Multi-sensors Systems for Ubiquitous Personal Navigation

导航技术正在以惊人的速度扩展，特别是在民用社区中，车辆和个人导航仪等设备在我们的日常生活中变得司空见惯。虽然车辆和机载导航是一项成熟的服务，但通过智能手机的个人导航系统（PNS）才刚刚开始获得应用市场激增，硬件发展和消费者采用的动力。PNS使移动的基于位置的服务（LBS）成为普遍的并且嵌入在诸如社交网络、资源跟踪和定位人的许多应用中。基于全球定位系统（GPS）的导航设备是这些应用的主要解决方案。然而，GPS信号并不总是可用的，这对诸如密集的城市峡谷、树冠下和室内深处等GPS信号被拒绝的环境构成了很大的挑战。 微机电系统（MEMS）技术的最新进展与电子器件的小型化相结合，使得能够将多个传感器部署到大众消费市场中的新一代导航设备中。例如，在许多智能手机中，嵌入了廉价的基于MEMS的惯性导航传感器、磁力计和气压计，以实现游戏和娱乐应用。然而，成本和尺寸的增加是以导航精度为代价的。MEMS传感器具有比其更高等级的对应物大得多的误差特性，这意味着集成策略对于这些传感器的成功和准确性至关重要。此外，人体运动使得个人导航与其他导航平台（例如汽车、火车、船只等）不同且具有挑战性：个人导航意味着速度和方向的频繁变化，并且位置不能像汽车和火车那样被约束到预定义的轨道。根据定义，移动的设备是手持的，处于难以预测的位置;各种应用模式根据用户的活动（诸如步行、驾驶、跑步等）而频繁地改变。 本研究的目标是解决上述挑战，并提供一个多传感器辅助的解决方案，无处不在的，连续的，可靠的，和无缝的个人导航技术，使用传感器目前存在于下一代智能手机。

Navigation technologies are expanding at a phenomenal rate, especially within the civil community, with devices such as vehicle and personal navigators becoming commonplace within our everyday lives. While vehicle and airborne navigation is a well-established service, personal navigation system (PNS) through smart phones has just started to gain momentum fuelled by the application marketplace proliferation, hardware evolution, and consumer adoption. PNS enables mobile location based services (LBS) which become pervasive and are embedded in many applications such as social networking, resource tracking and positioning people . Global Positioning System (GPS) based navigation devices are the dominant solution for these applications. However, GPS signals are not always available and this poses a great deal of challenge for GPS signal-denied environments such as dense urban canyons, under tree canopy and deep indoors. Recent advances in Micro-Electro-Mechanical Systems (MEMS) technology combined with the miniaturization of electronics have empowered the deployment of multiple sensors into a new generation of navigation devices in the mass consumer market. In many smart phones, for example, inexpensive MEMS-based inertial navigation sensors, magnetometers, and barometers are embedded to enable gaming and entertainment applications. Nevertheless, the gains in cost and size come with a price in terms of navigation accuracy. MEMS sensors have much larger error characteristics than their higher grade counterparts meaning that the integration strategy is paramount to the success and accuracy of these sensors. Furthermore, human movement makes personal navigation different and challenging from other navigation platforms (such as for cars, trains, boats, etc.): Personal navigation implies frequent changes of speed and orientation and position cannot be constraint to predefined tracks as for cars and trains. Mobile devices, by definition, are handheld in a position that is difficult to be predicted; various application modes alter frequently depending on user's activities such as walking, driving, running etc. The goal of this research is to address and solve the above challenges, and provide a multi-sensor assisted solution for ubiquitous, continuous, reliable, and seamless techniques for Personal Navigation using sensors currently exist in next generation smart phones.