Online predicting of line-of-sight angular rate based on LS-SVM method

Firstly, the least squares support vector machine(LS-SVM) algorithm was improved by a time-weighted and last-elimination mechanism. Secondly, the improved LS-SVM algorithm was used for line-of-sight angular rate online prediction when the target was unlocked by the seeker due to the line-of -sight of the seeker which was blocked by the clouds(clouds-crossing) or other interferences. On one hand, if the target was locked by the seeker the improved LS-SVM should be used for online training. On the other hand, if the target was unlocked the decision function(result of the online training) should be used for predicting the line-of-sight angular rate. Lastly, by adding up the miss-distances of the numerical simulations in which the seeker was unlocked in the terminal part of the trajectory, the results demostrated the effectiveness of LS-SVM method to the typical line-of-sight angular rate signal predicting and the application prospects in increasing the capacity of clouds-crossing and anti- interference of the small air to surface tactical missles.