Intercept Point Prediction of Ballistic Missile Defense Using Neural Network Learning

Lee, Jun-Yong; Jo, Byeong-Un; Moon, Gun-Hee; Tahk, Min-Jea; Ahn, Ji-Whan

doi:10.1007/s42405-020-00292-5

Cited by 10 publications

(5 citation statements)

References 16 publications

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“…We used a deep learning model to predict the trajectory by extracting and learning data features. We used a sliding time window instead of just the last state, and our findings were in accordance with recent studies indicating that a prediction model based on deep learning can achieve satisfactory results [21][22][23][24][25][26]. The prediction model based on the LSTM model could effectively avoid gradient disappearance or gradient explosion.…”

Section: Discussionsupporting

confidence: 83%

Two-Dimensional Space Turntable Pitch Axis Trajectory Prediction Method Based on Sun Vector and CNN-LSTM Model

et al. 2023

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A two-dimensional space turntable system has been used to ensure that the Solar X-ray and Extreme Ultraviolet Imager (X-EUVI) can track the Sun stably, and the prediction of the two-dimensional turntable trajectory is an important part of payload health management. Different from the dynamic model using traditional trajectory prediction, we propose a new method for predicting the pitch axis trajectory of the turntable based on the sun vector and a deep learning CNN-LSTM model. First, the ideal solar position of the pitch axis was calculated using the sun vector. Then, the ideal solar position was combined with the running turntable pitch axis motor speed, current, and solar position error signal as the CNN-LSTM model input data. The model parameters were trained and adjusted through test data simulation using Fengyun-3E satellite orbit data. Finally, the next position of the pitch axis was predicted. The test results showed that in the sun vector and CNN-LSTM model, the RMSE value was 0.623 and the MSE value was 0.388. It was better than the LSTM model or CNN model alone and could accurately predict the pitch axis position.

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Section: Discussionsupporting

confidence: 83%

Two-Dimensional Space Turntable Pitch Axis Trajectory Prediction Method Based on Sun Vector and CNN-LSTM Model

et al. 2023

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“…Assume that x(n) is the range profile of the target, and s k (n) is the k-th range profile in the dataset. The correlation function of x(n) with s k (n) is defined as (1) where m is the shift of s k (n) . The normalized maximum correlation of x(n) with s k (n) is defined as Evidently, R k can be used to measure the similarity between x(n) and s k (n).…”

Section: Range-profile Matchingmentioning

confidence: 99%

“…Ballistic missile defense requires a warning time as long as possible, accurate trajectory computation, accurate intercept control, and timely effect assessment. However, according to a penetration technology, ballistic missile may release not only warheads, but also decoys in its midcourse, which makes its defense become very difficult [1][2][3][4][5]. The effective recognition of warheads from decoys thus has to be handled in the defense of ballistic missiles and has become a most challenging problem in ballistic missile defense systems.The features in radar cross section (RCS), shape, micro-motion, etc., can be used to recognize warheads from decoys.…”

mentioning

confidence: 99%

Recognition of warheads by range-profile matching with automatic threshold

Tan,

Wang

2024

EURASIP J. Adv. Signal Process.

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In this paper, a novel algorithm is presented for warhead recognition in the defense of ballistic missiles. The range profiles from the warheads of interest in typical illumination directions form a dataset. First, each range profile in the dataset is compared to the range profile of the target under observation, and the most similar range profile is found. Then, the observed target is considered as a warhead if the deviation of its range profile from the most similar range profile is less than or equal to a threshold. The threshold is chosen such that the detection rate is a constant. The simulation results verify the effectiveness of the proposed algorithm. Since the threshold is automatically calculated according to the detection rate, this algorithm has a larger applicability than the current methods based on range-profile matching.

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“…The intelligent game maneuver adopts a closed-loop maneuver scheme of "interceptor movement-situational awareness-maneuver strategy generation-maneuver control implementation" that realizes timely maneuvering to increase miss distance and increase evasion probability. The key to intelligent game maneuver lies in the selection of intelligent algorithms Among the intelligent algorithms associated with hypersonic aircraft, deep learning (DL)and reinforcement learning (RL) are the first to bear the brunt [21][22][23][24][25][26][27][28][29][30][31][32]. Due to its strong nonlinear fitting ability, the deep neural network (DNN) in DL has been widely used in the PE problems of hypersonic aircraft [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

“…The key to intelligent game maneuver lies in the selection of intelligent algorithms Among the intelligent algorithms associated with hypersonic aircraft, deep learning (DL)and reinforcement learning (RL) are the first to bear the brunt [21][22][23][24][25][26][27][28][29][30][31][32]. Due to its strong nonlinear fitting ability, the deep neural network (DNN) in DL has been widely used in the PE problems of hypersonic aircraft [21][22][23]. Among these, the most prevalent study [21] resolves the tension between the accuracy and speed of the IPP by building an IPP neural network model after using the ballistic model to create training data.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Maneuver Strategy for a Hypersonic Pursuit-Evasion Game Based on Deep Reinforcement Learning

Guo,

Jiang,

Huang

et al. 2023

Aerospace

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In order to improve the problem of overly relying on situational information, high computational power requirements, and weak adaptability of traditional maneuver methods used by hypersonic vehicles (HV), an intelligent maneuver strategy combining deep reinforcement learning (DRL) and deep neural network (DNN) is proposed to solve the hypersonic pursuit–evasion (PE) game problem under tough head-on situations. The twin delayed deep deterministic (TD3) gradient strategy algorithm is utilized to explore potential maneuver instructions, the DNN is used to fit to broaden application scenarios, and the intelligent maneuver strategy is generated with the initial situation of both the pursuit and evasion sides as the input and the maneuver game overload of the HV as the output. In addition, the experience pool classification strategy is proposed to improve the training convergence and rate of the TD3 algorithm. A set of reward functions is designed to achieve adaptive adjustment of evasion miss distance and energy consumption under different initial situations. The simulation results verify the feasibility and effectiveness of the above intelligent maneuver strategy in dealing with the PE game problem of HV under difficult situations, and the proposed improvement strategies are validated as well.

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Intercept Point Prediction of Ballistic Missile Defense Using Neural Network Learning

Cited by 10 publications

References 16 publications

Two-Dimensional Space Turntable Pitch Axis Trajectory Prediction Method Based on Sun Vector and CNN-LSTM Model

Two-Dimensional Space Turntable Pitch Axis Trajectory Prediction Method Based on Sun Vector and CNN-LSTM Model

Recognition of warheads by range-profile matching with automatic threshold

Intelligent Maneuver Strategy for a Hypersonic Pursuit-Evasion Game Based on Deep Reinforcement Learning

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