CAJ | 학술논문

针对传统的 EKF-IMM 算法鲁棒性较差等问题,提出了一种基于强跟踪滤波器(STF)的交互式多模型算法.该算法通过引入强跟踪滤波器(STF)的渐消因子,实现了对滤波器增益的实时调节,从而提高了系统对机动目标的自适应跟踪能力和跟踪精度.仿真结果表明,在目标不发生机动时,该算法和 EKF-IMM 算法的跟踪效果相近,在目标发生强机动时,该算法在径向速度和方位角的跟踪精度要优于EKF-IMM算法;提出的算法具有更优的机动目标跟踪性能.
침대전통적 EKF-IMM 산법로봉성교차등문제,제출료일충기우강근종려파기(STF)적교호식다모형산법.해산법통과인입강근종려파기(STF)적점소인자,실현료대려파기증익적실시조절,종이제고료계통대궤동목표적자괄응근종능력화근종정도.방진결과표명,재목표불발생궤동시,해산법화 EKF-IMM 산법적근종효과상근,재목표발생강궤동시,해산법재경향속도화방위각적근종정도요우우EKF-IMM산법;제출적산법구유경우적궤동목표근종성능.
@@@@Aiming at the problem of the robustness of EKF-IMM is below average, an interacting multiple models algorithm using Strong Tracking Filter(STF)is proposed. Through introducing a fading factor of strong tracking filter, this algorithm realizes the realtime adjusting the gain of the filters, and updating the adaptive tracking performance and tracking precision for maneu-vering targets accordingly. The Monte Carlo simulation result shows that this algorithm has the same tracking effect for non-maneuvering target as EKF-IMM, and the tracking performance for maneuvering target is superior to EKF-IMM on radial velocity and azimuth. The simulation results verify that this algorithm has better performance than EKF-IMM in tracking maneuvering targets.