Hypersonic Glide Research Articles

Intelligent Online Multiconstrained Reentry Guidance Based on Hindsight Experience Replay

Traditional guidance algorithms for hypersonic glide vehicles face the challenge of real-time requirements and robustness to multiple deviations or tasks. In this paper, an intelligent online multiconstrained reentry guidance is proposed to strikingly reduce computational burden and enhance the effectiveness with multiple constraints. First, the simulation environment of reentry including dynamics, multiconstraints, and control variables is built. Different from traditional decoupling methods, the bank angle command including its magnitude and sign is designed as the sole guidance variable. Secondly, a policy neural network is designed to output end-to-end guidance commands. By transforming the reentry process into a Markov Decision Process (MDP), the policy network can be trained by deep reinforcement learning (DRL). To address the sparse reward issue caused by multiconstraints, the improved Hindsight Experience Replay (HER) method is adaptively combined with Deep Deterministic Policy Gradient (DDPG) algorithm by transforming multiconstraints into multigoals. As a result, the novel training algorithm can realize higher utilization of failed data and improve the rate of convergence. Finally, simulations for typical scenes show that the policy network in the proposed guidance can output effective commands in much less time than the traditional method. The guidance is robust to initial bias, different targets, and online aerodynamic deviation.

An adaptive state estimation for tracking hypersonic glide targets with model uncertainties

To track a maneuvering hypersonic glide target, an adaptive method for state estimation is proposed with model uncertainties in this paper. Various maneuver modes lead to considerable motion model uncertainty. In the method, unknown aerodynamic accelerations are modeled as the Singer model with a small maneuver frequency. When the real motion mode deviates from the default model, the maneuver frequency is adaptively enlarged by a novel technique derived from the orthogonal principle to reduce the model error. Simultaneously, the Huber technique is adopted to address measurement model uncertainty. Two strategies are specially formulated to alleviate possible misjudgments that a type of model error activates the non-corresponding technique. The adaptive state estimation is realized under the framework of unscented Kalman filter. Through tracking different flight trajectories, simulation results demonstrate that the proposed method has stronger robustness and higher estimation accuracy than conventional methods in the presence of model uncertainties and the computation burden is significantly less than the multiple-model method.

Detecting and tracking hypersonic glide vehicles: A cybersecurity-engineering analysis of academic literature

Hypersonic vehicles are vehicles travelling faster than Mach 5 (five times the speed of sound). Hypersonic technologies have existed since the end of the 1950s, but recent developments of defence applications have led to their resurgence. Hypersonic weapons can be hypersonic (powered) cruise missiles or hypersonic glide vehicles (HGVs). The near-space trajectories of HGV, combined with their superior manoeuvrability, enable HGVs to evade existing space and terrestrial sensors used to track ballistic missiles, posing an immediate threat to today’s radar networks and making HGVs well-suited for intercontinental (> 5500 km) targets. Securing HGV detection and tracking systems is of great interest to at-risk nations and cybersecurity researchers alike.
 
 However, like hypersonic flight technologies, HGV defence technologies are heavily guarded secrets. The shortage of public-domain information did not stop academia from proposing various detection and tracking schemes, but a reasonable question is: “How credible and useful is current public-domain information, including academic publications, on HGV detection and tracking for academic researchers to base their cybersecurity research on?” To answer this question, we scanned and critically reviewed public-domain literature on HGV detection and tracking. We then identified ambiguities and knowledge gaps in the literature. In this paper, we provide a concise version of our multivocal literature review and an analysis of the identified ambiguities and knowledge gaps in our attempt to answer our earlier question.

An LEO Constellation Early Warning System Decision-Making Method Based on Hierarchical Reinforcement Learning.

