基于PCA-SMO的CSI指纹定位方法

Wi-Fi信道状态信息（CSI）中包含丰富的特征信息，使得基于CSI的指纹定位方法可以构建更高维度的特征以改善定位精度，但指纹特征中的冗余信息也导致构建的指纹库存储量大、建立定位模型的时间开销变大以及实时定位计算量大等问题. 对此，提出使用主成分分析（PCA）的方法对原始指纹特征进行降维，而后利用序列最小最优化算法（SMO）建立降维后特征与对应位置的回归模型并进行位置预测. 实验结果表明，此算法在有效克服上述问题的同时，平均定位误差为1.25 m，定位误差在2 m之内的累计概率可以达到97%. 

Fingerprint positioning method of CSI based on PCA-SMO

Wi-Fi channel state information(CSI)contains rich feature information,which enables CSI based fingerprint positioning methods to build higher-dimensional features to improve positioning accuracy.However,the redundant information in the fingerprint features also makes the built-up fingerprint database large in storage,and increasing of the time cost of establishing the positioning model becomes large,and the real-time positioning calculation.In this regard,this paper proposes to use principal component analysis(PCA)method to reduce the dimensionality of the original fingerprint features,and then use the sequential minimal optimization(SMO)algorithm to establish the regression model of the reduced feature and the corresponding position and predict the position.The experimental results show that the algorithm in this paper can effectively overcome the above problems,while the average positioning error is 1.25 m,and the cumulative probability of positioning error within 2 m can reach 97%.