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Target maneuver trajectory prediction is an important prerequisite for air combat situation awareness and threat assessment. Aiming at the problem of low prediction accuracy in traditional trajectory prediction methods, combined with the chaotic characteristics of the target maneuver trajectory time series, a target maneuver trajectory prediction m...
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Citations
... Target maneuver trajectory prediction is an important prerequisite for air combat situation awareness and threat assessment. Xi et al. [24] proposed a prediction model of target maneuver trajectory based on chaos theory and an improved genetic algorithm-Volterra neural network (IGA-VNN) model, where the chaotic time-series IGA-VNN model was applied to target maneuver trajectory time series prediction. In close-range air combat, highly reliable trajectory prediction can greatly help the pilot win a battle. ...
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.
... 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. ...
... The state response of system (35) from zero moments to time t is formulated as ...
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.
... This advantage makes ANN widely used. Xi et al. (2020) proposed a target maneuver trajectory prediction model based on chaotic theory and improved genetic algorithm-Volterra neural ...
In recent years, unmanned aerial vehicle (UAV) autonomous flight technology has been applied in many fields. However, in the process of autonomous operation, the UAV may deviate from the set flight path due to various disturbance factors, which results in mission failure. In order to find the abnormal situation in time and take corresponding measures, it is necessary to monitor the operation state of the UAV. Predicting the UAV flight path is the main monitoring method at present, but the accuracy and real-time of the existing prediction methods are limited. Therefore, this paper proposes an error compensation Bessel bidirectional long short-term memory (EC-Bessel-BiLSTM) real-time path prediction model deployed in ground stations. First, because of inconsistency of the units in all directions of the original positioning information provided by global positioning system (GPS), the Bessel geodetic coordinate transformation is introduced to unify the units of 3D coordinate data, so as to improve the prediction accuracy. Second, considering the problems of poor data quality and data missing in the operation process, the least square (LSQ) fitting method is used to supplement and correct the positioning coordinate data to obtain more reliable and accurate path observation values as the model input. Finally, a deep learning path prediction model based on bi-directional long short-term memory (BiLSTM) network is constructed, and the appropriate network parameters are determined with the prediction accuracy and time as evaluation indicators. In order to further improve the prediction accuracy, a compensator based on proportional integral differential (PID) error control theory is designed according to the output characteristics of the BiLSTM network, which is used for providing compensation values for the prediction results of the model. The training and testing results using the actual flight data of UAV operation show that, under the experimental environment built, the model proposed in this paper can complete the UAV flight path prediction with root mean square error (RMSE) less than 1m within 0.1s, and has better performance and higher prediction accuracy than other neural network models.
Nowadays, various innovative air combat paradigms that rely on unmanned aerial vehicles (UAVs), i.e., UAV swarm and UAV-manned aircraft cooperation, have received great attention worldwide. During the operation, UAVs are expected to perform agile and safe maneuvers according to the dynamic mission requirement and complicated battlefield environment. Deep reinforcement learning (DRL), which is suitable for sequential decision-making process, provides a powerful solution tool for air combat maneuver decision-making (ACMD), and hundreds of related research papers have been published in the last five years. However, as an emerging topic, there lacks a systematic review and tutorial. For this reason, this paper first provides a comprehensive literature review to help people grasp a whole picture of this field. It starts from the DRL itself and then extents to its application in ACMD. And special attentions are given to the design of reward function, which is the core of DRL-based ACMD. Then, a maneuver decision-making method based on one-to-one dogfight scenarios is proposed to enable UAV to win short-range air combat. The model establishment, program design, training methods and performance evaluation are described in detail. And the associated Python codes are available at gitee.com/wangyyhhh, thus enabling a quick-start for researchers to build their own ACMD applications by slight modifications. Finally, limitations of the considered model, as well as the possible future research direction for intelligent air combat, are also discussed.
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.
Target maneuver trajectory prediction is an important prerequisite for air combat situation awareness and maneuver decision-making. However, how to use a large amount of trajectory data generated by air combat confrontation training to achieve real-time and accurate prediction of target maneuver trajectory is an urgent problem to be solved. To solve this problem, in this paper, a hybrid algorithm based on transfer learning, online learning, ensemble learning, regularization technology, target maneuvering segmentation point recognition algorithm, and Volterra series, abbreviated as AERTrOS-Volterra is proposed. Firstly, the model makes full use of a large number of trajectory sample data generated by air combat confrontation training, and constructs a Tr-Volterra algorithm framework suitable for air combat target maneuver trajectory prediction, which realizes the extraction of effective information from the historical trajectory data. Secondly, in order to improve the real-time online prediction accuracy and robustness of the prediction model in complex electromagnetic environments, on the basis of the Tr-Volterra algorithm framework, a robust regularized online Sequential Volterra prediction model is proposed by integrating online learning method, regularization technology and inverse weighting calculation method based on the priori error. Finally, inspired by the preferable performance of models ensemble, ensemble learning scheme is also incorporated into our proposed algorithm, which adaptively updates the ensemble prediction model according to the performance of the model on real-time samples and the recognition results of target maneuvering segmentation points, including the adaptation of model weights; adaptation of parameters; and dynamic inclusion and removal of models. Compared with many existing time series prediction methods, the newly proposed target maneuver trajectory prediction algorithm can fully mine the prior knowledge contained in the historical data to assist the current prediction. The rationality and effectiveness of the proposed algorithm are verified by simulation on three sets of chaotic time series data sets and a set of real target maneuver trajectory data sets.
