The Application of Alterable Parameter Genetic Algorithm and Neural Network in Artillery Locating Radar Detecting Small Signal

Shang Guo Tan; Rui Dong Hou; Wei Pan

doi:10.4028/www.scientific.net/AMR.645.259

Paper Titles

Auto Disturbance Rejection Control for Nonlinear Object
p.243

Novel Approach against Reverse Bandwagon Profile Inject Attack in Recommender Systems
p.247

The Shape of Fruit Recognition Using Matching Round
p.251

Research on Methods of Improving Platinum Resistance Sensor’s Temperature Measurement Precision
p.255

The Application of Alterable Parameter Genetic Algorithm and Neural Network in Artillery Locating Radar Detecting Small Signal
p.259

Harmonic Impact on the Energy Meter
p.263

On the Shortest and Conflict-Free Path Planning of Multi-AGV System Based on Dijkstra Algorithm and the Dynamic Time-Window Method
p.267

The Calibration & Monitor System of Battery Operation Parameters Simulation Generator Based on LabVIEW
p.272

Construction of the Mine Safety Knowledge Management Information Platform
p.276

HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 645The Application of Alterable Parameter Genetic...

The Application of Alterable Parameter Genetic Algorithm and Neural Network in Artillery Locating Radar Detecting Small Signal

Abstract:

To perform effective radar small signal detection in low SNR, a signal-processing model is established. In the model, the feature factors that distinguish small signal from noise are defined with whitening process and feature decomposition frequency estimation, then the RBF parameters are optimized by using genetic algorithm and APGA-RBF neural network is formed to realize classification, thereby the small signal detection is completed. Results of simulation show that the detection probability is greatly increased as well as the performance of classification.

You might also be interested in these eBooks

Intelligent System, Applied Materials and Control Technology

View Preview

Info:

Periodical:

Advanced Materials Research (Volume 645)

Pages:

259-262

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.645.259

Citation:

Cite this paper

Online since:

January 2013

Authors:

Shang Guo Tan, Rui Dong Hou, Wei Pan

Keywords:

Alterable Parameter Genetic Algorithm, Feature Extraction, Neural Network (NN), Signal Detection

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] H. Leung. Small target detection in clutter using recursive nonlinear prediction [J]. IEEE Transaction on AES, 2000(2), 36: 713-718.

DOI: 10.1109/7.845269

Google Scholar

[2] Henry Leung, Geoffrey Hennessey, Signal Detection Using the Radial Basis Function Coupled Map Lattice[J]. IEEE Transaction on Neural Networks, 2000, 11(5): 1133-1151.

DOI: 10.1109/72.870045

Google Scholar

[3] Ma Xiao-yan, Li Chun-xia, Zhang Xian-da. The Modification of intelligent target detection in nonstationary clutter. 2001 CIE International conference on radar proceedings, 2001: 324-328.

DOI: 10.1109/icr.2001.984683

Google Scholar

[4] Henry Leung, Neville Dubash, Nan Xie. Detection of Small Objects in Clutter Using a GA-RBF Neural Nnetwork[J]. IEEE Trans. on AES, 2002, 38(1): 98-118.

DOI: 10.1109/7.993232

Google Scholar

[5] Li jianxun. Research on Neural Network-based Radar Anti-deceptive Jamming Method. Wuhan: Air Force Radar Academy, 2004 (in Chinese).

Google Scholar