In this paper, two algorithms of Global Positioning System
based attitude determination are
proposed. The first algorithm extends the Kalman filter approach to determine
the integer
ambiguity and the orientation that is needed in a typical
gps-based attitude determination
problem. The second algorithm explores the mean field annealing neural
network approach,
which is a combination of the competitive Hopfield neural network and the
stochastic
simulated annealing technique, to resolve the optimal attitude problems.
A test platform is set
up for verifying these algorithms. The two algorithms are further compared
in terms of
computation speed and convergence rate.