Novel Deep Hybrid and Ensemble Algorithms for Improving GPS Navigation Positioning Accuracy

GPS (Global Positioning System) has been a widespread system used for various purposes in today's world and it is essential to suggest innovative effective solutions to improve its use and functions. The present study proposes GPS coordinate conversion models based on Machine Learning (ML) and Deep Learning (DL) algorithms in order to "improve accuracy of GPS conversion and positioning services". 23 different models are tested on two different data sets to achieve this purpose. The study primarily aims to improve positioning accuracy of navigation systems by using GPS data through hybrid and ensemble algorithms. The proposed DL-based models are named as GPSCNNs and GPSLSTM. GPSCNNs contain "Xception, VGG16, VGG19, Alexnet, CNN1, CNN2, CNN3" deep learning algorithms in their structure. Of these algorithms, "Xception, VGG16, VGG19, Alexnet" are pre-trained models. "CNN1" consists of 2 Convolution, 2 Average Pool, 1 Flatten, and 5 Dense layers. "CNN2" consists of 1 Convolution, 1 Max Pool, 1 Flatten, and 4 Dense layers. "CNN3" consists of 4 Convolution, 4 Batch Normalization, 2 Max Pool, 1 Flatten, and 3 Dense layers. GPSLSTM contains 1 LSTM and 1 Dense layer in its structure. Raw GPS data are fed into the models as input, which was followed by obtaining information about the features of the data and getting coordinate data as input. The results show that ensemble models provide the most accurate positioning and GPSCNNs and GPSLSTM were quite promising in boosting this accuracy.