RSS Localization Using an Optimized Fusion of Two Deep Neural Networks

Abstract

The problem of received signal strength (RSS) source localization in wireless sensor networks has raised attention in recent years due to its low complexity. However, the performance of the RSS localization algorithms is degraded due to malicious phenomena such as multipath and fading. Hence, machine learning and especially deep neural networks (DNNs) are applied to solve this problem. So far, convolutional the neural network (CNN), the recurrent neural network (RNN), and multilayer perceptron (MLP) are used to this end. In this letter, neural networks such as MLP, CNN, and RNN are suggested to be combined pairwise. In this regard, we propose to use a fusion of two general neural network localization estimators. To design the combination coefficients, a heuristic approach based on the errors of the training step of two neural networks are suggested. Moreover, two optimized approaches for selecting the combination coefficients are provided based on mean and variances and also based on the mean square error of the combined estimator. The simulation results show improvement of the localization accuracy by fusion of two neural networks.