Fast data fusion algorithm for building navigation base in formation flying

When multiple miniature vehicles with inter-vehicle distance measurement ability collaborate in a formation, navigation base can be established without external assisting systems. Shared navigation data is fused to compensate the drift of navigation system and the noise from distance measurement. Existing algorithms like Least Squared Error (LSE) can process nonlinear measurements at high speed, but the noise level of result is high. Kalman Filtering can achieve lower noise level, but it needs huge amount of calculation. The amount of calculation rendered it unsuitable for onboard computing systems. To reduce the computation load of the data fusion algorithm and maintain high precision, Fast Data Fusion (FDF) algorithm which combines LSE and KF is proposed. In this algorithm, LSE is used as the preprocessing stage to build a coarse estimation and KF is then used to further alleviate noises in the pre-processed estimations. In FDF, the dynamic model can be much simpler than KF, so the computation load is reduced while the result still has the advantage of high precision. Simulation results show that, proposed method uses no more than 2.5% of computation time of Kalman Filtering and achieved matching precision.