Target maneuver trajectory prediction based on RBF neural network optimized by hybrid algorithm

Zhang, Ning; An, Xuehui; Kou, Yingxin; Li, Zhanwu; Yang, Ai-wu

doi:10.23919/jsee.2021.000042

Cited by 16 publications

(7 citation statements)

References 5 publications

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“…Zhang et al [ 25 ] proposed an attention-based convolution LSTM memory network to calculate the arrival probability of each space in the reachable region of the target aircraft, which has a higher accuracy than other existing algorithms. Xi et al [ 26 ] developed a target maneuver trajectory prediction model based on phase space reconstruction-radial basis function neural network.…”

Section: Literature Reviewmentioning

confidence: 99%

Air Combat Intention Recognition with Incomplete Information Based on Decision Tree and GRU Network

Xia

Chen

Fang

2023

Entropy

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Battlefield information is generally incomplete, uncertain, or deceptive. To realize enemy intention recognition in an uncertain and incomplete air combat information environment, a novel intention recognition method is proposed. After repairing the missing state data of an enemy fighter, the gated recurrent unit (GRU) network, supplemented by the highest frequency method (HFM), is used to predict the future state of enemy fighter. An intention decision tree is constructed to extract the intention classification rules from the incomplete a priori knowledge, where the decision support degree of attributes is introduced to determine the node-splitting sequence according to the information entropy of partitioning (IEP). Subsequently, the enemy fighter intention is recognized based on the established intention decision tree and the predicted state data. Furthermore, a target maneuver tendency function is proposed to screen out the possible deceptive attack intention. The one-to-one air combat simulation shows that the proposed method has advantages in both accuracy and efficiency of state prediction and intention recognition, and is suitable for enemy fighter intention recognition in small air combat situations.

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Section: Literature Reviewmentioning

confidence: 99%

Air Combat Intention Recognition with Incomplete Information Based on Decision Tree and GRU Network

Xia

Chen

Fang

2023

Entropy

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“…However, since future disturbances caused by the target maneuver cannot be measured in advance, we can only predict them by finding the existing laws in the historical data. Fitting extrapolation is a common method in target trajectory prediction [34,35], whose basic idea is to use the preestablished models or functions to approximate the historical data and predict the future motion of the target with the fitted model. Based on the fitting extrapolation, we propose a disturbance estimation method for short-term prediction and specify its implementation at each instant.…”

Section: Disturbance Estimationmentioning

confidence: 99%

Three-Dimensional Guidance Approach with Obstacle Avoidance against Maneuvering Targets

Jiang¹,

Yang²,

Zhang³

et al. 2023

International Journal of Aerospace Engineering

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A guidance approach with obstacle avoidance is proposed for maneuvering target interception. Firstly, by decomposing the constrained optimal guidance problem into two associated subproblems, the overall scheme of guidance is presented. Secondly, to improve the guidance accuracy against the maneuvering target, an MPC controller with disturbance estimation is designed, which transforms the global optimization problem into a finite-horizon optimal control problem. An auxiliary controller is also developed to ensure the system’s stability. Then, to achieve obstacle avoidance without affecting the guidance accuracy, a unified function is built to assess the threats from obstacles of different shapes, based on which the optimal guidance-obstacle avoidance model is established. Finally, to solve the above optimization problems, a hybrid solver is developed based on the adaptive moment estimation (Adam) algorithm, whose output will serve as the real-time guidance command. The numerical simulation results show that the guidance approach presented in this paper can achieve precise guidance and obstacle avoidance in various scenarios, and the Monte Carlo experiment further demonstrates the robustness and computation efficiency of the approach.

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“…11 Trajectory planning in Cartesian space is to interpolate the end coordinates to ensure accurate motion of the robot. [15][16][17][18] The circular and linear trajectory planning programs are written in Matlab, and the planning results are as shown in Figures 1 and 2.…”

Section: Robot Cartesian Space Trajectory Planningmentioning

confidence: 99%

“…In Luo, 14 the target trajectory prediction model is established based on LSTM, and its parameters are optimized to realize real-time tracking of the target by the robot in different scenarios. The target maneuvering trajectory prediction model based on PSR-RBF is established in Zhifei et al, 15 and the prediction model parameters are optimized LM and IPSO algorithms to improve the prediction performance of the target trajectory.…”

Section: Introductionmentioning

confidence: 99%

Research on autonomous dynamic real-time capture of robot based on hybrid prediction model

Guo

Yang

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part C:

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The trajectory of the dynamic target is an important basis for the robot to capture the action, and its advanced prediction precision plays a decisive role in achieving efficient capture. In this paper, a dynamic adaptive weighting method is proposed to establish a hybrid prediction model of GM (1,1) and SESM. The weights are allocated in real time, according to the prediction accuracy to improve the prediction accuracy of a single model and enhance the applicability of spatial trajectory prediction. Then, based on the rolling prediction principle, a high-precision advanced prediction method of the hybrid model is created. Compared with the advanced prediction of the model itself, the average prediction errors of the spatial arc and straight line verification trajectories are reduced by 40.26% and 49.77% respectively. The simulation model of robot dynamic target capture is built in Matlab, and the measured trajectory is used as the target trajectory. The robot capture experiment platform is built, and the target motion trajectory is collected by the camera in real time to capture the experiment, which verifies the effectiveness of the proposed method in actual capture.

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Target maneuver trajectory prediction based on RBF neural network optimized by hybrid algorithm

Cited by 16 publications

References 5 publications

Air Combat Intention Recognition with Incomplete Information Based on Decision Tree and GRU Network

Air Combat Intention Recognition with Incomplete Information Based on Decision Tree and GRU Network

Three-Dimensional Guidance Approach with Obstacle Avoidance against Maneuvering Targets

Research on autonomous dynamic real-time capture of robot based on hybrid prediction model

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