一种WiFi混合滤波的RSSI室内定位算法

A WiFi hybrid filtering RSSI indoor positioning algorithm

针对接收信号强度指示(RSSI)在基于WiFi的室内定位应用中易受到环境影响出现波动、突变进而导致定位精度降低的问题,提出了一种WiFi混合滤波的RSSI室内定位算法。首先,根据WiFi信号距离衰减模型设计一种自适应阈值滤波器剔除观测信号异常值;然后,根据自适应阈值滤波、卡尔曼(Kalman)滤波、均值(Mean)滤波设计了一种混合滤波算法(ATKM)对RSSI数据进行滤波;最后,提出了一种基于斯皮尔曼-欧几里得距离的加权K近邻算法(SEWKNN)进行位置估计。在真实环境下的实测结果表明,本文提出的ATKM滤波算法能显著降低RSSI信号的波动,提出的SEWKNN算法在室内环境中平均定位误差为1.17 m,在走廊环境中平均定位误差为1.53m,相比传统的WKNN算法平均定位误差分别下降18.18%和16.84%。

To address the challenge of fluctuations and mutations in received signal strength indication(RSSI)caused by environmental influences in WiFi-based indoor positioning applications,which ultimately leads to reduced accuracy,we propose a WiFi hybrid filtering RSSI indoor positioning algorithm.Firstly,an adaptive threshold filter is developed based on the WiFi signal distance attenuation model to effectively eliminate outliers from the observed signal.Next,a hybrid filtering algorithm(ATKM)was designed based on adaptive threshold filtering,Kalman filtering,and Mean filtering to filter RSSI data.Finally,we propose a weighted k-nearest neighbor algorithm(SEWKNN)for position estimation,which utilizes Spearman-Euclidean distance.Experimental results conducted in real-world environments demonstrate that the ATKM filtering algorithm significantly reduces the fluctuations in RSSI signals.The SEWKNN algorithm achieves an average positioning error of 1.17 m in indoor environments and 1.53 m in corridor environments.Compared to the traditional WKNN algorithm,the proposed SEWKNN algorithm reduces the average positioning error by 18.18%and 16.84%respectively,indicating its superior performance.