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首页> 外文期刊>IEEE Transactions on Aerospace and Electronic Systems >3-D track initiation in clutter using 2-D radar measurements
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3-D track initiation in clutter using 2-D radar measurements

机译:使用2-D雷达测量在杂波中启动3-D轨迹

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We present an algorithm for initiating 3-D tracks using range and azimuth (bearing) measurements from a 2-D radar on a moving platform. The work is motivated by the need to track possibly low-flying targets, e.g., cruise missiles, using reports from an aircraft-based surveillance radar. Previous work on this problem considered simple linear motion in a flat Earth coordinate frame. Our research extends this to a more realistic scenario where the Earth's curvature is also considered. The target is assumed to be moving along a great circle at a constant altitude. After the necessary coordinate transformations, the measurements are nonlinear functions of the target state and the observability of target altitude is severely limited. The observability, quantified by the Cramer-Rao lower bound (CRLB), is very sensitive to the sensor-to-target geometry. The paper presents a Maximum Likelihood (ML) estimator for estimating the target motion parameters in the Earth-centered Earth-fixed (ECEF) coordinate frame from 2-D range and angle measurements. In order to handle the possibility of false measurements and missed detections, which was not considered previously, we use the Probabilistic Data Association (PDA) algorithm to weight the detections in a frame. The PDA-based modified global likelihood is optimized using a numerical search. The accuracies obtained by the resulting ML-PDA estimator are quantified using the CRLB for different sensor-target configurations. It is shown that the proposed estimator is efficient, that is, it meets the CRLB. Of particular interest is the achievable accuracy for estimating the target altitude, which is not observed directly by the 2-D radar, but can be only inferred from the range and bearing observations.
机译:我们提出了一种使用移动平台上的2-D雷达的距离和方位角(方位角)测量值来启动3-D轨迹的算法。这项工作的动机是需要使用基于飞机的监视雷达报告来跟踪可能低空飞行的目标,例如巡航导弹。关于此问题的先前工作是在平坦的地球坐标系中考虑简单的线性运动。我们的研究将其扩展到更现实的场景,其中还考虑了地球的曲率。假定目标以恒定高度沿大圆运动。经过必要的坐标转换后,测量值成为目标状态的非线性函数,并且目标高度的可观察性受到严重限制。由Cramer-Rao下限(CRLB)量化的可观察性对传感器到目标的几何形状非常敏感。本文提出了一种最大似然(ML)估计器,用于从二维距离和角度测量值估算以地球为中心的地球固定(ECEF)坐标系中的目标运动参数。为了处理错误的测量和错过的检测的可能性(以前没有考虑过),我们使用概率数据协会(PDA)算法对帧中的检测进行加权。使用数字搜索对基于PDA的修改后的全局似然进行优化。对于不同的传感器目标配置,使用CRLB量化由所得ML-PDA估计器获得的精度。结果表明,提出的估计量是有效的,也就是说,它满足CRLB。特别令人感兴趣的是估算目标高度的可达到的精度,该精度不能直接由二维雷达观察到,而只能从范围和方位观察中推断出来。

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