Aiming at the problem that during indoor positioning,affected by wireless communication distance and environment,ZigBee observation signal has significant random noise,which leads to certain deficiencies in the accuracy and stability of positioning results,the paper proposed a RSSI indoor location algorithm based on iterative mean filter:Gaussian filter was used to eliminate the influence of low probability observation events in the process of data acquisition,and the path loss model was established according to received signal strength indicator ranging theory,so as to estimate the distance between signal receiving node and broadcast beacon node;then,in the process of positioning solution,the iterative mean filter algorithm was integrated on the basis of the linear least squares algorithm to suppress the influence of random errors on the positioning results;finally,the self-developed CC2530 ZigBee module was used to verify the feasibility and stability of the algorithm. Results showed that the point position accuracy of the equal-weighted least squares algorithm would be about 0.64 m,and the accuracy of the least squares algorithm could be improved to 0.52 m after distance-weighted determination;by integrating the iterative mean filter into the equal-weighted least squares algorithm,the accuracy could reach about 0.5 m,which would be equivalent to that of the distance-weighted least squares;at the same time,the point accuracy of integrating two methods of the distance-weighed and the iterative mean filter could be improved to about 0.35 m.
ZigBeeiterative mean filterindoor positioning algorithmsignal strengthweighted least squares
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