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An indoor location system based on multilayer artificial neural network(ANN) with area division is proposed.The characteristics of recorded signal strength(RSS),or signal to noise ratio(SNR) from each available access points(APs),are utilized to establish the radio map in the off-line phase.And in the on-line phase,the two or three dimensional coordinates of mobile terminals(MTs) are estimated according to the similarity between the new recorded RSS or SNR and fingerprints pre-stored in radio map.Although the feed-forward ANN with three layers is sufficient to describe any nonlinear mapping relationship between inputs and outputs with finite discontinuous points,the efficient inputs for better training performances are difficult to be determined because of complex and dynamic indoor environment.Then,the discussion of distance relativity for different signal characteristics and optimal strategies for multi-mode phenomenon avoidance is presented.And also,the feasibility and effectiveness of this method are verified based on the experimental comparison with normal ANN without area division,K-nearest neighbor(KNN) and probability methods in typical office environment.
An indoor location system based on multilayer artificial neural network (ANN) with area division is proposed. Characteristics of recorded signal strength (RSS), or signal to noise ratio (SNR) from each available access points (APs) the radio map in the on-line phase, the two or three dimensional coordinates of mobile terminals (MTs) are estimated according to the similarity between the new recorded RSS or SNR and fingerprints pre-stored in radio map.Although the feed-forward ANN with three layers is sufficient to describe any nonlinear mapping relationship between inputs and outputs with finite discontinuous points, the efficient inputs for better training plays are difficult to be determined because of complex and dynamic indoor environment.Then, the discussion of distance relativity for different signal characteristics and optimal strategies for multi-mode phenomenon avoidance was presented. Also, the feasibility and effectiveness of this method are verified based on the experimental comparison with normal ANN without area division, K-nearest neighbor (KNN) and probability methods in typical office environment.