Radar emitter intrapulse signal blind sorting under modified wavelet denoising

Wang, Xuebao; Huang, Gan; Zhou, Zhiwen; Tian, Wei; Yao, Jialun; Gao, Jun

doi:10.1049/joe.2019.0681

Cited by 5 publications

(3 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We have proposed a noise reduction method based on wavelet transform aiming at radar emitter intra-pulse signals in [ 38 ], but only to solve part of the problem. We applied the EC-STFT and FT features to the RDBN for recognition experiments.…”

Section: Simulations and Discussionmentioning

confidence: 99%

Radar Emitter Recognition Based on the Energy Cumulant of Short Time Fourier Transform and Reinforced Deep Belief Network

Wang

Huang

Zhou

et al. 2018

Sensors

Self Cite

View full text Add to dashboard Cite

To cope with the complex electromagnetic environment and varied signal styles, a novel method based on the energy cumulant of short time Fourier transform and reinforced deep belief network is proposed to gain a higher correct recognition rate for radar emitter intra-pulse signals at a low signal-to-noise ratio. The energy cumulant of short time Fourier transform is attained by calculating the accumulations of each frequency sample value with the different time samples. Before this procedure, the time frequency distribution via short time Fourier transform is processed by base noise reduction. The reinforced deep belief network is proposed to employ the input feature vectors for training to achieve the radar emitter recognition and classification. Simulation results manifest that the proposed method is feasible and robust in radar emitter recognition even at a low SNR.

show abstract

Section: Simulations and Discussionmentioning

confidence: 99%

Radar Emitter Recognition Based on the Energy Cumulant of Short Time Fourier Transform and Reinforced Deep Belief Network

Wang

Huang

Zhou

et al. 2018

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…With the continuous progress of radar technology, due to the increasing number of radiation sources, and the frequency range extension, there are higher requirements of radar countermeasures [1]- [3]. To achieve advantages over counterparts, it is necessary to accurately sort the radar signals in a short time [4]- [5].…”

Section: Introductionmentioning

confidence: 99%

Implementation of Radar Signal Sorting Based on K-means Clustering and Cuckoo Search Methods

Deng

Kong

Jiang

et al. 2023

Preprint

View full text Add to dashboard Cite

In this paper, a fusion clustering radar signal sorting algorithm is proposed to overcome the shortcoming of the traditional K-means clustering sorting algorithm, which is sensitive to the initial clustering center and easy to fall into a local optimal solution. The improved algorithm combines K-means clustering algorithm and cuckoo search, to reduce the dependence on the initial clustering center and avoid falling into local optimum. Further, a new definition method of fitness function and a new adjustment mechanism of discovery probability are proposed, and sine and cosine guidance are introduced in the process of location updating. The number of clusters is proposed to be adjusted adaptively according to the distance between classes, and the initial cluster center is determined by the data field. Implementation results show that the sorting accuracy of the new algorithm is 96.6%, which is 54% higher than that of the traditional K-means clustering. Compared with PSO-Kmeans, the accuracy is improved by 22%, and the convergence time is reduced by 10 seconds on average.

show abstract

“…B LIND source separation (BSS) refers to the process of separating and recovering signals by using only the observed signals when the source signals and the transmission channel are unknown [1]. After years of research and development, BSS has been successfully applied in many fields, including military communications [2], image processing [3], speech signal processing [4], and other fields. BSS is defined as underdetermined blind source separation (UBSS) when the number of observed signals is less than the number of source signals [5].…”

Section: Introductionmentioning

confidence: 99%

Mixing Matrix Estimation Algorithm for Underdetermined Blind Source Separation

Bai

Shi³

et al. 2021

IEEE Access

View full text Add to dashboard Cite

An original algorithm for underdetermined mixing matrix estimation is proposed in this paper, which can estimate the mixing matrix effectively when the number of source signals is unknown. Firstly, a new single source point (SSP) detection algorithm based on transformation matrix is proposed, which can effectively detect single source time-frequency (TF) points by using the characteristics of complex ratio and improve the sparsity of source signals. In view of the fact that the number of source signals is unknown, a novel estimation algorithm based on element sorting is proposed, which can significantly improve the estimation accuracy of the number of source signals. Finally, the mixing matrix is estimated by using the cluster center obtained by agglomerative hierarchical clustering (AHC) algorithm. The simulation results show that the proposed algorithm can improve the estimation accuracy of the number of source signals and underdetermined mixing matrix obviously, and the algorithm has higher robustness compared with other algorithms.

show abstract

Radar emitter intrapulse signal blind sorting under modified wavelet denoising

Cited by 5 publications

References 6 publications

Radar Emitter Recognition Based on the Energy Cumulant of Short Time Fourier Transform and Reinforced Deep Belief Network

Radar Emitter Recognition Based on the Energy Cumulant of Short Time Fourier Transform and Reinforced Deep Belief Network

Implementation of Radar Signal Sorting Based on K-means Clustering and Cuckoo Search Methods

Mixing Matrix Estimation Algorithm for Underdetermined Blind Source Separation

Contact Info

Product

Resources

About