Multiple radar subbands fusion technique based on generalized likelihood ratio test

Ghasemi, Mahdi; Pishrow, Mohammad Mehdi

doi:10.1016/j.phycom.2021.101331

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…As far as teachers' awareness is concerned, under the current situation that the functions of translation software are constantly upgraded and updated, many teachers do not understand the advantages and disadvantages of machine translation timely and fully. ey can neither make use of its advantages in teaching nor face the challenges it brings, so as to adjust the teaching content accordingly [5]. As far as the content of teaching materials is concerned, although the quantity and quality of business English translation teaching materials on the market have improved in recent ten years, due to the limited factors such as long publishing cycle, limited energy of teachers, difficulty in collecting highquality translation materials, rapid development of business society, the content of teaching materials cannot be updated in time, and the listed cases are relatively outdated.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Recognition Model of Business English Translation Based on Improved GLR Algorithm

Deng¹,

Hu²,

Liu³

2022

Computational Intelligence and Neuroscience

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Aiming at the problem of low accuracy of traditional algorithm model, an intelligent recognition model of business English translation based on an improved GLR algorithm is proposed. Through this algorithm, the automatic sentence recognition technology is established, and according to the characteristics of business English, the improved GLR algorithm is used for collection, sorting, and analysis, so as to realize the intelligent recognition of business English. The results show that based on the improved GLR algorithm, the recognition accuracy is high, and the comprehensive score is 92.5 points, which overcomes the disadvantages of the GLR algorithm, and the operation speed and processing are improved. Based on the improved GLR algorithm, the intelligent translation of business English is realized, which is accurate and fast, and greatly promotes the learning and development of business English.

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Section: Introductionmentioning

confidence: 99%

Intelligent Recognition Model of Business English Translation Based on Improved GLR Algorithm

Deng¹,

Hu²,

Liu³

2022

Computational Intelligence and Neuroscience

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show abstract

“…This algorithm introduced a penalty function to suppress different types of noise based on the Probability Density Functions of noise. Mahdi [14] proposed a multi‐stage approach based on the Generalised Likelihood Ratio, or GLR, test for estimating the target range profile using sparse subband measurement data.…”

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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Multiple radar subbands fusion technique based on generalized likelihood ratio test

Cited by 2 publications

References 16 publications

Intelligent Recognition Model of Business English Translation Based on Improved GLR Algorithm

Intelligent Recognition Model of Business English Translation Based on Improved GLR Algorithm

Multi‐subband fusion algorithm based on autoencoder

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