目标发生旋转和尺度变化等时会导致跟踪算法出现目标丢失和精度大幅度下降等问题。因此解决目标在运动过程中出现的旋转以及尺度变化问题成为当前的研究热点。提出具有旋转特性的目标跟踪算法,该算法以Hamed等提出的 BACF(background-aware correlation filter)为基准,保留BACF算法中的定位,将笛卡儿坐标系下的目标特征转换到极坐标系下,并采用傅里叶-梅林公式来计算目标旋转角度和尺度的改变,在公开数据集POT上进行验证和比较,发现经过改进后的算法在目标旋转时,矩形框可以跟随目标发生旋转,并且本文算法在POT数据集上的准确率和成功率具有大幅度的提升,分别为0.6561和0.5930,旋转特性准确率和成功率分别为0.9619和0.8527。

Frame processing method offers a model-based approach to Inverse Synthetic Aperture Radar (ISAR) imaging. It also provides a way to estimate the rotation rate of a non-cooperative target from radar returns via the frame operator properties. In this paper, the relationship between the best achievable ISAR image and the reconstructed image from radar returns was derived in the framework of Finite Frame Processing theory. We show that image defocusing caused by the use of an incorrect target rotation rate is interpreted under the FP method as a frame operator mismatch problem which causes energy dispersion. The unknown target rotation rate may be computed by optimizing the frame operator via a prominent point. Consequently, a prominent intensity maximization method in FP framework was proposed to estimate the underlying target rotation rate from radar returns. In addition, an image filtering technique was implemented to assist searching for a prominent point in practice. The proposed method is justified via a simulation analysis on the performance of FP imaging versus target rotation rate error. Effectiveness of the proposed method is also confirmed from real ISAR data experiments.