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The reliability of global navigation satellite system (GNSS) positioning degrades when satellite signals are interfered. Such degradation is hard to be deteced by a micro-electro mechanical system (MEMS) based inertial system(INS)/GNSS, integrating navigation system with a conventional Kalman filtering, which results in poten- tial integrity problem of the system. Hence, an algorithm combining wireless fidelity (WiFi) signal with a federa- ted Kalman filter (FKF) is proposed to identify the system integrity in dense urban navigation. The criterion of the system integrity detection is created followed by the derivation of the integrity coefficient. The field test shows that integrity changes can be captured by applying WiFi, and the maximum positioning error is reduced by 67~ without compensation of inertial sensors in integrity deterioration. 相似文献
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在室内WiFi环境下,针对常见指纹匹配算法所忽略的信号波动问题,提出了一种基于自适应修正曼哈顿距离和AP选择的指纹匹配算法,并结合加权K近邻方法实现定位。首先采用AP选择算法获取部分受干扰程度小和出现频率高的AP,在指纹匹配时仅使用该部分AP的接收信号强度进行计算;在分析WiFi信号传播衰减公式和信号波动的基础上,提出了将自适应修正曼哈顿距离作为指纹匹配的度量距离,使用该距离旨在平滑信号波动对指纹相似度计算的影响;最后采用加权K近邻方法估计测试点的坐标。实验结果表明,在加权K近邻方法的框架下,基于自适应修正曼哈顿距离的定位算法在定位精度上优于基于欧氏距离、曼哈顿距离、余弦距离和Sorensen距离的定位算法。 相似文献
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