Deep Neural Network-Based Guidance Law Using Supervised Learning
This paper proposes that the deep neural network-based guidance (DNNG) law replace the proportional navigation guidance (PNG) law. This approach is performed by adopting a supervised learning (SL) method using a large amount of simulation data from the missile system with PNG. Then, the proposed DNN...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/21/7865 |
| _version_ | 1797548589347504128 |
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| author | Minjeong Kim Daseon Hong Sungsu Park |
| author_facet | Minjeong Kim Daseon Hong Sungsu Park |
| author_sort | Minjeong Kim |
| collection | DOAJ |
| description | This paper proposes that the deep neural network-based guidance (DNNG) law replace the proportional navigation guidance (PNG) law. This approach is performed by adopting a supervised learning (SL) method using a large amount of simulation data from the missile system with PNG. Then, the proposed DNNG is compared with the PNG, and its performance is evaluated via the hitting rate and the energy function. In addition, the DNN-based only line-of-sight (LOS) rate input guidance (DNNLG) law, in which only the LOS rate is an input variable, is introduced and compared with the PN and DNNG laws. Then, the DNNG and DNNLG laws examine behavior in an initial position other than the training data. |
| first_indexed | 2024-03-10T15:02:44Z |
| format | Article |
| id | doaj.art-0bd4a3acb1f6442dabb809a286d694a1 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T15:02:44Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-0bd4a3acb1f6442dabb809a286d694a12023-11-20T20:01:53ZengMDPI AGApplied Sciences2076-34172020-11-011021786510.3390/app10217865Deep Neural Network-Based Guidance Law Using Supervised LearningMinjeong Kim0Daseon Hong1Sungsu Park2Department of Aerospace Engineering, Sejong University, Seoul 05006, KoreaDepartment of Aerospace Engineering, Sejong University, Seoul 05006, KoreaDepartment of Aerospace Engineering, Sejong University, Seoul 05006, KoreaThis paper proposes that the deep neural network-based guidance (DNNG) law replace the proportional navigation guidance (PNG) law. This approach is performed by adopting a supervised learning (SL) method using a large amount of simulation data from the missile system with PNG. Then, the proposed DNNG is compared with the PNG, and its performance is evaluated via the hitting rate and the energy function. In addition, the DNN-based only line-of-sight (LOS) rate input guidance (DNNLG) law, in which only the LOS rate is an input variable, is introduced and compared with the PN and DNNG laws. Then, the DNNG and DNNLG laws examine behavior in an initial position other than the training data.https://www.mdpi.com/2076-3417/10/21/7865proportional navigation guidance (PNG) lawdeep neural networks-based guidance (DNNG) lawsupervised learning (SL)homing missile |
| spellingShingle | Minjeong Kim Daseon Hong Sungsu Park Deep Neural Network-Based Guidance Law Using Supervised Learning Applied Sciences proportional navigation guidance (PNG) law deep neural networks-based guidance (DNNG) law supervised learning (SL) homing missile |
| title | Deep Neural Network-Based Guidance Law Using Supervised Learning |
| title_full | Deep Neural Network-Based Guidance Law Using Supervised Learning |
| title_fullStr | Deep Neural Network-Based Guidance Law Using Supervised Learning |
| title_full_unstemmed | Deep Neural Network-Based Guidance Law Using Supervised Learning |
| title_short | Deep Neural Network-Based Guidance Law Using Supervised Learning |
| title_sort | deep neural network based guidance law using supervised learning |
| topic | proportional navigation guidance (PNG) law deep neural networks-based guidance (DNNG) law supervised learning (SL) homing missile |
| url | https://www.mdpi.com/2076-3417/10/21/7865 |
| work_keys_str_mv | AT minjeongkim deepneuralnetworkbasedguidancelawusingsupervisedlearning AT daseonhong deepneuralnetworkbasedguidancelawusingsupervisedlearning AT sungsupark deepneuralnetworkbasedguidancelawusingsupervisedlearning |