Due to the problem that the nodes having the same distance but different position in the complex environment, brings shortage to accuracy and stability of indoor positioning, a new indoor positioning algorithm with Dynamic Environment Attenuation Factor (DEAF) was proposed. This algorithm built a DEAF model and redefined the way to assume the value. In this algorithm, particle filtering method was firstly used to smooth the Received Signal Strength Indication (RSSI); then, the DEAF model was used to calculate the estimation distance of the node; finally, the trilateration was used to get the position of the target node. Comparative experiments had been done using several filtering models, and the results show that this dynamic environment attenuation factor model combined with particle filtering can resolve the problem of the environment difference very well. This algorithm reduces the mean error to about 0.68 m, and the result has higher positioning accuracy and good stability.

In order to solve the problem of high-precise indoor positioning calculation using received signal strength, a novel WMKF (Kalman Filtering and Weighted Median) positioning algorithm was proposed. The algorithm was different from previous indoor localization algorithms. Firstly, Kalman filter method was used to smooth random error, and weighted median method was made to reduce the influence of gross error, then the log distance path loss model was used to obtain the decline curve and calculate the estimated distance. Finally, the centroid method was used to get the position of the target node. The experimental results show that, this WMKF algorithm initially improve that the poor stability of positioning in a relatively complex environment, and effectively enhanced the positioning accuracy, making the accuracy between 0.81m to 1m.