A Weighted Recursive Least Square Relative Navigation Algorithm Based on Rendezvous-Radar Measurements

The conventional approach used for the rendezvous-radar-based relative navigation algorithms is chiefly based on Kalman filter, which contains lots of matrix calculations. This paper presented a weighted recursive least square (RLS) relative navigation algorithm, which compared with the Kalman filter algorithms, could obviously reduce computational requirements. The observation equation of the algorithm presented was based on the rendezvous radar measurements and the state transfer matrix. In order to ensure the algorithm had a good performance, a method that calculated the initial values using the measurements was employed. For validating the efficiency and accuracy of the algorithm, a 6-state Kalman filter and 9-state filter were respectively compared with. Results of the simulation indicated the initial values were appropriate. Although the accuracy of the algorithm was poorer than the 9-state filter, it also met the demand of the relative navigation. On the other hand, the computation time needed by the algorithm was much less than the 6-state filter.