Research on Communication Reconnaissance Information Processing and Fusion

2014 ◽  
Vol 552 ◽  
pp. 359-362
Author(s):  
Hong Wei Quan ◽  
Dong Liang Peng
Keyword(s):  
Information Fusion ◽  
Electromagnetic Signal ◽  
Electromagnetic Spectrum ◽  
Simulation Environment ◽  
Characteristic Parameters ◽  
Communication Signals ◽  
Time Frequency ◽  
Blackboard Model ◽  
Processing Data ◽  
Signal Environment

In complex electromagnetic signal environment, the reconnaissance equipments in tactical communication system can uninterruptedly reconnoiter a variety of enemy’s communication signals as well as access a number of characteristic parameters of time, frequency and space domain by searching analysis, feature extraction, direction finding and comprehensive identification. After a series of signal processing, data mining and information fusion, we can get the characteristic parameters of the electromagnetic spectrum of the enemy’s reconnaissance equipments, which provide the basis for analysis and estimation of electromagnetic situation in battlefield. In this paper a multi-hierarchical blackboard model is proposed for multi-sources communication reconnaissance information mining and fusion and the effectiveness of the method is validated in simulation environment.

scholarly journals Time-Frequency Analysis and Application of a Vibration Signal of Tunnel Excavation Blasting Based on CEEMD-MPE-HT

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Chen-yang Ma ◽  
Li Wu ◽  
Miao Sun ◽  
Qing Yuan
Keyword(s):  
Empirical Mode Decomposition ◽  
Frequency Band ◽  
Frequency Analysis ◽  
Frequency Characteristic ◽  
Permutation Entropy ◽  
Natural Vibration Frequency ◽  
Characteristic Parameters ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Mode Decomposition

The traditional empirical mode decomposition method cannot accurately extract the time-frequency characteristic parameters contained in the noisy seismic monitoring signals. In this paper, the time-frequency analysis model of CEEMD-MPE-HT is established by introducing the multiscale permutation entropy (MPE), combining with the optimized empirical mode decomposition (CEEMD) and Hilbert transform (HT). The accuracy of the model is verified by the simulation signal mixed with noise. Based on the project of Loushan two-to-four in situ expansion tunnel, a CEEMD-MPE-HT model is used to extract and analyze the time-frequency characteristic parameters of blasting seismic signals. The results show that the energy of the seismic wave signal is mainly concentrated in the frequency band above 100 Hz, while the natural vibration frequency of the adjacent existing tunnel is far less than this frequency band, and the excavation blasting of the tunnel will not cause the resonance of the adjacent existing tunnel.

Fusion-Net: Time-Frequency Information Fusion Y-Network for Speech Enhancement

2021 ◽  
Author(s):  
Santhan Kumar Reddy Nareddula ◽  
Subrahmanyam Gorthi ◽  
Rama Krishna Sai S. Gorthi
Keyword(s):  
Speech Enhancement ◽  
Information Fusion ◽  
Time Frequency ◽  
Frequency Information

Experimental Investigation of the Electromagnetic Signal Attenuation Characteristics of the Partial Discharge Laboratory at Jadavpur University

2008 ◽  
Vol 9 (2) ◽  
Author(s):  
Biswendu Chatterjee ◽  
Debangshu Dey ◽  
Sivaji Chakravorti ◽  
Chinmoy Kanti Roy
Keyword(s):  
Magnetic Material ◽  
Electromagnetic Interference ◽  
Electromagnetic Waves ◽  
Real Life ◽  
Electromagnetic Signal ◽  
Shielding Effectiveness ◽  
Signal Attenuation ◽  
Conducting Material ◽  
Communication Signals ◽  
Attenuation Characteristics

