Using Neural Network with Gradient Descent Adaptive Learning Rate Training Algorithm for Geometric Dilution of Precision Approximation
摘要：
Thegeometric dilution of precision is widely used as criterion for selecting the best set of the measurement devices. Some methods had been proposed to find the value of theGeometric Dilution of Precision(GDOP), such as using inverse matrix to solve the linear equation.But it takes a large amount of computing to find the optimal solution. In this paper, we proposed a method which use neural network with gradient descent adaptive learning rate training algorithm to approximate the value of GDOP. By the simulation results, it is suggested to combine the service base station with three others to estimate the position of mobile station.This will significantly reduce the computational complexity.
