Nonlinear Dynamic Estimation of the Orientation Angles of a Moving Object from Distributed Satellite Measurements

Abstract

We solve the problem of dynamic nonlinear stochastic estimation of moving object orientation parameters from measurements of spatially separated satellite receivers. It is shown that the use of the Euler–Krylov angles as the current parameters of object orientation provides for a sharp reduction in the dimension of the proposed estimation algorithm as compared with the existing algorithms. An example is given illustrating the effectiveness of the developed stochastic estimation algorithm.