High resolution methods for electronic counter measures environments establishing and side lobe cancellation in cognitive radar

For high resolution methods for electronic counter measures environments establishing and side lobe cancellation in cognitive radar problems, dynamic modeling methods based on neural net were proposed to simulate the complicated electronic counter measures environments for cognitive radar, and the method based on self-adaptive neural net from cognitive computer of cognitive radar was also proposed to fix on the needed weights of amplitude or phase by means of the direction and intensity of jam resource. Dynamic modeling methods based on neural net was effective to solve some nonlinear mapping in traditional modeling question, to denote dynamic characteristic of electronic counter measures, to deal with multi-input and multi-output variants included by fix quantitative analysis, qualitative analysis. The method for side lobe cancellation in cognitive radar based on self-adaptive neural net solved the weight choosing problems of dynamic variety, adaptability, optimum, comparing with traditional weight choosing method such as MSE. Further, calculating time could satisfy the demand of cognitive radar operating real time. Simulation results showed that the resolved methods had superior performance on the accuracy and robust of electronic counter measures environments establishing and side lobe cancellation in cognitive radar.