基于RSSI测距的室内定位算法研究与实现

发布时间：2018-06-09 13:45

本文选题：室内定位 + WiFi　；参考：《南京信息工程大学》2017年硕士论文

【摘要】：随着互联网与智能终端的快速发展,基于无线传感网络的室内定位应用越来越广泛,例如商场仓库快速寻找货物位置等。基于接收信号强度指示(RSSI)的WiFi节点定位技术,由于其成本低、功耗小、硬件要求低等优势,在室内定位中应用广泛。由于室内环境复杂,长距离通信、节点发射功率、突发干扰、环境因子等因素严重影响检测RSSI值,进而影响室内定位精度。因此如何有效抑制室内环境因素的干扰,提高室内定位的精度对基于RSSI测距的WiFi室内定位具有很大的意义。在传统测距的定位系统中,信号在传播过程中会受多径效应、障碍物和其它噪声等干扰因素的影响,造成信标节点在相同的传输距离下接收到的RSSI值也会具有较大的差异性,若直接使用此数据进行计算,则会造成较大的误差,降低转换精度,因此需要对采集到的数据进行预先处理。本文选用卡尔曼滤波对测量数据进行预处理得出状态向量的最优估计,运用最小二乘法拟合传播方程;在传统UKF算法的基础上提出一种基于对数鲁棒函数的UKF改进算法,采用鲁棒校正技术动态校正过程噪声方差Q的估计值,提高状态变量估计精度,对测量数据进行二次处理,求得未知节点到锚节点的距离,削弱非视距环境造成的影响,减少测距误差,通过三边定位法获得未知节点估计坐标;引入粒子群优化算法优化未知节点的估计坐标,降低坐标误差,提高室内定位算法精度。通过Matlab仿真实验结果分析表明,和传统的测距定位算法相比,本文定位方法能有效的减少测距误差,提高室内定位系统的定位精度。
[Abstract]:With the rapid development of the Internet and intelligent terminals, indoor positioning based on wireless sensor network (WSN) is more and more widely used, such as shopping malls to quickly find the location of goods. WiFi node location technology based on received signal strength indication (RSSI) is widely used in indoor positioning because of its advantages of low cost, low power consumption and low hardware requirements. Because of the complex indoor environment, long distance communication, node transmit power, burst interference, environmental factors and other factors, the RSSI value is seriously affected, and then the indoor positioning accuracy is affected. Therefore, how to effectively suppress the interference of indoor environmental factors and improve the accuracy of indoor positioning is of great significance to WiFi indoor positioning based on RSSI ranging. In the traditional location system of ranging, the signal will be affected by multi-path effect, obstacles and other interference factors during the process of transmission, which results in the difference of the RSSI value received by the beacon node at the same transmission distance. If this data is directly used for calculation, it will cause large errors and reduce the conversion accuracy. Therefore, it is necessary to pre-process the collected data. In this paper, Kalman filter is used to preprocess the measurement data to get the optimal estimation of the state vector, the least square method is used to fit the propagation equation, and an improved UKF algorithm based on logarithmic robust function is proposed based on the traditional UKF algorithm. Robust correction technique is used to dynamically correct the estimation value of noise variance Q, to improve the estimation accuracy of state variables, to process the measured data twice, to obtain the distance from unknown node to anchor node, and to weaken the influence caused by non-line-of-sight environment. The estimation coordinates of unknown nodes are obtained by triangulation method, and the estimated coordinates of unknown nodes are optimized by particle swarm optimization algorithm to reduce the coordinate error and improve the accuracy of indoor positioning algorithm. The simulation results of Matlab show that compared with the traditional location algorithm, this method can effectively reduce the ranging error and improve the positioning accuracy of indoor positioning system.
【学位授予单位】：南京信息工程大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN92

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