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SLOW-MOVING EMITTER PASSIVE RANGING USING AN BEARING-ONLY TRACKING FILTER AND INPUT ESTIMATION

机译:使用仅轴承的跟踪滤波器和输入估计,对缓慢移动的发射器进行被动测距

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A bearing-only tracking algorithm to locate the slow moving target-ship emitter source position from a missile is presented. The algorithm employs an extended Kalman filter (EKF) combined with input estimation (IE) skill instead of the conventional EKF and uses the angular measurements from an onboard direction finder (DF). The dynamic relationship between the target-ship and missile motion is formulated in hybrid coordinate, which yields good noise-handling performance. This research formulates the dynamic model of a missile-target in midcourse phase for identification with an un-modeled target maneuvering input covering possible modeling error which the modeling error is also the major concerning issue in the passive ranging. Moreover, this paper presents a novel on-line estimation approach, adaptive filter, to tracking the slow moving target from a bearing-only data. The combined scheme of the adaptive IE filter markedly improves the tracking accuracy and trajectory shaping capability as well. Simulation results reveal that the proposed algorithm is superior to that of the pure conventional filter algorithm.
机译:提出了一种仅用于跟踪的定位算法,用于从导弹中定位缓慢移动的目标飞船发射器源位置。该算法采用结合了输入估计(IE)技能的扩展卡尔曼滤波器(EKF)代替了传统的EKF,并使用了来自机载测向仪(DF)的角度测量值。目标舰船与导弹运动之间的动力学关系是在混合坐标系下制定的,具有良好的噪声处理性能。这项研究建立了导弹目标的中段动力学模型,用于识别未建模的目标机动输入,该模型涵盖了可能的建模误差,而建模误差也是被动测距中的主要问题。此外,本文提出了一种新颖的在线估计方法,即自适应滤波器,用于从仅方位数据中跟踪缓慢移动的目标。自适应IE滤波器的组合方案也显着提高了跟踪精度和轨迹整形能力。仿真结果表明,该算法优于纯常规滤波算法。

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