Target Recognition Algorithm For Phased Array Radar Signal

The effects of incorporating a time-varying update time into three target-tracking algorithms based on the a and a -filters are presented and compared. The update times are chosen according to the inverse of either the square root or cube root of the position residual. Simulation results show that the a 13 v -filter yields larger update times during the manoeuvre than those obtained with the a 13 -filter, as the residuals obtained with the former filter are smaller during the manoeuvre. It is found that the best compromise between the number of updates and estimation errors is obtained when the update time is chosen according to the inverse of the square root of the residual when the a -filter is being used, and the inverse of the cube root of the residual when the a -filter is being used. Besides that, a state estimator based on continuos-state Hopfield net applying to phased array tracking is discussed in this paper, we also show how to chose some of the estimator design parameters. Simulation results show that the new approach performs similar to Kalman filter. Due to the parallel computational mode of neural net, the new approach is more attractive for real time implementation, such as radar target tracking, from the computational time point of view, than classical trackers.