Abstract:

The algorithms of radar highresolution range profile (HRRP) are often sensitively to noise, the performance of recognition will descend if the signal to noise ratio of the testing sample is not equal to as the training sample. In practical application, the algorithms should have a robust recognition performance in different noisy conditions. Thus, based on the probabilistic principal component analysis (PPCA) model, a roust algorithm for radar target recognition is proposed. This algorithm makes the parameters of PPCA model alter by different noisy conditions, and the influence of normalization of radar data on the parameters of PPCA model is analyzed to improve the searching efficiency of the algorithm, a fast algorithm is deduced. Experimental results for measured data show that the validity of the proposed method, which is more robust to noise.