The cooperative positioning problem of hypersonic vehicles regarding LEO constellations is the focus of this research study on space-based early warning systems. A hypersonic vehicle is highly maneuverable, and its trajectory is uncertain. New challenges are posed for the cooperative positioning capability of the constellation. In recent years, breakthroughs in artificial intelligence technology have provided new avenues for collaborative multi-satellite intelligent autonomous decision-making technology. This paper addresses the problem of multi-satellite cooperative geometric positioning for hypersonic glide vehicles (HGVs) by the LEO-constellation-tracking system. To exploit the inherent advantages of hierarchical reinforcement learning in intelligent decision making while satisfying the constraints of cooperative observations, an autonomous intelligent decision-making algorithm for satellites that incorporates a hierarchical proximal policy optimization with random hill climbing (MAPPO-RHC) is designed. On the one hand, hierarchical decision making is used to reduce the solution space; on the other hand, it is used to maximize the global reward and to uniformly distribute satellite resources. The single-satellite local search method improves the capability of the decision-making algorithm to search the solution space based on the decision-making results of the hierarchical proximal policy-optimization algorithm, combining both random hill climbing and heuristic methods. Finally, the MAPPO-RHC algorithm's coverage and positioning accuracy performance is simulated and analyzed in two different scenarios and compared with four intelligent satellite decision-making algorithms that have been studied in recent years. From the simulation results, the decision-making results of the MAPPO-RHC algorithm can obtain more balanced resource allocations and higher geometric positioning accuracy. Thus, it is concluded that the MAPPO-RHC algorithm provides a feasible solution for the real-time decision-making problem of the LEO constellation early warning system.

Trajectory prediction in pipeline form for intercepting hypersonic gliding vehicles based on LSTM

The interception problem of Hypersonic Gliding Vehicles (HGVs) has been an important aspect of missile defense systems. In order to provide interceptors with accurate information of target trajectory, a model based on an improved Long Short-Time Memory (LSTM) network for trajectory prediction pipeline is proposed for the interception of a skip gliding hypersonic target. Firstly, for trajectory prediction required by intercepting guidance laws, the altitude, velocity and velocity direction of the target are formulated in the form of analytic functions, consisting of linear decay terms and amplitude decay sinusoidal terms. Then, the dynamic characteristics of the model parameters are analyzed, and the target trajectory prediction pipeline is proposed with the prediction error considered. Finally, an improved LSTM network is designed to estimate parameters in a dynamically-updated manner, and estimation results are used for the calculation of the final trajectory prediction pipeline. The proposed prediction algorithm provides information on the velocity vector for midcourse guidance with the effect of prediction errors on interception taken into account. Simulation is conducted and the results show the high accuracy of the algorithm in HGVs’ trajectory prediction which is conducive to increasing the interception success rate.

A deep learning-based approach to time-coordination entry guidance for multiple hypersonic vehicles

AbstractA multiple-vehicles time-coordination guidance technique based on deep learning is suggested to address the cooperative guiding problem of hypersonic gliding vehicle entry phase. A dual-parameter bank angle profile is used in longitudinal guiding to meet the requirements of time coordination. A vehicle trajectory database is constructed along with a deep neural network (DNN) structure devised to fulfill the error criteria, and a trained network is used to replace the conventional prediction approach. Moreover, an extended Kalman filter is constructed to detect changes in aerodynamic parameters in real time, and the aerodynamic parameters are fed into a DNN. The lateral guiding employs a logic for reversing the sign of bank angle, which is based on the segmented heading angle error corridor. The final simulation results demonstrate that the built DNN is capable of addressing the cooperative guiding requirements. The algorithm is highly accurate in terms of guiding, has a fast response time, and does not need inter-munition communication, and it is capable of solving guidance orders that satisfy flight requirements even when aerodynamic parameter disruptions occur.

Cooperative optimal guidance of hypersonic glide vehicles by real-time optimization and deep learning

This paper investigates the cooperative optimal guidance of hypersonic glide vehicles (HGVs) in the terminal phase with consideration on time-varying velocity, aerodynamic forces, and practical constraints. The cooperative optimal guidance problem is formulated as an optimal control problem with time as the independent variable, which brings great convenience in controlling the impact time. We propose proper convexification techniques to convexify this problem and apply successive convex optimization to get the solution of the original problem. To achieve cooperative guidance of multiple HGVs with time coordination constraint, a deep learning–based approach is proposed to find an optimal common impact time assigned to all the HGVs. The training samples required by deep learning are obtained by convex optimization. An algorithm is then presented to summarize the cooperative optimal guidance strategy. In each guidance loop, the common impact time is updated according to mission conditions, and the guidance commands are generated by the successive solution procedure in real time. Numerical examples will be provided to demonstrate that the proposed cooperative optimal guidance algorithm is effective and efficient, and it can achieve better performance than a popular cooperative proportional navigation guidance law.

