Extended target detection and tracking using the simultaneous attitude estimation

We propose a new extended-target detection algorithm which can adapt to the time-varying target shapes. We estimate the attitude of the target using an extended Kalman filter with a sequence of image frames. The estimated target attitude is then used to predict the projected shape of the target image. Using the predicted target shape, we can construct a better-tuned matched filter for the detection of the target in the next image frame. The proposed algorithm has been tested with synthetic IR image frames, and we observe that the false alarm rate has been reduced by the order of magnitude in comparison with the simple matched filtering method with the Gaussian-shaped target assumption