Path Optimization of Target Detection Method Based on Deep Learning Feature Fusion

Zhang, Yanbo

doi:10.1007/978-3-030-70042-3_126

Cited by 1 publication

(1 citation statement)

References 4 publications

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“…[31,32] also applied the attention mechanism and image semantic segmentation algorithms to airborne target recognition and maritime SAR imaging recognition, both of which achieved good results on measured data. The U-Net framework also has a wide range of applications in other scenarios of radar, such as multistation cooperative radar target recognition [33], marine target detection [34], satellite-borne SAR images ship detection [35], urban building imaging [36], and so on [37,38].…”

Section: Introductionmentioning

confidence: 99%

RAU-Net-Based Imaging Method for Spatial-Variant Correction and Denoising in Multiple-Input Multiple-Output Radar

Ren,

Luo,

Fan

et al. 2023

Remote Sensing

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The conventional back projection (BP) algorithm is an accurate time-domain algorithm widely used for multiple-input multiple-output (MIMO) radar imaging, owing to its independence of antenna array configuration. The time-delay curve correction back projection (TCC-BP) algorithm greatly reduces the computational complexity of BP but suffers from spatial-variant correction, sidelobe interference and background noise due to the use of coherent superposition of echo time-delay curves. In this article, a residual attention U-Net-based (RAU-Net) MIMO radar imaging method that adapts complex noisy scenarios with spatial variation and sidelobe interference is proposed. On the basis of the U-Net underlying structure, we develop the RAU-Net with two modules: a residual unit with identity mapping and a dual attention module to obtain resolution spatial-variant correction and denoising on real-world MIMO radar images. The network realizes MIMO radar imaging based on the TCC-BP algorithm and substantially reduces the total computational time of the BP algorithm on the basis of improving the imaging resolution and denoising capability. Extensive experiments on the simulated and measured data demonstrate that the proposed method outperforms both the traditional methods and learning-imaging methods in terms of spatial-variant correction, denoising and computational complexity.

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