Incorporation of neural network-like processors for GPS navigation

Abstract

The solution for the receiver’s position and clock bias using four or more GPS pseudorange measurements involves nonlinear quadratic equations. One of the popular techniques for linearizing the equations and solving them is the least squares (LS) scheme based on an iterative gradient approach. For real-time applications when the solution is to be obtained within a time of the order of 100 ns, a computer often cannot comply with the desired computation time, or high-end computers are too expensive. In this paper various ordinary differential equation formulation schemes, and corresponding circuits of neuron-like analog processors, will be described and several tested in order to ascertain their suitability for GPS navigation processing purposes. The circuits of simple neuron-like analog processors are employed essentially for on-line inversion of matrices, which is usually required for determining LS solutions, as well as dilution of precision (DOP) calculation in standard GPS receivers. Data from single epoch and kinematic positioning experiments will be simulated to validate the effectiveness of the proposed scheme. The properties and performance of the proposed scheme will be assessed and compared to those of the conventional method of matrix inversion.