A New Radar Detection Effectiveness Estimation Method Based on Deep Learning

Some of traditional analytical processes with non-linear characteristics are difficult to manage. For example, many issues of radar detection effectiveness estimation relying on human experience are hardly to suggest by using the traditional analytical methods. Therefore, some explored researches aimed at this problem are carried out. The main purpose of the paper is some reasonable ways are tried to designed to replace the analytical process, so as to complete the radar detection effectiveness evaluation. As well known, Deep Learning which is the typical models of deep neural network has a very good capability for expressing non-linear contents. Hence, it could be brought into studying the issue of the radar detection effectiveness evaluation, and this new idea and relative new method are proposed in the paper. Furthermore, the CNN as one of the typical network models or algorithms of the Deep Learning would be employed to execute relative researches. In the proposed method, the input sample data set which CNN needs can be constructed through designing the spatial distribution images composed of the radar radiation domain and the target location. And the labels for present or absent missing alarm can be obtained according to some rules. Then, the CNN model with five hidden layers is established to complete the non-linear mapping from input sample set to output labels, in order to achieve the estimated results. Simulation results prove the validity of the proposed method.