scholarly journals A GTCC-Based Underwater HMM Target Classifier with Fading Channel Compensation

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Shameer K. Mohammed ◽  
Supriya M. Hariharan ◽  
Suraj Kamal
Keyword(s):  
Fading Channel ◽  
Rayleigh Fading ◽  
Automatic Target Recognition ◽  
Operating Conditions ◽  
Real Field ◽  
Classifier Performance ◽  
Acoustic Target ◽  
Artificial Neural Network Ann ◽  
Underwater Target ◽  
Selection Of

Underwater acoustic target classifiers are found to have many applications in military and security areas where a higher degree of prediction accuracy is needed that makes classifier efficiency and reliability an interesting subject. Classifiers are often trained with known acoustic target specimens with their characteristic feature set and tested with measurements obtained from the sonar that is deployed in the surveillance or observation zone. The selection of source-specific deterministic features in automatic target recognition (ATR) system is very significant, since it determines the reliability, efficiency, and success rate of the classifier. The robustness of the gammatone cepstral coefficients (GTCC) in combination with the statistical Euclidean distance, artificial neural network (ANN), and hidden Markov model (HMM) classifiers has been investigated, and its performance is compared with that of other feature extraction schemes. The classifier performance has been analyzed in Rayleigh fading conditions, based on which the performance is enhanced by incorporating an autoregressive (AR) Rayleigh fading channel compensation. The performance of the classifier in different operating conditions is investigated, with underwater target signals consisting of the real field data collected during expedition, and the results are presented in this paper.

AN EFFICIENT HMM UNDERWATER SIGNAL CLASSIFIER WITH ENHANCED FADING CHANNEL PERFORMANCE

2014 ◽  
Vol 23 (09) ◽  
pp. 1450121 ◽  
Author(s):  
T. BINESH ◽  
M. H. SUPRIYA ◽  
P. R. SASEENDRAN PILLAI
Keyword(s):  
Fading Channel ◽  
Rayleigh Fading ◽  
Markov Models ◽  
Operating Conditions ◽  
Signal Classification ◽  
Success Rates ◽  
Noise Sources ◽  
Feature Based ◽  
Actual Field ◽  
And Performance

Underwater signal classification has been an area of considerable importance due to its applications in multidimensional fields. The selection of the source specific features in a classifier is very significant, as it determines the efficiency and performance of the classifier. Discrete sine transform (DST)-based features possesses the essential traits suitable for the design of statistical models in underwater signal classifiers. These when incorporated in hidden markov models (HMMs), can provide an effective architecture which can be utilized in the classification of underwater noise sources. The design and performance analysis of a 12-state HMM-based classifier for underwater signals in Rayleigh fading channel conditions are presented in this paper. The HMMs utilizing the DST features are found to perform efficiently in underwater signal classification scenario, compared to existing cepstral feature-based classifiers. The fading channel estimation has been carried out and the classifier performance has been improved by providing Rayleigh fading compensation. The success rates of the classifier has been estimated under different operating conditions. The system performance has been analyzed in MATLABTM platform for the class of underwater signals, which include actual field collected data and the results have been presented in this paper.

SoS models for Rayleigh fading channel

2014 ◽  
Author(s):  
Y.H. Ding ◽  
R.J. Tian ◽  
J.S. Tao ◽  
X.R. Ma ◽  
X.C. Wang ◽  
...  
Keyword(s):  
Fading Channel ◽  
Rayleigh Fading ◽  
Rayleigh Fading Channel

Combining Modulation Diversity and Index Assignment to Improve Image VQ for a Rayleigh Fading Channel

2016 ◽  
Author(s):  
Waslon Terllizzie A. Lopes ◽  
Francisco Madeiro ◽  
Benedito G. Aguiar Neto ◽  
Marcelo S. Alencar
Keyword(s):  
Fading Channel ◽  
Rayleigh Fading ◽  
Rayleigh Fading Channel ◽  
Index Assignment ◽  
Modulation Diversity

Dual FH/MFSK system over a Rayleigh fading channel

1996 ◽  
Vol 32 (20) ◽  
pp. 1852 ◽  
Author(s):  
Chanbun Park ◽  
Jae Hong Lee
Keyword(s):  
Fading Channel ◽  
Rayleigh Fading ◽  
Rayleigh Fading Channel

scholarly journals Intelligent long-term performance analysis in power electronics systems

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Saeed Peyghami ◽  
Tomislav Dragicevic ◽  
Frede Blaabjerg
Keyword(s):  
Performance Indicator ◽  
Thermal Characteristics ◽  
Operating Conditions ◽  
System Level ◽  
Power Electronic ◽  
Reliability Modeling ◽  
Artificial Neural Network Ann ◽  
Long Term Performance ◽  
Term Performance

AbstractThis paper proposes a long-term performance indicator for power electronic converters based on their reliability. The converter reliability is represented by the proposed constant lifetime curves, which have been developed using Artificial Neural Network (ANN) under different operating conditions. Unlike the state-of-the-art theoretical reliability modeling approaches, which employ detailed electro-thermal characteristics and lifetime models of converter components, the proposed method provides a nonparametric surrogate model of the converter based on limited non-linear data from theoretical reliability analysis. The proposed approach can quickly predict the converter lifetime under given operating conditions without a further need for extended, time-consuming electro-thermal analysis. Moreover, the proposed lifetime curves can present the long-term performance of converters facilitating optimal system-level design for reliability, reliable operation and maintenance planning in power electronic systems. Numerical case studies evaluate the effectiveness of the proposed reliability modeling approach.

scholarly journals Underwater Acoustic Target Recognition Based on Depthwise Separable Convolution Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1429
Author(s):  
Gang Hu ◽  
Kejun Wang ◽  
Liangliang Liu
Keyword(s):  
Feature Extraction ◽  
Target Recognition ◽  
Target Feature ◽  
Underwater Acoustic ◽  
Radiated Noise ◽  
Automatic Feature Extraction ◽  
Acoustic Target ◽  
And Cluster Analysis ◽  
Underwater Target ◽  
The One

Facing the complex marine environment, it is extremely challenging to conduct underwater acoustic target feature extraction and recognition using ship-radiated noise. In this paper, firstly, taking the one-dimensional time-domain raw signal of the ship as the input of the model, a new deep neural network model for underwater target recognition is proposed. Depthwise separable convolution and time-dilated convolution are used for passive underwater acoustic target recognition for the first time. The proposed model realizes automatic feature extraction from the raw data of ship radiated noise and temporal attention in the process of underwater target recognition. Secondly, the measured data are used to evaluate the model, and cluster analysis and visualization analysis are performed based on the features extracted from the model. The results show that the features extracted from the model have good characteristics of intra-class aggregation and inter-class separation. Furthermore, the cross-folding model is used to verify that there is no overfitting in the model, which improves the generalization ability of the model. Finally, the model is compared with traditional underwater acoustic target recognition, and its accuracy is significantly improved by 6.8%.

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