A new method of feature splicing based on wavelet transform for recognition of HRRP with noise

Abstract

In meta-learning based small-sample HRRP recognition, HRRP data is one-dimensional, and the amount of extractable features is not as much as that of multidimensional data, so it is necessary to splice the one-dimensional data into twodimensional data to improve the recognition rate. This paper strives to reconceptualize the features among HRRP data from a two-dimensional perspective, and proposes a low noise sensitivity feature extraction based on wavelet decomposition and a low-frequency wavelet coefficient splicing method in descending order by scale to make it more applicable to the recognition of small sample targets containing noisy data. The HRRP with noise was decomposed by wavelet packet, and the lowest frequency wavelet coefficient with low noise sensitivity was extracted by wavelet packet sub-band energy and cosine similarity, and then spliced in descending order of scale, combined with the original data to form two-dimensional data, and trained with neural networks. The experiments show that the proposed method has obvious advantages in recognition accuracy, dependence on the number of samples and feature extraction ability.