IN-BODY RANGING FOR ULTRA-WIDE BAND WIRELESS CAPSULE ENDOSCOPY USING NEURAL NETWORKS BASED ON PARTICLE SWARM OPTIMIZATION

We consider the problem of accurate in-body ranging for localization of a wireless capsule endoscope utilizing ultra-wide band (UWB) signaling. In this context, we explore the joint use of neural network structures and learning algorithms based on metaheuristics, an example of which is particle swarm optimization (PSO). The contributions of this paper are three-fold. First, we undertake a systematic performance analysis of the PSO technique for UWB-based in-body ranging and propose an improved version of the PSO algorithm. Second, we quantitatively compare the performance of PSO techniques against more traditional learning algorithms, such as Bayesian Regularization, Levenberg-Marquardt and Single Conjugate Gradient. Third, we quantify the impact of activation functions used to define the neural network structure on performance. Our results indicate that PSO-based techniques can outperform traditional techniques by as much as 44%, depending on the activation functions used in the neural network.