首页> 外文会议>IASTED International Conference on Antennas, Radar, and Wave Propagation >SLOW-MOVING EMITTER PASSIVE RANGING USING AN BEARING-ONLY TRACKING FILTER AND INPUT ESTIMATION
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SLOW-MOVING EMITTER PASSIVE RANGING USING AN BEARING-ONLY TRACKING FILTER AND INPUT ESTIMATION

机译:使用仅辅助跟踪滤波器和输入估计进行缓慢移动的发射器无源测距

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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:介绍了仅用于从导弹定位慢速移动目标船发射极源位置的轴承跟踪算法。该算法采用扩展卡尔曼滤波器(EKF)与输入估计(IE)技能组合而不是传统的EKF,并使用来自车载方向查找器(DF)的角度测量。目标船舶和导弹运动之间的动态关系在混合坐标中配制,从而产生良好的噪声处理性能。该研究在中间阶段中制定了导弹目标的动态模型,以识别未建模的目标机动输入,涵盖建模误差的可能的建模误差也是被动测距中的主要问题。此外,本文提出了一种新的在线估计方法,自适应滤波器,以跟踪轴承数据的慢速移动目标。自适应IE滤波器的组合方案显着提高了跟踪精度和轨迹整形能力。仿真结果表明,该算法优于纯传统滤波算法的算法。

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