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首页> 外文期刊>Aerospace and Electronic Systems, IEEE Transactions on >Maximum Likelihood Approach to the Estimation and Discrimination of Exoatmospheric Active Phantom Tracks using Motion Features
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Maximum Likelihood Approach to the Estimation and Discrimination of Exoatmospheric Active Phantom Tracks using Motion Features

机译：利用运动特征估计和区分大气外主动幻象轨迹的最大似然法

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摘要

An optimal kinematics-based discrimination algorithm is presented for the nearly real-time discrimination of exoatmospheric active phantom (deception) tracks against other physical targets (PTs). The new approach uses a batch-processing maximum likelihood estimator (MLE) to precisely estimate the deception range of decoys from raw radar tracking measurements by using motion features. Hence once these parameters are estimated, they can serve as direct statistics to initiate a discrimination. By augmenting the state vector with deception parameters, explicit expressions of motion models of decoys in the radar centered East-North-Up (ENU) coordinate system (CS) and spherical-CS are derived. Based on these models, the theoretical Cramer-Rao lower bound (CRLB) and the observability of the deception parameters are also analyzed. A Levenberg-Marquardt method is employed to obtain more robust estimate of these parameters, and the estimated parameters combined with the CRLB are used for designing discrimination algorithm. The simulations verify the feasibility of the algorithm. Furthermore, the discrimination performance due to the influence of radar position, data rate, and radar measurement error are also covered. The advantage of the algorithm lies in that it can position the warhead precisely as well as discriminating active decoys simultaneously when compared with the traditional 6-dimensional orbit determination methods.
机译：提出了一种基于运动学的最优判别算法，用于对大气外活动体模（欺骗）轨迹与其他物理目标（PT）进行近乎实时的判别。新方法使用批处理最大似然估计器（MLE）通过使用运动特征从原始雷达跟踪测量值精确估计诱饵的欺骗范围。因此，一旦估计了这些参数，它们就可以用作直接统计以启动判别。通过用欺骗参数增加状态向量，推导了以雷达为中心的东-北-北（ENU）坐标系（CS）和球面CS中诱饵运动模型的明确表达式。基于这些模型，还分析了理论Cramer-Rao下界（CRLB）和欺骗参数的可观察性。采用Levenberg-Marquardt方法获得这些参数的更可靠的估计，并将估计的参数与CRLB结合用于设计判别算法。仿真结果验证了该算法的可行性。此外，还涵盖了由于雷达位置，数据速率和雷达测量误差的影响而产生的识别性能。该算法的优点在于，与传统的6维轨道确定方法相比，它可以精确定位弹头，并同时区分主动诱饵。

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