A deep learning based iterative denoising algorithm for multiple frequency lines recovery

Abstract

Passive detection technology constitutes a crucial research direction in underwater acoustic target detection. It has been the subject of ongoing investigations to address the pressing need for stealth capabilities. The most formidable hurdle that all types of detectors must overcome is the extraction of line spectral components relevant to the target, given the convoluted underwater environment teeming with significant noise pollution. In this paper, a pioneering deep learning-based algorithm, known as the Additive Diffusion Probabilistic Denoising Model (ADPDM), is proposed to rectify the performance inadequacies of neural network-based approaches when operating under low signal-to-noise ratios (SNRs). To begin with, the ADPDM was ingeniously crafted. It was designed to astutely modify the representation of underwater signals by transforming the generative inference process of the diffusion model into a deterministic recovery strategy. Subsequently, the ADPDM was expanded into the complex-valued time–frequency joint domain, in order to take full advantage of the multi-dimensional information representation brought about by the lofargram. Moreover, an accelerating inference algorithm was adopted and calibrated to be fully compatible with the ADPDM framework. In contrast to the prevailing frequency line trackers that predominantly concentrate on discerning the frequency positions of the line spectrum, the ADPDM is dedicated to unearthing and reconstructing the latent line spectrum components concealed within the observed signal. This, in turn, paves the way for more effective subsequent detection or estimation operations. Empirical results demonstrated that the frequency lines within the signal enhanced by the ADPDM can be detected with remarkable efficacy, even when a relatively less sophisticated tracker is employed. On the basis of these findings, the detection performance metrics of the ADPDM have been shown to outstrip those of the current state-of-the-art (SOTA) methods, both those founded on deep learning and the hidden Markov model (HMM), across the entire spectrum of experimental SNRs.