Identification and micro-motion parameter estimation of non-cooperative UAV targets

Yang, Jiachen; Zhang, Zhuo; Mao, Wei; Yue, Yang

doi:10.1016/j.phycom.2021.101314

Cited by 12 publications

(5 citation statements)

References 33 publications

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“…Among them, short-term continuity is more effective in detecting high-pitched music signals. Related scholars have proposed a semiblind separation of speech and music based on sparsity and continuity; they used sparsity and continuity constraints to optimize dictionary coefficients, used the dictionary to represent the power spectral density of each source signal, and mixed them through a nonlinear function [24][25][26][27][28][29][30][31][32]. e power spectrum of the signal is mapped to the dictionary space, and finally, the source signal is reconstructed using an adaptive Wiener filter and spectral subtraction.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Research on Segmentation Experience of Music Signal Improved Based on Maximization of Negative Entropy

Qin

2021

Complexity

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With the rapid growth of digital music today, due to the complexity of the music itself, the ambiguity of the definition of music category, and the limited understanding of the characteristics of human auditory perception, the research on topics related to automatic segmentation of music is still in its infancy, while automatic music is still in its infancy. Segmentation is a prerequisite for fast and effective retrieval of music resources, and its potential application needs are huge. Therefore, topics related to automatic music segmentation have important research value. This paper studies an improved algorithm based on negative entropy maximization for well-posed speech and music separation. Aiming at the problem that the separation performance of the negative entropy maximization method depends on the selection of the initial matrix, the Newton downhill method is used instead of the Newton iteration method as the optimization algorithm to find the optimal matrix. By changing the descending factor, the objective function shows a downward trend, and the dependence of the algorithm on the initial value is reduced. The simulation experimental results show that the algorithm can separate the source signal well under different initial values. The average iteration time of the improved algorithm is reduced by 26.2%, the number of iterations is reduced by 69.4%, and the iteration time and the number of iterations are both small. Fluctuations within the range better solve the problem of sensitivity to the initial value. Experiments have proved that the new objective function can significantly improve the separation performance of neural networks. Compared with the existing music separation methods, the method in this paper shows excellent performance in both accompaniment and singing in separated music.

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Section: Related Workmentioning

confidence: 99%

[Retracted] Research on Segmentation Experience of Music Signal Improved Based on Maximization of Negative Entropy

Qin

2021

Complexity

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“…According to the analysis in [150], there are significant differences in RCS characteristics between aircraft targets of various sizes, materials, and shapes, and therefore these characteristics can be applied to RATR. Considering the large variance of the RCS of different targets and the effect of micro-motion factors on RCS time series, Yang et al [151] analyzed the statistical RCS characteristics and adopted multilayer CNNs and RNNs to classify targets based on RCS time series, respectively, confirming that deep learning methods have the capability of target recognition based on RCS series, especially with micro-motion difference. The depth of the model has a vital effect on the accuracy of recognition, and RNN models are slightly inferior to CNN models.…”

Section: Deep Learning For Other Radar-target-characteristic-based Ratrmentioning

confidence: 99%

Radar Target Characterization and Deep Learning in Radar Automatic Target Recognition: A Review

Jiang

Wang

et al. 2023

Remote Sensing

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Radar automatic target recognition (RATR) technology is fundamental but complicated system engineering that combines sensor, target, environment, and signal processing technology, etc. It plays a significant role in improving the level and capabilities of military and civilian automation. Although RATR has been successfully applied in some aspects, the complete theoretical system has not been established. At present, deep learning algorithms have received a lot of attention and have emerged as potential and feasible solutions in RATR. This paper mainly reviews related articles published between 2010 and 2022, which corresponds to the period when deep learning methods were introduced into RATR research. In this paper, the current research status of radar target characteristics is summarized, including motion, micro-motion, one-dimensional, and two-dimensional characteristics, etc. This paper reviews the progress of deep learning methods in the feature extraction and recognition of radar target characteristics in recent years, including space, air, ground, sea-surface targets, etc. Due to more and more attention and research results published in the past few years, it is hoped that this review can provide potential guidance for future research and application of deep learning in fields related to RATR.

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“…As the categories of construction projects are complicated, the corresponding management processes are also relatively complex [24][25][26]. Therefore, the important work of the collaborative office management platform is to set up customized and diversified process management templates according to the parties involved in the project and the characteristics of the approval process and then compile practical and comprehensive approval flow templates covering the approval flow of each participant such as construction, supervision, and owner.…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

Optimization and Simulation of Labor Resource Management Information Platform Based on Internet of Things

Liang

Cao

Liu

2021

Wireless Communications and Mobile Computing

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This paper conducts an in-depth analysis and research on the optimization of the labor resource management information platform through the Internet of Things (IoT) technology; through the collection, classification, and data search functions of this application system, it meets the supply and demand of professional talents within a certain enterprise. At the same time, it also realizes the curriculum training application on improving the skills and literacy of the employees of a certain enterprise, and it can learn the enterprise curriculum training from the comments of the employees on the enterprise curriculum. The effect of the enterprise course training can be learned from the comments of the employees on the enterprise course, providing an important reference basis for the future revision of the enterprise course training content. The performance of the participants in the training also has objective data for reference, so that the situation will not be disconnected from reality, and the interaction between enterprise management and employees can achieve a balanced effect. The goal of this workforce resource management system is to create a systematic workforce resource management platform for professional talents and help enterprises achieve the goal of speeding up and increasing efficiency. The system interface provided by the third party is used for horizontal data expansion to realize the sharing of basic information or video data as well as system expansion to realize real-time monitoring and management of project works. The cloud platform realizes efficient management and scientific application of construction site projects by construction management departments, which better solves the current problem of lack of supervision at construction sites.

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Identification and micro-motion parameter estimation of non-cooperative UAV targets

Cited by 12 publications

References 33 publications

[Retracted] Research on Segmentation Experience of Music Signal Improved Based on Maximization of Negative Entropy

[Retracted] Research on Segmentation Experience of Music Signal Improved Based on Maximization of Negative Entropy

Radar Target Characterization and Deep Learning in Radar Automatic Target Recognition: A Review

Optimization and Simulation of Labor Resource Management Information Platform Based on Internet of Things

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