基于室内指纹定位的优化算法

针对室内环境中WIFI信号强度易受外界干扰，其不稳定性使得在指纹数据库中进行匹配时准确性较低，定位精度不高的问题，提出一种基于室内指纹定位的优化算法。该算法分别对指纹数据库和匹配算法进行优化。数据库优化采用限幅和滑动平均滤波进行预处理，并根据室内环境分配采样点所属区域ID，构建多维指纹数据库；匹配算法优化首先根据SVM对待定位点分类，获取其对应的区域id，再将欧氏距离、曼哈顿距离和切比雪夫距离三者结合得到位置估计。最后，结合PDR算法将得到的步长与航向角一同进行粒子滤波实现定位。实验表明:本文的算法将定位精度提高了13.92%。

Optimization Algorithm Based on Indoor Fingerprint Positioning

For indoor environment, the WIFI signal strength is susceptible to external interference. Due to its instability, the accuracy of matching in the fingerprint database is low and the positioning accuracy is not high. An optimization algorithm based on indoor fingerprint positioning is proposed. This algorithm optimizes the fingerprint database and matching algorithm respectively. Database optimization uses limiting and moving average filtering for pre-processing. According to the indoor environment, assign the ID of the area to which the sampling point belongs to build a multidimensional fingerprint database. The matching algorithm is optimized to classify the points to be located according to the SVM and obtain the corresponding area ids. The Euclidean distance, Manhattan distance and Chebyshev distance are combined to obtain a position estimate. Finally, combined with the PDR algorithm, the obtained step size and heading angle are subjected to particle filtering to achieve positioning. The algorithm in this paper improves the positioning accuracy by 13.92%.