基于分段RTS平滑的凸组合航迹融合算法

Convex Combination Track-to-track Fusion Algorithms Based on Piecewise RTS Smoothing

针对凸组合航迹融合算法在过程噪声不为零的情况下性能下降的问题,引入了RTS平滑算法来提高融合性能。由于传统的RTS平滑算法是得到全部滤波结果之后才执行逆向平滑过程,造成输出延迟,为此,提出了分段RTS平滑算法,一方面可以提高航迹融合性能,另一方面能够保证融合过程中的实时性。在融合过程中,针对局部节点有无额外计算能力的不同情况,结合实施平滑步骤的时机,提出了基于分段RTS平滑的先平滑再融合和先融合再平滑两种改进的凸组合航迹融合方法。这两种方法在不同过程噪声水平下,性能表现都超过凸组合融合算法和最优融合算法。仿真结果表明了新算法的有效性和优越性。

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, RTS smoother was introduced to improve the performance. The traditional RTS smoothing algorithm can be work when all the filtering data are obtained, and the output is delayed largely. Piecewise RTS smoothing algorithm was presented to solve the problem. On the one hand, track-totrack 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.