Extended-target detection and tracking methods for the IRST

Abstract

The signal processing unit of the IRST detects and tracks approaching enemy missiles. The target detection can be carried out with a statistical matched filter. The basic matched filter assumed the signal model as a 2D Gaussian shape. However, the homing missile takes the lead-angle trajectory and the target shape becomes an ellipse. In such a case, the matched filter tuned tuned to the Gaussian shape fails detection or results high false alarm rate. To overcome this difficulty, we proposed new target detection algorithm using attitude information of the target. We estimate the attitude of the target using an extended Kalman filter from 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. The simulation result shows that the proposed algorithm reduces the false alarm rate. For the target tracking, we proposed a new method of target tracking using the Viterbi algorithm. By defining the velocity vector as a state, we can define the transition probability of the state and apply the Viterbi algorithm to estimate the state. We observe that the proposed method track the target trajectory as well.