基于改进Kalman滤波的可见光极小目标TBD跟踪方法

针对无人机可见光图像极小目标跟踪问题，本文提出一种基于改进卡尔曼滤波的 (Tracking before detection,TBD)跟踪方法。首先利用检测算法定位目标位置作为卡尔曼滤波的测量值，检测过程中的匹配相似度参数作为卡尔曼滤波测量噪声协方差矩阵的参照依据，其次利用卡尔曼滤波建立跟踪框架预测下一帧的目标位置，最后检测模块以预测位置为 参考位置进行局部搜索，完成整个检测跟踪过程。为了提高跟踪效率，本文根据检测和预测位置积累误差判决检测模式，误差超过门限值则采取全局检测模式消除积累误差，否 则使用局部检测模式，降低TBD跟踪算法的运算复杂度。仿真实验证明，本文方法可以有效检测跟踪极小目标，提高跟踪的实时处理能力。

Minimal Target TBD Tracking Method for Visible Image Based on Improved Kalman Filter

A tracking before detection(TBD) tracking method for minimal targets tracking in unmanned aerial vehicle (UAV) visible image based on improved Kalman filter is presented. Firstly, detected target obtained by detecting algorithm is used as the measurement value of Kalman filter. Parameters of matching similarity in the detection process is used as an important reference for measurement noise covariance matrix of Kalman filter. Secondly, in the tracking module, tracking framework based on Kalman filter is established to predict the target position in next frame. Finally, targets are searched by detection module in local area accor ding to predictive position. In addition, in order to improve the tracking efficiency, accumulation error between detection position and predictive position is calculated to choose detection mode. Global detection mode is taken if accumulation error is greater than the given threshold and accumulation error is set to be zero, or local detection mode is done. The strategy can effectively reduce computational complexity of the TBD tracking method. Simulation experiment results show that the method can obtain the better performance of detection and tracking than that of classic Kalman filter.