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.

中文翻译：

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使用无源雷达将飞机定向纳入自动目标识别

关于无源雷达利用“机会照明器”的大多数研究（例如FM广播）都集中在检测和跟踪目标上。本研究探索了向此类系统添加自动目标识别（ATR）功能。此处描述的ATR算法使用在短时间内收集的潜在目标的雷达横截面（RCS）。接收到的信号模型说明了飞机的位置和方向，传播损耗以及天线增益模式。一种提出的算法使用协调飞行模型来估计飞机方向，而一种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。在两种情况下，都使用从跟踪算法获得的速度测量值来估计方向。在目标库中模拟每架飞机的雷达返回，就像每架飞机都执行与系统检测到的目标相同的机动一样。为了提高结果的鲁棒性，更复杂的算法通过动态编程共同优化了可行的定向轮廓和目标类型。