Attitude Control of a Hypersonic Glide Vehicle Based on Reduced-Order Modeling and NESO-Assisted Backstepping Variable Structure Control

Aiming at solving the control problem caused by the large-scale change of the Hypersonic Glide Vehicle (HGV) parameters, this paper proposes a design method of backstepping variable structure attitude controller based on Nonlinear Extended State Observer (NESO), with the characteristics of HGV model and the idea of uncertainty estimation and compensation associated. Firstly, the design of the second-order NESO is studied. Due to the large number of NESO parameters, a systematic method for determining the second-order NESO parameters is given in this paper, and the stability of the observer is proved completely using the piecewise Lyapunov analysis. Then, the NESO-assisted backstepping variable structure attitude controller employs the reduced-order modeling idea to decompose the whole system design problem into two first-order subsystem design problem, and classifies the nonlinear dynamic changes caused by the large-scale changes of the aircraft parameters into the aggregated uncertain terms of the two subsystems. The simulation results show that the backstepping attitude controller based on NESO can realize the stable and accurate tracking of the flight attitude when the aircraft parameters change in a large range.

Active-set pseudospectral model predictive static programming for midcourse guidance

This paper focuses on the midcourse guidance law for the interceptor to engage the hypersonic gliding vehicles (HGVs). Firstly, an active-set pseudospectral model predictive static programming (APS-MPSP) algorithm is presented, which entails two major improvements: the application of pseudospectral discretion and handling of control constraint using active-set iteration strategy. Except inheriting the high efficiency of the MPSP, this new algorithm can address the control constraint directly. Secondly, the optimal predictive guidance design is established, which embeds the target prediction into the guidance framework by formulating a simultaneous nonlinear optimal problem. Employing the APS-MPSP algorithm to solve this optimal problem efficiently, the predictive guidance command that ensures interceptor reach an appropriate predicted interception point (PIP) can be obtained directly. This strategy has the merits of no need to precompute the PIP or time-to-go and high prediction accuracy. The reliability and efficiency of the proposed methods are verified by numerical examples and comparisons with already methods.

Spatial management of air and missile defence operations

This paper relates to the accepted presentation presented at the international Air and Missile Defence Technology Conference, held on November 16–17, 2022, in London, the UK, (day two), reflecting the contents of the presentation slides. It describes applications of the patented and internationally tested Spatial Grasp Technology (SGT) and its Spatial Grasp Language (SGL) for integrated air and missile defense (IAMD). Based on holistic space navigation and processing by recursive mobile code self-spreading in distributed words, SGT differs radically from the traditional management of large systems since it consists of parts exchanging messages. The dynamic network of SGL interpreters can be arbitrarily large and cover terrestrial and celestial environments as powerful spatial engines. The paper contains an example of tracking and destruction of multiple cruise missiles by self-evolving spatial intelligence in SGL using networks of radar stations. It also briefs the growing multiple satellite constellation in low Earth orbits (LEO) for potential IAMD applications. Starting from the Strategic Defense Initiative (SDI) of the past and then briefing the latest project of the Space Development Agency, the paper shows SGL solutions for discovery, tracking, and destroying ballistic missiles and hypersonic gliders with the use of collectively behaving constellations of LEO satellites. It also shows how to organize higher levels of supervision of groups of mobile chasers fighting multiple targets (potentially both missiles and drones), by providing global awareness and even consciousness in SGL which can drastically improve their performance. The latest version of SGT can be implemented on any platform and put into operation in a short time, similar to its previous versions in different countries.

