基于无线定位的个性化导览关键技术及其在博物馆中的应用

本文提出一种基于Wi-Fi无线定位网络能够满足相关应用精度需求的室内导览方法，该方法使用智能手机自身处理能力实时进行信号强度概率分布以及位置指纹匹配计算，使用基于动态权值的方法来对室内环境进行建模，引入加权线性公式组合推荐算法实现基于优化A星算法的路线规划。本文同时给出了该方法应用于构建博物馆个性化导览系统的应用示例，实验结果表明该方法具有较高的定位精度和推荐准确率。本文所提室内导览方法具有通用性好和组网成本低的特点，能够较好满足博物馆等室内导览系统应用需求，具备进一步进行商业化应用的潜力。

A Wireless Personalized Navigation Technology For Museums

The indoor positioning system and personalized navigation technology in large buildings, such as shopping malls, museums, and transport hubs, are drawing more attention along with the rapid development of mobile Internet. In order to improve the accuracy of positioning and simplify the complexity of algorithm, this paper proposed a generic personalized indoor navigation schema based on Wi-Fi network. The localization process is implemented by using the fingerprint technology based on the probability distribution of the received signal strength (RSS). The system adopts a novel modeling method that treats the indoor environment as a set of grids. Each grid contains multi-weight values that are given dynamically according to the interaction process. Optimized A star algorithm is used for path planning with the hybrid personalized recommendation system. A testing mobile App for android is implemented with the features of indoor positioning, navigation and social components. The experimental evaluation presents a fine recommendation effectiveness and a high positioning accuracy, which could satisfy the requirements of personalized indoor navigation applications.