Research on radar signal recognition based on automatic machine learning
Furkejuvvon:
| Publikašuvnnas: | Neural Computing & Applications vol. 32, no. 7 (Apr 2020), p. 1959 |
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| Váldodahkki: | |
| Almmustuhtton: |
Springer Nature B.V.
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| Fáttát: | |
| Liŋkkat: | Citation/Abstract Full Text - PDF |
| Fáddágilkorat: |
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MARC
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| 001 | 2288317243 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 0941-0643 | ||
| 022 | |a 1433-3058 | ||
| 024 | 7 | |a 10.1007/s00521-019-04494-1 |2 doi | |
| 035 | |a 2288317243 | ||
| 045 | 2 | |b d20200401 |b d20200430 | |
| 100 | 1 | |a Li, Peng |u Chongqing University, College of Microelectronics and Communication Engineering, Chongqing, China (GRID:grid.190737.b) (ISNI:0000 0001 0154 0904); Chongqing University of Arts and Sciences, School of Electronic and Electrical Engineering, Chongqing, China (GRID:grid.449955.0) (ISNI:0000 0004 1762 504X) | |
| 245 | 1 | |a Research on radar signal recognition based on automatic machine learning | |
| 260 | |b Springer Nature B.V. |c Apr 2020 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a With the advancement of machine learning and radar technology, machine learning is becoming more and more widely used in the field of radar. Radar scanning, signal acquisition and processing, one-dimensional range image, radar SAR, ISAR image recognition, radar tracking and guidance are all integrated into machine learning technology, but machine learning technology relies heavily on human machine learning experts for radar signal recognition. In order to realize the automation of radar signal recognition by machine learning, this paper proposes an automatic machine learning AUTO-SKLEARN system and applies it to radar radiation source signals. Identification: Firstly, this paper briefly introduces the classification of traditional machine learning algorithms and the types of algorithms specifically included in each type of algorithm. On this basis, the machine learning Bayesian algorithm is introduced. Secondly, the automatic machine learning AUTO based on Bayesian algorithm is proposed. -SKLEARN system, elaborates the process of AUTO-SKLEARN system in solving automatic selection algorithm and hyperparameter optimization, including meta-learning and its program implementation and automatic model integration construction. Finally, this paper introduces the process of automatic machine learning applied to radar emitter signal recognition. Through data simulation and experiment, the effect of traditional machine learning k-means algorithm and automatic machine learning AUTO-SKLEARN system in radar signal recognition is compared, which shows that automatic machine learning is feasible for radar signal recognition. The automatic machine learning AUTO-SKLEARN system can significantly improve the accuracy of the radar emitter signal recognition process, and the scheme is more reliable in signal recognition stability. | |
| 653 | |a Machine learning | ||
| 653 | |a Military communications | ||
| 653 | |a Signal processing | ||
| 653 | |a Bayesian analysis | ||
| 653 | |a Radar tracking | ||
| 653 | |a Automation | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Data simulation | ||
| 653 | |a Synthetic aperture radar | ||
| 653 | |a Optimization | ||
| 653 | |a Algorithms | ||
| 653 | |a Image acquisition | ||
| 653 | |a Radar imaging | ||
| 653 | |a Object recognition | ||
| 653 | |a Radar scanning | ||
| 653 | |a Computer simulation | ||
| 653 | |a Emitters | ||
| 773 | 0 | |t Neural Computing & Applications |g vol. 32, no. 7 (Apr 2020), p. 1959 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2288317243/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/2288317243/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |