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Research on Multi-Source Fusion Based Seamless Indoor/Outdoor Positioning Technology

机译:基于多源融合的无缝室内外定位技术研究

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GNSS has been applied widely. Yet because satellite signals are vulnerable and susceptible to blockage, the operability of GNSS in urban canyons are greatly hampered and GNSS even proves useless for indoor settings. This paper proposes system architecture for the integration of WLAN fingerprinting, visual positioning, baroceptor-derived altitude estimation and GNSS for seamless indoor/ outdoor positioning for vehicles and pedestrians. This architecture augments GNSS through the integration of terminal-sideetwork-side positioning and position/ measurement domain. After temporal and spatial synchronization, data from each sensor is filtered by sub filters and then processed by the main filter. The purpose of these operations is to provide accurate and continuous estimates of positions. Tests conducted in the new technology center of CAS show that the architecture proposed can achieve seamless indoor/outdoor positioning, with a better accuracy performance than any single-source method as the former still maintains accuracy and continuity when the later generates noticeable errors. Calculation shows that multi-source fusion has an accuracy level of better than 1 m (outdoor)/3 m (indoor), hence capable of meeting users' demand for seamless indoor/outdoor positioning.
机译:GNSS已被广泛应用。然而,由于卫星信号易受干扰,容易受到阻塞,因此,GNSS在城市峡谷中的可操作性受到了极大的阻碍,甚至在室内环境中,GNSS都被证明是无用的。本文提出了一种系统架构,用于集成WLAN指纹,视觉定位,受压感受器得出的高度估计和GNSS,以实现车辆和行人的无缝室内/室外定位。该架构通过集成终端侧/网络侧定位和位置/测量域来增强GNSS。在时间和空间同步之后,来自每个传感器的数据由子过滤器过滤,然后由主过滤器处理。这些操作的目的是提供准确且连续的位置估计。在中国科学院新技术中心进行的测试表明,所提出的体系结构可以实现无缝的室内/室外定位,比任何单一来源的方法都具有更好的精度性能,因为当后者产生明显的错误时,前者仍然保持准确性和连续性。计算表明,多源融合的准确度优于1 m(室外)/ 3 m(室内),因此能够满足用户对室内/室外无缝定位的需求。

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