Abstract: Aimed at the problem that the performance of convex combination track-to-track fusion algorithm degrades in the case that the process noise of dynamic system does not equal zero, RhS smoother was introduced to improve the performance. hhe traditional RhS smoothing algorithm can be work when all the filtering data arc obtained, and the outputis delayed largely. Piecewise RTS smoothing algorithm was presented to solve the problem. On the one hand, track-to-track performance can be enhanced; on the other hand, real-time ability can be remained. Furthermore, aimed at the calculation ability of local node owns or not, Smoothing First and Fusing Next (SFFN) and Fusing First and Smoothing Next (FFSN) algorithms based on the piecewise RTS smoothing algorithm were presented according to the chance of implementing the smoothing process. The new methods exceed the performance of the traditional convex combination fusion algorithm and the optimal fusion algorithm in a wide range of process noise. Simulation results show the new algorithms' validity and superiority.

Key words: Data fusion,Tracking,Track to-track fusion,Piecewise RTS smoothing,Filtering