Block-Division Based Ultra-Wideband Fusion of Multiple Radar Bands for Resolution Enhanced Imagery

Yuan, Songmei; Chen, Chengzeng; Xu, Xiaojian

doi:10.1109/icspcs.2018.8631786

Cited by 2 publications

(1 citation statement)

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“…Upon large angular phase difference, the cross‐range window phenomenon occurs, which causes estimation errors. Yuan Saisai [11] proposed a block‐division‐based data fusion technique, which can be used for multiple radar band fusion over ultrawide bandwidth and image resolution enhancement for complex targets.…”

Section: Introductionmentioning

confidence: 99%

Multi‐subband fusion algorithm based on autoencoder

Jiang

Zhang

Li³

et al. 2022

IET Signal Processing

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A novel algorithm for multi-subband signal fusion achieves performance superior to traditional all-pole model, matrix pencil algorithm and deep-neural-network (Deep neural network (DNN)). The method uses a deep-learning autoencoder more fully described as a multi-subband fusion autoencoder (MSFAE). This autoencoder comprises two parts: a multi-subband encoder and a full-band decoder. Full-band echo distance envelopes are used as training data for the full-band autoencoder, to obtain the full-band coding and the full-band decoder. Then, the multi-subband echo distance envelopes are used as training data, and the full-band coding is used as labels, to train the multi-subband encoder. Finally, the multi-subband encoder and the full-band decoder are combined to obtain the MSFAE. The multi-subband distance envelopes are input to the MSFAE to obtain the full-band distance envelopes, improving the radar distance resolution and obtaining highresolution range profiles. In contrast with the traditional all-pole model and matrix pencil algorithm, the authors' MSFAE directly processes the information in the frequency domain, avoiding the error of pole estimation in the echo domain. In contrast with DNN, the authors' MSFAE needs only multi-subband distance envelopes as input, avoiding noise subband redundancy. The experimental results show that the fusion accuracy of MSFAE is higher than the traditional all-pole model, matrix pencil algorithm and DNN. The MSFAE has superior performance using the fusion method, even at low signal-to-noise ratio.

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Section: Introductionmentioning

confidence: 99%