基于IOS平台的WIFI移动终端室内定位应用系统的研究和实现

发布时间：2018-06-06 23:58

本文选题：室内定位 + WIFI　；参考：《电子科技大学》2014年硕士论文

【摘要】：随着移动互联网时代的到来和移动技术的不断发展,LBS(Location-based services)即基于位置的服务越来越被普罗大众所熟悉。在移动互联网浪潮普及的今天,LBS通过用户使用智能手机和智能平板访问互联移动网络的过程中提供位置信息而被广泛服务应用于社交娱乐,而随着移动手机和移动平板的普及,室内定位服务将成为移动互联网时代的核心功能,变得越来越重要。本文针对这一热点方向,在基于苹果的IOS系统和MAC OS系统上设计并实现了一套基于WIFI的室内定位系统。创新性的在基于位置指纹的定位算法上做出了优化和改进研究。提出了对采集到得RSSI信号进行静态和动态优化处理的两种方法,一种是静态处理方法,对采集到作为数据指纹的信号进行高斯分布滤波;另一种是动态处理方法,采用修正加权滤波的方法,在实时定位阶段,采样的RSSI信号需要在短时间内完成处理,减小了定位中出现大幅偏差的概率。在NNSS-AVG算法和高斯概率估计两种算法的基础上,提出了一种混合改进算法,第一阶段利用NNSS算法快速选取一定数量的预定位点。第二阶段利用高斯概率分布算法对预定位点进行位置概率估计计算,最后通过加权系数的方法得出最终的定位计算结果,该方法有效地综合了两类算法的优点,在庞大的定位数据对比计算量的前提下提升了定位系统的定位响应时间和定位精准度。测试表明,基于NNSS-AVG算法和高斯概率估计的混合算法能够有效的提升最终定位的精准度,定位响应时间也能够得到一定程度的提高。
[Abstract]:With the advent of the mobile Internet era and the continuous development of mobile technology, the location based services are becoming more and more familiar to the general public. Today, with the popularity of mobile Internet, LBS is widely used in social entertainment by providing location information through the use of smartphones and smart tablets to access the interconnected mobile network, and with the popularity of mobile phones and mobile tablets, Indoor positioning service will become the core function of the mobile Internet era, becoming more and more important. Aiming at this hot spot, this paper designs and implements a set of indoor positioning system based on WIFI on IOS system and MAC OS system based on Apple. The innovative location algorithm based on location fingerprint is optimized and improved. Two methods for static and dynamic optimal processing of collected RSSI signals are proposed, one is static processing method, the other is dynamic processing method. In the real time localization stage, the sampled RSSI signal needs to be processed in a short time by using the modified weighted filtering method, which reduces the probability of large deviation in the localization. On the basis of NNSS-AVG algorithm and Gao Si probability estimation algorithm, a hybrid improved algorithm is proposed. In the first stage, NNSS algorithm is used to quickly select a certain number of predetermined sites. In the second stage, the Gao Si probability distribution algorithm is used to estimate the location probability of the predetermined site. Finally, the final location result is obtained by the method of weighting coefficient. The method effectively integrates the advantages of the two kinds of algorithms. The positioning response time and positioning accuracy of the positioning system are improved under the premise of a large amount of comparison and calculation of the positioning data. The test results show that the hybrid algorithm based on NNSS-AVG algorithm and Gao Si probability estimation can effectively improve the accuracy of the final location, and the localization response time can be improved to a certain extent.
【学位授予单位】：电子科技大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TP311.52;TN92

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