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Multiple model estimator for a tightly coupled HRR automatic target recognition and MTI tracking system

机译:紧密耦合的HRR自动目标识别和MTI跟踪系统的多模型估计器

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Abstract: The goal of this research is to exploit couplings between tracking and ATR systems employing high range resolution radar (HRRR) and moving target indicator (MTI) measurements. As will be shown, these systems are coupled via pose, kinematic, and association constraints. Exploiting these couplings results in a tightly coupled system with significantly improved performance. This problem deals with two different types of spaces, namely the continuous space kinematics (e.g. position and velocity) and the discrete space target type. A multiple model estimator (MME) was chosen for this problem. The MME consist of a bank of extended Kalman filters (one for each target type). The continuous space kinematics are dealt with via these extended Kalman filter. Further, the probability of each Kalman filter is computed and used to determine the corresponding discrete space target probability. Presented in this paper are empirical results that show improvement over conventional techniques. !12
机译:摘要:本研究的目的是利用高分辨雷达(HRRR)和运动目标指示器(MTI)测量来利用跟踪和ATR系统之间的耦合。如将显示的,这些系统通过姿势,运动学和关联约束耦合。利用这些联轴器可以产生性能显着提高的紧密联轴器系统。该问题涉及两种不同类型的空间,即连续空间运动学(例如位置和速度)和离散空间目标类型。为此问题选择了一个多模型估计器(MME)。 MME由一组扩展的卡尔曼滤波器组成(每种目标类型一个)。连续的空间运动学通过这些扩展的卡尔曼滤波器进行处理。此外,计算每个卡尔曼滤波器的概率,并将其用于确定相应的离散空间目标概率。本文提出的经验结果表明,与传统技术相比有所改进。 !12

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