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首页> 外文会议>Proceedings of the 22nd international technical meeting of the Satellite Division of the Institute of Navigation (ION GNSS 2009) >Context Detection for Improving Positioning Performance and Enhancing User Experience
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Context Detection for Improving Positioning Performance and Enhancing User Experience

机译：上下文检测可提高定位性能并增强用户体验

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Integration of MEMS sensors with GPS enables a variety of features that improve location availability and enhance mobile user experience.Context(or situational)detection is one such important set of features.It is necessary to have the knowledge of state of user in order to deploy the most appropriate location technology in conjunction with GPS,for example,in indoor environments,where the GPS signal is not available or is severely degraded. Context detection algorithms utilize low cost MEMS sensors such as 3-axis accelerometer,pressure sensor integrated with GPS.These algorithms recognize and track user activity modes such as walking,stationary, jogging,driving,climbing up/down stairs,on the escalator or in the elevator and predict the locale such as inside/outside the building.The context detection algorithms can be designed to use any available MEMS sensor data independent of the underlying location technology in the devices.These algorithms do not require that the sensors be accurately aligned or calibrated for them to be effective.Usefulness of the context detection algorithms is dependent on the performance parameters such as probability of false detection of context and latency in detecting transition between various user modes.In this paper,a description of algorithms is provided.Performance of these algorithms is dependent on the type(sensing capability and class of sensor)and combination of sensors used.Results of testing these algorithms for the various use cases experienced in the real-life scenarios is presented.
机译：MEMS传感器与GPS的集成可实现多种功能，从而改善位置可用性并增强移动用户的体验。上下文（或情境）检测就是其中的一种重要功能，因此必须了解用户的状态才能进行部署与GPS结合使用的最合适的定位技术，例如在GPS信号不可用或严重恶化的室内环境中。上下文检测算法利用低成本的MEMS传感器（例如3轴加速度计，集成有GPS的压力传感器）来识别和跟踪用户的活动模式，例如步行，固定，慢跑，驾驶，爬上/下楼梯，在自动扶梯上或上下文检测算法可以设计为使用任何可用的MEMS传感器数据，而与设备中的底层定位技术无关，这些算法不需要将传感器精确对准或对齐上下文检测算法的有效性取决于性能参数，例如错误检测上下文的概率和检测各种用户模式之间转换的等待时间。本文提供了算法的描述。这些算法取决于所使用的传感器的类型（传感器的传感能力和类别）以及传感器的组合。提出了针对实际场景中遇到的各种用例测试这些算法的方法。

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