Compound weighted fusion evaluation and optimization of intelligent tracking algorithm in radar seeker

This paper designs a hierarchical weighted fusion evaluation/optimization scheme for the radar seeker neural network (NN) tracking algorithm. The first weighted fusion of closed-loop performance index is carried out to exclude the hardware influence on algorithm evaluation. Then, according to different tracking scenarios, the tracking index is divided into different periods; a single period score is given by a linear-nonlinear hybrid scoring mechanism. Furthermore, in a single index, the internal scores of different time periods are weighted and fused for the second time to obtain the index overall score. Finally, the third weighted fusion of the multi-index scores obtains the comprehensive score of the algorithm. We design the parameter evaluation case sets and repeat the aforementioned compound weighting; hence the case with the highest comprehensive score is obtained. Finally, the algorithm is optimized by the highest-score case. The experiment using fuzzy NN radar seeker verifies the effectiveness of the method.