  All spacecraft attitude estimation methods are based on Wahba's optimization
problem. This problem can be reduced to finding the largest eigenvalue and the
corresponding eigenvector for Davenport's $K$-matrix. Several iterative
algorithms, such as QUEST and FOMA, were proposed, aiming at reducing the
computational cost. But their computational time is unpredictable because the
iteration number is not fixed and the solution is not accurate in theory.
Recently, an analytical solution, ESOQ was suggested. The advantages of
analytical solutions are that their computational time is fixed and the
solution should be accurate in theory if there is no numerical error. In this
paper, we propose a different analytical solution to the Wahba's problem. We
use simple and easy to be verified examples to show that this method is
numerically more stable than ESOQ, potentially faster than QUEST and FOMA. We
also use extensive simulation test to support this claim.
