基于 DPC 指纹子空间匹配的室内 WiFi 定位方法

针对无线接收信号强度 (RSS) 受传播环境突发噪声影响从而引起指纹定位较大误差的问题,本文提出了一种指纹子 空间匹配结合密度峰值聚类 (DPC) 的定位算法,有效避免大误差点。 首先通过在线阶段目标 RSS 信号的接入点 (AP) 覆盖 向量,确定有效的参考位置点,并划分多个指纹子空间,利用改进的 WKNN 算法估计目标在每个子空间内的位置;最后利用 DPC 算法选取决策值最大的 S 个估计位置确定目标。 所提算法简单,不需要离线阶段的学习过程训练定位模型,尤其适合存在 大量 AP 的大范围室内定位区域。 实际环境中的定位实验表明,基于 DPC 的指纹子空间匹配算法比 WKNN 算法的定位精度提 升了 25% 左右,且在参考点分布密度为 1. 8 m × 1. 8 m 的实验条件下基本消除了 4 m 以上的大定位误差,有效提高了定位方法 的整体性能。

WiFi fingerprint based indoor positioning with subspace matching and DPC

There is large error caused by the burst noise of the received signal strength (RSS) in the indoor fingerprint positioning. To address this issue, a subspace matching method combined with the algorithm of clustering by fast search and find of density peaks (DPC) is proposed. In this way, the large positioning error could be effectively avoided. Specifically, the coverage vector of the access points in the online RSS is used to select a subset of the reference points and the subspaces of the radio map. An improved weighted K-nearest neighbors ( WKNN) approach is applied to achieve estimation. Then, the DPC algorithm is used to select S estimated positions with the largest decision values to determine the position of the target. The simple algorithm needs no learning process in the offline stage, which is especially suitable for large indoor areas with a lot of access points. Compared with the WKNN algorithm, experimental results show that the proposed method improves the positioning accuracy by about 25% . The large positioning error of more than 4 m is eliminated when the reference points is 1. 8 m×1. 8 m. The overall positioning performance is improved effectively.