面向智能手机的多源融合定位精度和能耗权衡机制研究

定位算法的精度与能耗是相互制约的两个关键性能指标，特别是对于具备GPS、WLAN和IMU等多种定位手段、能量受限的智能手机来说，实现满足不同精度需求和能耗约束的多源融合定位方法显得尤为重要，是一个亟待解决的难题。本项目拟围绕基于GPS/WLAN/IMU的多源融合定位精度和能耗权衡机制开展研究。首先，从理论上全面分析WLAN位置指纹定位的误差机理，形成系统的定位优化方法和能耗节约策略，提出适用于室外环境的关键算法。其次，深入剖析基于IMU的行人航迹推算算法的误差、能耗及其与IMU采样频率的依赖关系，通过合理选择采样频率提高算法的能效。最后，探索多源融合定位方法的误差影响机制和能耗变化规律，构建定位误差、设备占空比、采样频率、能耗等要素的约束关系模型，提出自适应的设备调度算法，解决融合定位的精度和能耗权衡问题。本项目不仅有助于完善智能手机的定位理论，而且为实现高能效的定位服务提供关键技术保障。

Localization accuracy and energy consumption are two key and correlated performance metrics for localization algorithms, and especially for smartphones with limited energy capacity and multiple location sensing abilities (e.g. GPS, WLAN and IMU), implementing multi-source fusion localization methods that satisfy both precision requirements and energy constraints is an urgent problem to be solved. This project intends to focus on trading off between the localization accuracy and energy consumption of the fusion localization based on GPS/WLAN/IMU. First, after comprehensively analyzing the localization errors of the WLAN-based fingerprinting method, systematic localization optimization methods and energy-saving strategies will be proposed, and critical algorithms for outdoor localization will be presented as well. Second, by in-depth analyzing the estimation error and energy consumption of pedestrian dead reckoning(PDR) algorithms based on IMU as well as their dependency on IMU sampling frequencies, the energy efficiency of the PDR algorithms will be significantly improved by selecting appropriate sampling frequencies. Last but not least, through investigating the error mechanism and the energy consumption rule of the fusion localization, the constraint model among the localization error, device duty cycle, sampling frequency, energy consumption and other factors will be established, so as to trade off between the localization accuracy and energy consumption of the fusion localization by adaptively scheduling the working status and the sampling frequencies of the devices embedded in smartphones. This project not only promotes the theory of smartphone localization, but also provides critical technical supports for energy-efficient localization services.