Deep Neural Network-Based Footprint Prediction and Attack Intention Inference of Hypersonic Glide Vehicles

In response to the increasing threat of hypersonic weapons, it is of great importance for the defensive side to achieve fast prediction of their feasible attack domain and online inference of their most probable targets. In this study, an online footprint prediction and attack intention inference algorithm for hypersonic glide vehicles (HGVs) is proposed by leveraging the utilization of deep neural networks (DNNs). Specifically, this study focuses on the following three contributions. First, a baseline multi-constrained entry guidance algorithm is developed based on a compound bank angle corridor, and then a dataset containing enough trajectories for the following DNN learning is generated offline by traversing different initial states and control commands. Second, DNNs are developed to learn the functional relationship between the flight state/command and the corresponding ranges; on this basis, an online footprint prediction algorithm is developed by traversing the maximum/minimum ranges and different heading angles. Due to the substitution of DNNs for multiple times of trajectory integration, the computational efficiency for footprint prediction is significantly improved to the millisecond level. Third, combined with the predicted footprint and the hidden information in historical flight data, the attack intention and most probable targets can be further inferred. Simulations are conducted through comparing with the state-of-the-art algorithms, and results demonstrate that the proposed algorithm can achieve accurate prediction for flight footprint and attack intention while possessing significant real-time advantage.

Analytical trajectory prediction for skip re-entry of lifting vehicle

An analytical trajectory prediction method is put forward for skip re-entry of lifting vehicles. First, the 3-D analytical solutions of skip re-entry trajectory are developed, which are crucial to realize the analytical trajectory prediction. To facilitate the derivation, an auxiliary geocentric inertial frame is established to deal with the strong nonlinear problem caused by the Earth’s rotation. Thereafter, the analytical solutions of the downrange, crossrange, and velocity are approximated by the outer solutions of a two-time scale singular perturbation. The analytical solutions of the altitude and flight-path angle are developed using regular perturbation techniques which can decompose the strongly coupled and nonlinear system into a sequence of linear problems. Second, since the analytical solutions are with respect to the lift-to-drag ratio and bank angle, a method based on the EKF technique is proposed to estimate the lift-to-drag ratio and bank angle of the re-entry vehicle. Third, the analytical trajectory prediction schemes for the lifting re-entry vehicle with multiple bank reversals are put forward. The simulations show that the proposed analytical solutions are in good agreement with the numerical solutions, but the computation time of the analytical solutions is about two orders of magnitude less than that of the numerical solutions. In addition, the superior performance of the proposed analytical trajectory prediction method is well demonstrated by the simulation results.

Intention Prediction of a Hypersonic Glide Vehicle Using a Satellite Constellation Based on Deep Learning

Tracking of hypersonic glide vehicles (HGVs) by a constellation tracking and observation system is an important part of the space-based early warning system. The uncertainty in the maneuver intentions of HGVs has a non-negligible impact on the tracking and observation process. The cooperative scheduling of multiple satellites in an environment of uncertainty in the maneuver intentions of HGVs is the main problem researched in this paper. For this problem, a satellite constellation tracking decision method that considers the HGVs’ maneuver intentions is proposed. This method is based on building an HGV maneuver intention model, developing a maneuver intention recognition and prediction algorithm, and designing a sensor-switching strategy to improve the local consensus-based bundle algorithm (LCBBA). Firstly, a recognizable maneuver intention model that can describe the maneuver types and directions of the HGVs in both the longitudinal and lateral directions was designed. Secondly, a maneuver intention recognition and prediction algorithm based on parallel, stacked long short-term memory neural networks (PSLSTM) was developed to obtain maneuver directions of the HGV. On the basis of that, a satellite constellation tracking decision method (referred to as SS-LCBBA in the following) considering the HGVs’ maneuver intentions was designed. Finally, the maneuver intention prediction capability of the PSLSTM network and two currently popular network structures: the multilayer LSTM (M-LSTM) and the dual-channel and bidirectional neural network (DCBNN) were tested for comparison. The simulation results show that the PSLSTM can recognize and predict the maneuver directions of HGVs with high accuracy. In the simulation of a satellite constellation tracking HGVs, the SS-LCBBA improved the cumulative tracking score compared to the LCBBA, the blackboard algorithm (BM), and the variable-center contract network algorithm (ICNP). Thus, it is concluded that SS-LCBBA has better adaptability to environments with uncertain intentions in solving multi-satellite collaborative scheduling problems.

