Comparison of three GPS-based attitude determination algorithms

In this paper, we conduct a comparative study on three GPS-based attitude determination algorithms. Our goal is to assess the advantages and limitations of several existing GPS-based attitude determine algorithms and develop an integrated method to explore the potential synergistic effect. The methods under investigation include ambiguity function, neural network, and Kalman filter approaches. These methods are compared in terms of modeling complexity, computational requirement, convergence rate, and real time implementation. An integrated method is then proposed to achieve the combined benefits of each method.