Trajectory Prediction of Target Aircraft Based on HPSO-TPFENN Neural Network

Wang, Xin; Yang, Rennong; Zuo, Jialiang; Xu, Xiaowei; Yue, Longfei

doi:10.1051/jnwpu/20193730612

Cited by 9 publications

(7 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Select 𝒗 , 𝒗 , ⋯ , 𝒗 , 𝒗 as the first set of input data, and label the intent type 𝑞 corresponding to the time period 1 to 12; use 𝒗 , 𝒗 , ⋯ , 𝒗 , 𝒗 as the second set of input data, the label is the intent type 𝑞 corresponding to the time period 2 5) (6). The method of constructing the test data is consistent with the training sample data [26].…”

Section: A Input Layermentioning

confidence: 99%

An Air Target Tactical Intention Recognition Model Based on Bidirectional GRU With Attention Mechanism

et al. 2021

View full text Add to dashboard Cite

show abstract

Section: A Input Layermentioning

confidence: 99%

An Air Target Tactical Intention Recognition Model Based on Bidirectional GRU With Attention Mechanism

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The training sample input data and training sample labels are generated this way and shown below. The test data are constructed in the same way as the training sample data [ 28 ].

…”

Section: Model Frameworkmentioning

confidence: 99%

“…where [28]. e collected aerial target combat intention characteristic set V m is now in a characteristic vector form that can be directly accepted and processed by the hidden layer.…”

Section: Input Layermentioning

confidence: 99%

A GRU‐Based Method for Predicting Intention of Aerial Targets

Teng

Song

Gang

et al. 2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Since a target’s operational intention in air combat is realized by a series of tactical maneuvers, its state presents the characteristics of temporal and dynamic changes. Depending only on a single moment to take inference, the traditional combat intention recognition method is neither scientific nor effective enough. Based on a gated recurrent unit (GRU), a bidirectional propagation mechanism and attention mechanism are introduced in a proposed aerial target combat intention recognition method. The proposed method constructs an air combat intention characteristic set through a hierarchical approach, encodes into numeric time-series characteristics, and encapsulates domain expert knowledge and experience in labels. It uses a bidirectional gated recurrent units (BiGRU) network for deep learning of air combat characteristics and adaptively assigns characteristic weights using an attention mechanism to improve the accuracy of aerial target combat intention recognition. In order to further shorten the time for intention recognition and with a certain predictive effect, an air combat characteristic prediction module is introduced before intention recognition to establish the mapping relationship between predicted characteristics and combat intention types. Simulation experiments show that the proposed model can predict enemy aerial target combat intention one sampling point ahead of time based on 89.7% intent recognition accuracy, which has reference value and theoretical significance for assisting decision-making in real-time intention recognition.

show abstract

“…In [33], it has been proposed that independent prediction of three-dimensional coordinates is more accurate than the overall prediction, so the coordinates on the X, Y, and Z axes are individually used as the input of the LSTM network. When using the three-degree-of-freedom model to simulate the trajectory, the data are sampled at an interval of 0.3 s, and ten times are sampled as a group.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

“…A group of maneuvering trajectories of the enemy aircraft is generated randomly by using the three-degree-of-freedom model, sample 300 times, and to make a prediction for every 10 groups, a total of 30 cycles are predicted. To improve the prediction accuracy, three-dimensional coordinate independent prediction [33] is used to compare with traditional RNN, CNN, and LSTM prediction methods. e results are shown in Figure 10.…”

Section: Comparison Of Predicted Trajectoriesmentioning

confidence: 99%

Moving Time UCAV Maneuver Decision Based on the Dynamic Relational Weight Algorithm and Trajectory Prediction

Xie

Ding

Wei

et al. 2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

To improve the accuracy and real-time performance of autonomous decision-making by the unmanned combat aerial vehicle (UCAV), a decision-making method combining the dynamic relational weight algorithm and moving time strategy is proposed, and trajectory prediction is added to maneuver decision-making. Considering the lack of continuity and diversity of air combat situation reflected by the constant weight in situation assessment, a dynamic relational weight algorithm is proposed to establish an air combat situation system and adjust the weight according to the current situation. Based on the dominance function, this method calculates the correlation degree of each subsituation and the total situation. According to the priority principle and information entropy theory, the hierarchical fitting function is proposed, the association expectation is calculated by using if-then rules, and the weight is dynamically adjusted. In trajectory prediction, the online sliding input module is introduced, and the long- and short-term memory (LSTM) network is used for real-time prediction. To further improve the prediction accuracy, the adaptive boosting (Ada) method is used to build the outer frame and compare with three traditional prediction networks. The results show that the prediction accuracy of Ada-LSTM is better. In the decision-making method, the moving time optimization strategy is adopted. To solve the problem of timeliness and optimization, each control variable is divided into 9 gradients, and there are 729 control schemes in the control sequence. Through contrast pursuit simulation experiments, it is verified that the maneuver decision method combining the dynamic relational weight algorithm and moving time strategy has a better accuracy and real-time performance. In the case of using prediction and not using prediction, the adaptive countermeasure simulation is carried out with the current more advanced Bayesian inference maneuvering decision-making scheme. The results show that the UCAV maneuvering decision-making ability combined with accurate prediction is better.

show abstract

Trajectory Prediction of Target Aircraft Based on HPSO-TPFENN Neural Network

Cited by 9 publications

References 5 publications

An Air Target Tactical Intention Recognition Model Based on Bidirectional GRU With Attention Mechanism

An Air Target Tactical Intention Recognition Model Based on Bidirectional GRU With Attention Mechanism

A GRU‐Based Method for Predicting Intention of Aerial Targets

Moving Time UCAV Maneuver Decision Based on the Dynamic Relational Weight Algorithm and Trajectory Prediction

Contact Info

Product

Resources

About