Electromagnetic interference is becoming an increasing concern, because of the high intensity of surrounding electromagnetic waves, mainly arising from communication signals and also due to widespread use of equipment that operates at radio frequencies. As a consequence, sensitive data acquisition equipment suffers from erroneous results. Operating such instruments in a suitable shielded environment can significantly reduce this electromagnetic interference. But to achieve good shielding in practice, construction-related problems are to be faced, especially in large spaces, where a single metal plate cannot cover the whole area. Unless special care is taken, electromagnetic waves can penetrate through the gap in the joints and defects like drill holes reducing the shielding effectiveness. Also, a single layer of shielding is not always effective as the quality deteriorates drastically even due to minor constructional defects as mentioned above. This paper describes real-life experiences, step-by-step, in the shielding of a spacious insulation diagnostic laboratory (with a target of at least 55 dB signal attenuation), firstly using a good conducting material, using two different methods for joining the sheets, and ultimately constructing a second layer of shielding using a magnetic material. To study the attenuation behavior of the laboratory with respect to electromagnetic waves, a device for the relative measurement of surrounding electromagnetic signal strength is developed. The signal levels are measured initially outside and then at different places inside the shielded laboratory. The results presented in this paper show (1) the variation of attenuation characteristics inside the shielded laboratory due to different methods adopted for joining the shielding sheets using a good conducting material, (2) the effect of a second shielding layer in the form of a box that was constructed using a magnetic material and placed inside the laboratory and (3) the improvement in attenuation behavior after the actual construction of the second layer of shielding using a magnetic material.

Patterns of evolution in human speech processing and animal communication

1998 ◽  
Vol 21 (2) ◽  
pp. 282-283
Author(s):  
Michael J. Ryan ◽  
Nicole M. Kime ◽  
Gil G. Rosenthal
Keyword(s):  
Sexual Selection ◽  
Speech Processing ◽  
Communication Systems ◽  
Animal Communication ◽  
Low Noise ◽  
Communication Signals ◽  
Human Speech ◽  
Strong Selection ◽  
Processing Data ◽  
Receiver System

We consider Sussman et al.'s suggestion that auditory biases for processing low-noise relationships among pairs of acoustic variables is a preadaptation for human speech processing. Data from other animal communication systems, especially those involving sexual selection, also suggest that neural biases in the receiver system can generate strong selection on the form of communication signals.

MULTIFRACTAL OF ELECTROMAGNETIC WAVEFORM AND SPECTRUM ABOUT COAL ROCK SAMPLES SUBJECTED TO UNIAXIAL COMPRESSION

Fractals ◽  
2020 ◽  
Vol 28 (04) ◽  
pp. 2050061 ◽  
Author(s):  
LIMING QIU ◽  
DAZHAO SONG ◽  
XUEQIU HE ◽  
ENYUAN WANG ◽  
ZHENLEI LI ◽  
...  
Keyword(s):  
Electromagnetic Wave ◽  
Failure Process ◽  
Dominant Frequency ◽  
Rock Failure ◽  
Electromagnetic Signal ◽  
Failure Behavior ◽  
Electromagnetic Spectrum ◽  
Dimension Formula ◽  
Coal Rock ◽  
The Difference

During coal and rock loading, a significantly large number of electromagnetic signals are generated as a result of fracture appearance and crack expansion. The generation of electromagnetic signal is the comprehensive embodiment of the coal rock failure behavior. Therefore, the generated signals contain complex and rich messages that can reflect the damage process and degree of coal and rock. In this work, the multifractal theory is applied to analyze the nonlinear characteristics of the electromagnetic wave and its spectrum induced during coal rock, which present good correlation with failure process. The failure process of coal rock is non-uniform, non-continuous and nonlinear, during which, there is a good synchronization and correlation between the electromagnetic pulses and the stress drop, rather than the stress. Both waveform and its spectrum of electromagnetic signal have multifractal characteristics, the larger the fracture scale is, the more significant the multifractal characteristic of electromagnetic signal is, and the multifractal characteristic of electromagnetic signal from coal is higher than that from sandstone. The difference of fracture energy and size can be represented by the maximum of the multifractal dimension [Formula: see text] of the electromagnetic wave and its spectrum during coal rock failure. In the electromagnetic spectrum, small signals are always dominant, and the dominant frequency is only a few isolated points. What is more, with the increase of fracture size, the difference between the dominant frequency and the non-dominant frequency is gradually enhanced.