Time-coordination entry guidance using a range-determined strategy

A time-coordination entry guidance method based on a range-determined strategy is developed for hypersonic glide vehicles. This method is composed of two components: a time-constrained entry guidance algorithm and a coordinated flight time generation approach. Different from the previous work, the proposed strategy provides a novel scheme to solve the coordinated entry guidance problem, significantly reducing the computational load and the coupling between flight time control and the motion form. Firstly, the longitudinal guidance algorithm generates the flight profile based on the flight time constraint while considering adaptability to external disturbance. The magnitude of the bank angle and the range-to-go command can be derived from the profile. The command is used to plan the lateral trajectory represented by the Bézier curve in lateral guidance. Then, the heading error corridor can be established according to the line-of-sight between the vehicle and the reference point on the lateral trajectory, which controls the bank angle reversal time. The profile and the lateral trajectory are updated to guide the vehicle to the terminal point while meeting terminal and path constraints. This paper discusses how to estimate the adjustable range of a single vehicle's flight time and illustrates an approach for multi-vehicle coordinated flight time generation. Numerical simulations are conducted in nominal and dispersed conditions, and the results demonstrate the method's efficiency and robustness.

An Intelligent Trajectory Prediction Algorithm for Hypersonic Glide Targets Based on Maneuver Mode Identification

Trajectory prediction for hypersonic glide targets is a difficult task that needs to be solved. To improve the prediction precision for hypersonic glide targets, based on the analysis of the target’s maneuver characteristic, an intelligent trajectory prediction algorithm based on the maneuver mode identification is proposed in this paper. Firstly, according to the typical maneuver modes of the target, a group of parameter suitable for maneuver mode identification and parameter estimation is proposed. The proposed maneuver parameters can reflect the maneuvering characteristics of the target than the other control parameters. Then, the rationality of parameters is analyzed. Secondly, using the long-short-term memory network (LSTM), the structure of intelligent trajectory prediction based on maneuver mode identification is proposed. The proposed prediction method is designed to improve the prediction accuracy by combining the target dynamic model with the flight data. Finally, the maneuver trajectory data set is established to train and test the method. For the test data set, when the observation time for the target is 200 s and the prediction time is 150 s, with a fast prediction speed, our method’s average error of spatial distance (AESD) is less than 2.9 km, and the maximum error of spatial distance (MESD) is less than 6.9 km. The result is better than other compared mainstream methods. And it is also proved valid with some observational error.

Anti-Interception Guidance for Hypersonic Glide Vehicle: A Deep Reinforcement Learning Approach

Anti-interception guidance can enhance a hypersonic glide vehicle (HGV) compard to multiple interceptors. In general, anti-interception guidance for aircraft can be divided into procedural guidance, fly-around guidance and active evading guidance. However, these guidance methods cannot be applied to an HGV’s unknown real-time process due to limited intelligence information or on-board computing abilities. In this paper, an anti-interception guidance approach based on deep reinforcement learning (DRL) is proposed. First, the penetration process is conceptualized as a generalized three-body adversarial optimal (GTAO) problem. The problem is then modelled as a Markov decision process (MDP), and a DRL scheme consisting of an actor-critic architecture is designed to solve this. Reusing the same sample batch during training results in fewer serious estimation errors in the critic network (CN), which provides better gradients to the immature actor network (AN). We propose a new mechanismcalled repetitive batch training (RBT). In addition, the training data and test results confirm that the RBT can improve the traditional DDPG-based-methodes.

