Adaptive Fading Memory Based Bluetooth Sequence Matching Localization Algorithm
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摘要: 针对传统指纹定位算法建库耗时长和定位精度低的问题，该文提出一种基于自适应渐消记忆的蓝牙序列匹配定位算法。首先，利用行人航迹推算(PDR)和最近邻算法(NNA)对运动序列进行位置标定和接收信号强度(RSS)映射；然后，根据邻近位置的相关性，采用序列递归搜索算法构建指纹序列数据库；最后，通过自适应渐消记忆算法，并结合初始序列匹配度实现位置估计。实验结果表明，该算法在室内环境下能够获得较低的建库时间开销以及较高的定位精度。Abstract: The traditional fingerprinting localization algorithm has high construct time overhead and low positioning accuracy. Because of this problem, an adaptive fading memory based bluetooth sequence matching localization algorithm is proposed. Firsly, Pedestrian Dead Reckoning(PDR) and Nearest Neighbor Algorithm(NNA) are applied to performing position calibration and Received Signal Strength(RSS) mapping of Motion Sequences. Secoudly, according to the relevance of neighboring locations, a sequence recursive search method is used to construct fingerprint sequence database. Finally, an adaptive fading memory algorithm and initial sequence matching degree are considered to realize the position estimation of target. The experimental results show that this algorithm is able to consume low construct time overhead and achieve high indoor localization precision.
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表 1 序列递归搜索算法伪代码
算法：序列递归搜索 输入：局部邻近性矩阵${\text{M}}$；初始数据库${\text{D}}$；指纹序列维度$n$。 输出：指纹序列集${\text{R}}$。 (1) 创建集合${\text{R}}$存储所有指纹序列，集合${\text{Q}}$存储临时指纹序列； (2) for ${\text{D}}$中的每一个指纹点$s$； (3) 将$s$添加到${\text{Q}}$中； (4) 自定义变量$p$； (5) $p = n$； (6) do (7) $p = p - 1$； (8) 利用${\text{M}}$寻找与${\text{Q}}$中序列终点邻近的所有位置点； (9) for 每一个邻近位置点$q$ (10) 将$q$添加到${\text{Q}}$中，更新指纹序列； (11) end for (12) until $p = 0$； (13) for Q中每一条序列${\text{u}}$ (14) if u中存在相同指纹点或对应信号存在$v$值 (15) 将${\text{u}}$从${\text{Q}}$中剔除； (16) else 在${\text{Q}}$中保留${\text{u}}$； (17) end for (18) 将${\text{Q}}$中的所有指纹序列添加到${\text{R}}$中； (19) 将${\text{Q}}$置为空； (20) end for 
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表 2 不同环境下4种算法的定位误差
实验环境 算法类型 平均误差(m) 67%误差(m) 90%误差(m) 空旷大厅和走廊环境 KNN 3.46 4.03 5.91 Inv-FP 2.24 2.40 4.23 iBILL 2.33 2.62 3.94 本文算法 1.72 1.93 2.92 室内复杂
办公环境KNN 3.12 3.81 5.23 Inv-FP 2.11 2.59 3.61 iBILL 2.03 2.51 3.24 本文算法 1.54 1.82 2.77
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