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Space-time registration of radar and ESM using unscented Kalman filter

机译：使用无味卡尔曼滤波器的雷达和ESM时空配准

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摘要

Space and time alignments are the prerequisites for the successful fusion of multiple sensors. A space-time registration model is proposed to estimate the system biases and to perform time synchronization together for mobile radar and electronic support measure (ESM) systems. A space-time registration model for radar and ESM is first developed, and an unscented Kalman filter (UKF) is proposed to estimate the space-time biases and target states simultaneously. The posterior Cramer-Rao bounds (PCRBs) are derived for the proposed UKF registration algorithm for ESM detection probability less than or equal to one. Theoretical analyses are performed to evaluate the accuracy and robustness of the proposed method. Computer simulations show that the UKF registration algorithm is indeed effective and robust for different radar and ESM tracking scenarios.
机译：空间和时间对齐是成功融合多个传感器的前提。提出了一种时空配准模型来估计系统偏差并一起为移动雷达和电子支持措施（ESM）系统执行时间同步。首先建立了雷达和ESM的时空配准模型，并提出了一种无味卡尔曼滤波器（UKF）来同时估计时空偏差和目标状态。对于建议的UKF注册算法，对于ESM检测概率小于或等于1，推导了后Cramer-Rao界（PCRB）。进行理论分析以评估所提出方法的准确性和鲁棒性。计算机仿真表明，UKF配准算法对于不同的雷达和ESM跟踪场景确实有效且健壮。

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