A Neural Network Based Recursive Least Square Multilateration Technique for Indoor Positioning

 Fast emerging wireless indoor positioning enables many location based services. However, fluctuation receive signal strength due to multipath propagation is a major challenge. Different Machine Learning (ML) approaches are attempted to improve the accuracy in indoor positioning. In this paper, a hybrid technique combining Recursive Least Square (RLS) with Artificial Neural Network (ANN) has been presented to solve a multilateration problem with different sets of anchor nodes. Instead of using directly measured distances, estimated distances using ANN are used in RLS implementation. Results from the hybrid RLS-ANN are compared with pure RLS, pure least square (LS) and LS-ANN approaches. Hybrid RLS-ANN provides the least Root Mean Square Error (RMSE) among all the techniques and improves the accuracy up to 80% compared to the pure LS multilateration technique. Complexity of the proposed technique is relatively low with good accuracy