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Incorporation of aircraft orientation into automatic target recognition using passive radar

机译:使用被动雷达将飞机定向纳入自动目标识别

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摘要

Most research regarding passive radar exploiting 'illuminators of opportunity', such as FM radio, has focused on detecting and tracking targets. This study explores adding automatic target recognition (ATR) capabilities to such systems. The ATR algorithms described here use the radar cross-section (RCS) of potential targets, collected over a short period of time. The received signal model accounts for aircraft position and orientation, propagation losses, and antenna gain patterns. One proposed algorithm uses a coordinated flight model to estimate aircraft orientations, while a more sophisticated algorithm uses an extended Kalman filter to estimate the target orientations along with measures of uncertainty in those estimates. In both cases, the orientations are estimated using velocity measurements obtained from a tracking algorithm. The radar return of each aircraft in the target library is simulated as though each is executing the same manoeuvre as the target detected by the system. To improve the robustness of the result, the more sophisticated algorithm jointly optimises over feasible orientation profiles and target types via dynamic programming.
机译:关于被动雷达利用“机会照明器”的大多数研究,如FM收音机,专注于检测和跟踪目标。本研究探讨为此类系统添加自动目标识别(ATR)功能。这里描述的ATR算法使用潜在目标的雷达横截面(RCS),在短时间内收集。接收的信号模型用于飞机位置和方向,传播损耗和天线增益模式。一种提出的算法使用协调的飞行模型来估计飞机取向,而更复杂的算法使用扩展的卡尔曼滤波器来估计目标方向以及这些估计中的不确定性的测量。在这两种情况下,使用从跟踪算法获得的速度测量来估计取向。模拟目标库中的每架飞机的雷达返回,尽管每个都是执行与系统检测到的目标相同的操纵。为了提高结果的稳健性,更复杂的算法通过动态编程共同优化可行的定向简档和目标类型。

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