基于距离像长度特征辅助的雷达目标跟踪

Feature-Aided Tracking Algorithm for Radar Target

  • 摘要: 传统雷达目标跟踪仅利用角度和距离数据,由于获取的测量信息较少,跟踪精度受限。本文利用现代雷达所具有的距离高分辨能力,提出了一种基于距离像长度特征辅助的跟踪模型,并结合先进的非线性滤波算法得到了一种高性能目标跟踪算法—FATUKF。该算法将目标的运动状态与距离像长度信息联系起来,通过增加观测量的维数来提高雷达的跟踪能力。对典型实例的计算机仿真结果表明,基于特征辅助的跟踪算法不仅收敛速度快,且能有效突破传统跟踪算法的理论误差下限,大大提高了雷达跟踪系统的整体性能。

     

    Abstract: In traditional radar target tracking, only angle and range measurement data are used, and the tracking precision can not be further improved because of insufficient target information. In this paper, utilizing the high range resolution profile (HRRP) information of modern radar, a highperformance target tracking algorithm referred to as FAT-UKF is proposed. The algorithm is based on HRRP extentaided tracking model, and is implemented via advanced nonlinear filtering technology. The performance improvement of tracking comes from extra measurement information of target extent, which is inherently related with target motion state. Simulation results for typical application example show that the presented method not only has a high convergence speed, but also can effectively break through the tracking error lower bounds of traditional methods, thus to significantly improve the total performance of tracking system.

     

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