Entry Guidance Based on Analytical Trajectory Solutions

Analytical entry guidance is designed to control the atmospheric gliding of a hypersonic glider. The guidance uses only approximate analytical solutions for a 3-D hypersonic gliding trajectory to fast plan reference trajectory onboard and thus has an extremely high computational efficiency. To reduce the amount of calculation, the adopted analytical solutions are improved first such that their coefficients depend only on generalized flight states. As the coupling of longitudinal and lateral dynamical equations is fully considered, the analytical solutions are of high accuracy but complicated, which consist of zeroth-and first-order solutions. To resolve the resulting difficulty of trajectory planning, an efficient scheme is proposed. In the first guidance cycle of steady glide, the scheme uses only the zeroth-order solutions to plan a trajectory rapidly. In the subsequent guidance cycles, the scheme first uses some profiles obtained from the previously planned trajectory to specify part of the coefficients of the zeroth-and first-order solutions in order to offset the impact of the equations coupling and then utilizes the complete solutions to update the trajectory by proposing a method based on a virtual endpoint of glide and virtual terminal crossrange, which can achieve a very small terminal heading error. The superiority of the guidance is demonstrated by conducting Monte-Carlo simulations.

Hypersonic weapons and nuclear deterrence

This study explores the relationships between hypersonic weapons and nuclear deterrence. This relationship is fraught with uncertainty because the velocity of innovation in hypersonics is difficult to forecast. Nevertheless, major nuclear powers are developing hypersonic weapons, including some that can be deployed on intercontinental launchers. Hypersonic glide vehicles or cruise missiles could threaten first strike stability by reducing the time for responsive decision making in the face of perceived threats, or by evading antimissile defenses otherwise competent to deflect attacks. Attacks on space based assets and cyberattacks, combined with hypersonic missiles, could pose unacceptable risks to assured retaliation based on an assumed number of survivable launch platforms. On the other hand, analysis suggests that, in the case of the United States and Russia, going forward, strategic nuclear deterrents with currently projected modernization plans should suffice to maintain deterrence and first strike stability, barring unforeseen developments in breakthrough technologies.

Joint State and Parameter Estimation for Hypersonic Glide Vehicles Based on Moving Horizon Estimation via Carleman Linearization

Aimed at joint state and parameter estimation problems in hypersonic glide vehicle defense, a novel moving horizon estimation algorithm via Carleman linearization is developed in this paper. First, the maneuver characteristic parameters that reflect the target maneuver law are extended into the state vector, and a dynamic tracking model applicable to various hypersonic glide vehicles is constructed. To improve the estimation accuracy, constraints such as path and parameter change amplitude constraints in flight are taken into account, and the estimation problem is transformed into a nonlinear constrained optimal estimation problem. Then, to solve the problem of high time cost for solving a nonlinear constrained optimal estimation problem, in the framework of moving horizon estimation, nonlinear constrained optimization problems are transformed into bilinear constrained optimization problems by linearizing the nonlinear system via Carleman linearization. For ensuring the consistency of the linearized system with the original nonlinear system, the linearized model is continuously updated as the window slides forward. Moreover, a CKF-based arrival cost update algorithm is also provided to improve the estimation accuracy. Simulation results demonstrate that the proposed joint state and parameter estimation algorithm greatly improves the estimation accuracy while reducing the time cost significantly.

A search method for a hypersonic gliding vehicle based on early warning information guidance

The high speed and high manoeuvrability of a hypersonic gliding vehicle (HGV) pose a severe challenge to the existing early warning detection system. Optimising the search resources of a phased array radar to improve the HGV detecting efficiency has become a practical problem. In this study, a HGV search method based on a hybrid optimisation algorithm is proposed with the early warning information guidance as a priori condition. Firstly, the HGV priority judgement model is established, and the priority quantification equations for height, velocity and distance are designed. Secondly, the search parameters of the radar are optimised with the maximum cumulative detection probability, the shortest average discovery time and the highest priority level as the objective functions, and a radar search model is established. Finally, to overcome the problem that particle swarm optimisation (PSO) is easy to fall into local optimal solution, a hybrid optimisation algorithm based on differential evolution (DE) and PSO is proposed. The performance of the proposed method is verified in two simulation scenarios, and the results show that the proposed method outperforms existing mainstream search methods and can reasonably allocate search resources according to the priority of HGV.