Enhanced Recognition of Naval Ship HRRP Targets Using Improved Adaptive Threshold Wavelet Denoising

Abstract

To address the challenges of noise interference and low signal-to-noise ratio (SNR) in measured one-dimensional ship range profile data, which significantly affect target recognition, a new method is proposed. An improved adaptive threshold wavelet denoising (IATWD) method is introduced. Initially, the two critical parameters of wavelet denoising (WD)—namely, the threshold and threshold functions (TFs)—are optimized. For threshold optimization, a formula related to the number of decomposition levels, the noise standard deviations per level, and the signal length is developed. As decomposition levels change, an optimal threshold can be adaptively determined for each level. Regarding threshold function (TF) improvement, an enhanced TF is designed that flexibly adjusts based on the benefits of both soft and hard TFs. Subsequently, by analyzing the interactions between the variable factors, wavelet base functions, and decomposition levels, optimal parameters for this denoising method are selected. Finally, the efficacy of the denoising and its impact on recognition were validated using denoising evaluation metrics and a Support Vector Machine (SVM) for both simulated and empirical data. Experimental results with both data types demonstrate that the IATWD method significantly outperforms both traditional WD and comparative improved methods in terms of denoising effectiveness and recognition rates.