迭代次数，而且还降低了测距误差变化对定位误差的影响。最后，对异构网络下的室内网络定位方法进行研究，针对WLAN和ZigBee网络定位各自优势互补的特性，提出了基于ZigBee加权质心和WLAN粒子群优化的异构网络协同定位算法。采用加权质心法初始值周边区域作为粒子群初始位置区域，进行粒子寻优，以缩小粒子群的搜索范围。仿真结果显示，本算法不仅提高了定位精度，缩小了粒子群优化算法的初始值区域，而且减少了迭代次数，降低了算法的复杂度。
[Abstract]:Nowadays, indoor positioning technology plays an extremely important role in the application of indoor navigation, social networking, marketing, logistics tracking and other location services. Due to the advantages of low power consumption, low cost and high reliability, WLAN network has been deployed in large numbers. Therefore, indoor positioning based on ubiquitous network WLAN and ZigBee has become a hot spot in localization technology. In order to meet the higher precision requirement, the research trend of indoor positioning technology is extending from isomorphic network to heterogeneous network. The paper mainly does the following three aspects of research. Firstly, the weighted centroid localization algorithm with different weights is studied and compared. A weighted centroid location algorithm based on distance ratio in ZigBee network is proposed to solve the problem of large error of edge location in weighted centroid localization algorithm. Combined with the simplified model of signal intensity distance loss, the ratio of unknown node to beacon node is used as the weight factor. Compared with the traditional weighted centroid algorithm, the influence of the value of a certain weighting factor on the location result can be reduced. Simulation results show that this method not only reduces the average positioning error of the algorithm, but also improves the edge disadvantage of the weighted centroid algorithm. Then, aiming at the shortcomings of the indoor localization method based on particle swarm optimization, which is easy to fall into the local optimal solution, the particle swarm optimization algorithm based on linear time-varying inertia weight is used. Adjust the value of inertia weight according to the number of iterations. In order to solve the problem that the greater the ranging error is, the improved fitness function is proposed, and the least square fitness function is used to replace the fitness function of mean square error. Simulation results show that the improved algorithm can not only avoid falling into local optimal value and reduce the number of iterations, but also reduce the influence of ranging error on location error. Finally, the indoor network location method based on heterogeneous network is studied. According to the complementary advantages of WLAN and ZigBee network localization, a cooperative location algorithm based on ZigBee weighted centroid and WLAN particle swarm optimization is proposed. In order to reduce the searching range of particle swarm, the initial value of weighted centroid method is used as the initial position region of particle swarm. Simulation results show that the proposed algorithm not only improves the positioning accuracy and reduces the initial region of PSO, but also reduces the number of iterations and the complexity of the algorithm.
【学位授予单位】：辽宁工业大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TN92

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