基于小样本的高精度室内停车场指纹定位算法

针对传统指纹定位算法在离线阶段采集样本工作量较大的问题，该文利用一种分区拟合值近似法（P-FP）来建立离线指纹库。为了解决在线阶段由于WiFi信号的波动导致的定位精度较低的问题，提出一种基于P-FP的设定阈值的序贯重要性采样（SIR）粒子滤波算法（PS-FP）来优化定位坐标。首先建立了路径的损耗模型，并对室内停车场进行分区拟合，得到每个分区的环境系数；然后用拟合值与实际测量值的差值来建立误差特性矩阵，并重新部署虚拟的参考节点（RP）；最后对离线指纹库进行C均值聚类。通过比较平均定位误差（MLE）寻找PS-FP算法的最优阈值，并采用PS-FP算法来优化在线定位坐标。实验结果表明，在部署很少的RP即获取样本比较少的条件下，PS-FP算法依然能达到较高的定位精度，其平均定位误差约为0.7 m。累积分布函数（CDF）的分析结果表明，采用PS-FP算法在2 m以内的定位误差能达到98%。

A High Accuracy Fingerprint Location Algorithm Based on Small Sample for Indoor Parking Lot

According to the large sampling workload for traditional fingerprint localization algorithms in the offline phase, a partition fitting approximation method (P-FP) is proposed to construct the offline fingerprint database. To solve the problem of the low location accuracy caused by the fluctuation of the WiFi signal in the online phase, the PS-FP algorithm, a sampling importance resampling (SIR) particle filter with threshold based P-FP, is further proposed. The entire indoor parking lot is partitioned, and the environmental coefficients of each partition can be gained by using the polynomial fitting model. And the error characteristic matrix is established with the difference between the fitting values and the actual measured values. Then, the virtual reference points (RPs) is redeployed, and C-means clustering is utilized for the offline fingerprint database. The optimal threshold is obtained by comparing the mean location error (MLE), then the PS-FP is used to optimize the online location coordinates. Experimental results demonstrate that PS-FP can achieve high location accuracy when the RP is fewer, and the mean location error is only about 0.7 m. Cumulative distribution function (CDF) shows that 98% of the location errors are within 2 m obtained by PS-FP.