The Automatic Gain Control of the Digital Reconnaissance Receiver

2015 ◽  
Vol 713-715 ◽  
pp. 820-824
Author(s):  
Hai Qing Jiang ◽  
Guang Liang Zou ◽  
Ying Liu
Keyword(s):  
Automatic Gain Control ◽  
Design Method ◽  
Gain Control ◽  
Electromagnetic Signal ◽  
Engineering Practice ◽  
Signal Environment ◽  
Digital Processor

On the basis of a brief introduction of how the automatic gain control (AGC) of the digital reconnaissance receiver works,this article proposed a digital AGC design method based on the digital processor parts, which provide a guarantee for the digital reconnaissance receiver to work in the complex external electromagnetic signal environment. The engineering implementation of AGC is introduced with specific projects. Both simulation and engineering practice verify the feasibility of this program.

Characteristic Parameters of Surface Electromyography Signals of Cervical Muscles

2012 ◽  
Vol 249-250 ◽  
pp. 1308-1312
Author(s):  
Lin Wang ◽  
Hong Wang ◽  
Rong Rong Fu ◽  
Ning Ning Zhang
Keyword(s):  
Surface Electromyography ◽  
Characteristic Parameters ◽  
Time Frequency ◽  
Cervical Muscles ◽  
Upper Trapezius ◽  
Resistance Moment ◽  
Long Time ◽  
Resistant Moment ◽  
Electromyography Signals ◽  
Semg Signals

Surface electromyography (SEMG) signals of cervical muscles are investigated by time-frequency analysis and biomechanics analysis. Medium frequency (MF) and integrated electromyography (IEMG) are extracted and analyzed from SEMG signals of subjects’ upper trapezius. The Experimental results show that the value of MF decreases and the value of IEMG increases with the increase of fatigue of the vertical muscles. Also, the values of IEMG at different testing points of same cervical muscle are compared. The value of IEMG with higher resistant moment is higher than that with lower resistant moment. That means the muscle with high resistance moment is easier to be fatigue. This investigation is important for people, especially those who work/read with bowing head or before computer for a long time, to prevent cervical spondylosis.

scholarly journals Multi-task Learning Approach for Automatic Modulation and Wireless Signal Classification

2021 ◽  
Author(s):  
Anu Jagannath ◽  
Jithin Jagannath
Keyword(s):  
State Of The Art ◽  
Spectrum Management ◽  
Signal Classification ◽  
Electromagnetic Spectrum ◽  
Signal Recognition ◽  
Single Task ◽  
Communication Signals ◽  
Spectrum Monitoring ◽  
Task Learning ◽  
Wireless Signal

Wireless signal recognition is becoming increasingly more significant for spectrum monitoring, spectrum management, and secure communications. Consequently, it will become a key enabler with the emerging fifth-generation (5G) and beyond 5G communications, Internet of Things networks, among others. State-of-the-art studies in wireless signal recognition have only focused on a single task which in many cases is insufficient information for a system to act on. In this work, for the first time in the wireless communication domain, we exploit the potential of deep neural networks in conjunction with multi-task learning (MTL) framework to simultaneously learn modulation and signal classification tasks. The proposed MTL architecture benefits from the mutual relation between the two tasks in improving the classification accuracy as well as the learning efficiency with a lightweight neural network model. Additionally, we consider the problem of heterogeneous wireless signals such as radar and communication signals in the electromagnetic spectrum. Accordingly, we have shown how the proposed MTL model outperforms several state-of-the-art single-task learning classifiers while maintaining a lighter architecture and performing two signal characterization tasks simultaneously. Finally, we also release the only known open heterogeneous wireless signals dataset that comprises of radar and communication signals with multiple labels.

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