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基于接收信号强度指示(RSSI)的室内定位方法,因具备性价比高、定位范围广且精度高的优势在众多领域存在应用价值[1-4]。但受室内障碍物遮挡、人员走动以及信号源功率不稳定影响,使用移动终端采集的RSSI信号中包含波动数据(“噪点”),导致基于指纹库的传统定位方法在移动终端位置估计中出现不准确现象[5-6]。由于现有指纹定位算法对于“噪点”处理较粗糙,而指纹库的好坏又对定位精度产生影响。如何剔除指纹库中信号“噪点”,通过优化指纹库来降低定位误差是室内定位技术中有待解决的关键问题。
本文基于现有无线接入点(access point, AP)个数,提出一种基于卡尔曼平滑的改进自适应加权K最近邻(AWKNN)位置指纹定位算法。通过求取RSSI均方差代替欧氏距离,解决在一些错误匹配点上由于欧氏距离中的项数远小于匹配个数而导致欧氏距离变小,从而误判为最佳匹配点的问题。
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