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Improved Target Tracking using Kinematic Measurements and Target Orientation Information

机译:使用运动学测量和目标方位信息改进的目标跟踪

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This paper discusses a target tracking system that provides improved estimates of target states using target orientation information in addition to standard kinematic measurements. The objective is to improve state estimation of highly maneuverable targets with noisy kinematic measurements. One limiting factor in obtaining accurate state estimates of highly maneuvering targets is the high level of uncertainty in velocity and acceleration. The target orientation information is helpful in alleviating this problem to accurately determine the velocity and acceleration components. However, there is no sensor that explicitly measures target orientation. In this paper, the Observable Operator Model (OOM) is used together with multiple sensor information to estimate target orientation measurement. This is done by processing the sensor feature measurements from different aspect angles and the estimated target orientation measurement is used in conjunction with kinematic measurements to conclusively estimate target states. Simulation results show that, the incorporation of target orientation can enhance the tracking performance in the presence of fast moving and/or maneuvering targets. In addition, the Posterior Cramer-Rao lower bound (PCRLB) that quantifies the achievable performance is derived. It is shown that the proposed estimator meets the PCRLB.
机译:本文讨论了一种目标跟踪系统,除了标准的运动学测量方法外,该系统还使用目标方向信息提供了对目标状态的改进估算。目的是通过运动学测量来改善对高度机动目标的状态估计。获得高度机动目标的精确状态估计值的一个限制因素是速度和加速度的高度不确定性。目标方向信息有助于缓解此问题,从而准确确定速度和加速度分量。但是,没有传感器可以明确地测量目标方向。在本文中,可观测算子模型(OOM)与多个传感器信息一起用于估计目标方位测量。这是通过处理来自不同纵横比的传感器特征测量结果而完成的,并将估计的目标方向测量值与运动学测量结合使用以最终估计目标状态。仿真结果表明,在快速移动和/或机动目标的存在下,结合目标定向可以增强跟踪性能。另外,得出量化可实现性能的后部Cramer-Rao下界(PCRLB)。结果表明,提出的估计量满足PCRLB。

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