Radar Jamming Effect Analysis Based on Bayesian Inference Network With Adaptive Clustering

Abstract

Radar jamming effect analysis (RJEA) is important for improving the performance of radar sensors in jamming environments and one of the most popular methods for RJEA is Bayesian inference network (BIN). Notice the features in RJEA are usually continuous, so they should be discretized for BIN, where the feature discretization approach determines the node sizes and the discretization error, which further seriously affect the training time and inference precision of BIN, respectively. In this paper, we put forward a adaptive clustering approach for the features in RJEA to reduce the node sizes of BIN in the premise of keeping or even improving its inference precision. To do so, a modified K-means algorithm is designed, where the initial clustering centers are preset according to the density of samples, and the number of final clustering centers, not given in advance, is estimated with their locations simultaneously via iterative optimization. Finally, the performance of BIN with the adaptive clustering is validated in the experiments on pulse Doppler radar sensor with radial frequency noise, where 20 features are extracted for RJEA. Compared with uniform discretization, the proposed method can reduce the training time of BIN from 19.52 second to 4.39 second and improve its inference precision from 71 % to 82% simultaneously. Compared with K-means discretization, the proposed method results in similar inference precision but less training time of BIN.