Positioning Improvement with Multiple GPS Receivers Based on Shallow Asymmetric Neural Network

Abstract

A positioning system in a specific space is for the purpose of determining the location of an object. The Global Positioning System (GPS) is the most popular and valuable development for classical navigation. However, it may not always be precise and available due to the effects of multi-path propagation and signal attenuation. Thus, we need some additional skills to keep its stability and accuracy or improve its performance. Based on the technique of neural networks, the positioning information from multiple GPS receivers is composed to obtain a better version which is more accurate and stable and thus can be applied to advanced applications. Particularly, the concepts of shallow and asymmetric neural networks are used in this work. Our design possesses fewer hidden layers via the former property and further reduces the connections of classical Fully Connected Neural Networks (FCNNs) via the latter property. Hence, it takes very little time to realize the training and predicting procedures. Also, it will help promote several works in practice, such as the implementation of embedded systems. Finally, a practical test, called the vehicular road test, is utilized to guarantee the level of confidence in the improvement of our algorithm.