Infrared measurement and composite tracking algorithm for air-breathing hypersonic vehicles

Zhang, Zhao; Gao, Cai; Jing, Wuxing

doi:10.1117/1.jei.27.2.023023

Cited by 7 publications

(4 citation statements)

References 39 publications

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“…The other two intention networks, A 2 and A 4 , are regressor networks, and the root mean square error (RMSE) is used to measure the accuracy of parameter regression. For specific details of the RMSE, please refer to [9,43]. The specific form is as follows: The test comparison results of the intention network are explained below.…”

Section: Resultsmentioning

confidence: 99%

“…Numerous scholars and laboratories have researched to solve the flight track prediction problem of HGVs. The main research directions include adaptive filtering algorithms [6][7][8][9][10][11] and interacting multiple model algorithms. For actual trajectories that are comparable to the model's trajectory, the single-model adaptive filtering algorithm has a high prediction accuracy [12].…”

Section: Hgv Maneuver Intention Predictionmentioning

confidence: 99%

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

Cheng

Wei

et al. 2022

Mathematics

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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.

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Section: Resultsmentioning

confidence: 99%

Section: Hgv Maneuver Intention Predictionmentioning

confidence: 99%

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

Cheng

Wei

et al. 2022

Mathematics

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“…

is related to the capability of the sensor and the intensity of the target’s infrared radiation, and it is described as follows [ 38 ]:

where

is the aperture diameter of the sensor,

is the detection of the sensor (

is the ambient transmittance,

is the transmittance of the optical system,

is the sensor signal process factor,

is the detection unit area (m 2 ),

is the noise equivalent bandwidth (Hz),

is the minimum signal-to-noise ratio required for the sensor to detect the target, and I is the infrared intensity of the target captured by the sensor. The HGVs researched in this paper enter the near space in an unpowered gliding manner, and the friction between the skin and the air generates infrared radiation [ 39 ]. Infrared radiation intensity can be expressed as follows [ 40 ]:

where A is the infrared radiated area of the target (m 2 );

and

are the lower and upper limits of the infrared band, respectively;

is the spectral emissivity of the target surface;

is the first radiation constant (W·m 2 ); and

is the second radiation constant (m·K).…”

Section: Problem Descriptionmentioning

confidence: 99%

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

Cheng

Wei

Sun

et al. 2023

Sensors

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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.

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“…The generalized pseudo-Bayesian estimator of order n (GPBn) filter and the interacting multiple model (IMM) filter [2] are the typical FSMM methods. Among them, the IMM filter is considered to be the best compromise between complexity and performance, and has been successfully applied in a large number of tracking applications [3,4,5,6,7,8]. In IMM filter, several sub-filters operate in parallel and cooperate with each other through an interacting strategy, leading to improved performance of estimation.…”

Section: Introductionmentioning

confidence: 99%

Robust Interacting Multiple Model Filter Based on Student’s t-Distribution for Heavy-Tailed Measurement Noises

Li¹,

Sun²

2019

Sensors

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In maneuvering target tracking applications, the performance of the traditional interacting multiple model (IMM) filter deteriorates seriously under heavy-tailed measurement noises which are induced by outliers. A robust IMM filter utilizing Student’s t-distribution is proposed to handle the heavy-tailed measurement noises in this paper. The measurement noises are treated as Student’s t-distribution, whose degrees of freedom (dof) and scale matrix are assumed to be governed by gamma and inverse Wishart distributions, respectively. The mixing distributions of the target state, dof, and scale matrix are achieved through the interacting strategy of IMM filter. These mixing distributions are used for the initialization of time prediction. The posterior distributions of the target state, dof, and scale matrix conditioned on each mode are obtained by employing variational Bayesian approach. Then, the target state, dof, and scale matrix parameters are jointly estimated. A variational method is also given to estimate the mode probability. The unscented transform is utilized to solve the nonlinear estimation problem. Simulation results show that the proposed filter improves the estimation accuracy of target state and mode probability over existing filters under heavy-tailed measurement noises.

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Infrared measurement and composite tracking algorithm for air-breathing hypersonic vehicles

Cited by 7 publications

References 39 publications

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

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

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

Robust Interacting Multiple Model Filter Based on Student’s t-Distribution for Heavy-Tailed Measurement Noises

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