Multiple Model Tracking for Hypersonic Gliding Vehicles With Aerodynamic Modeling and Analysis

Abstract

The multiple model tracking methods for hypersonic gliding vehicles (HGVs), which improves the design of tracking model-set is proposed in this paper. Different from the traditional tracking method with dynamic pressure models, the proposed method refines the model-set by modeling and analyzing the specific aerodynamic characteristics of the HGVs. Based on the aerodynamic model of the vehicle, the novel tracking models are established and the multiple model tracking methods are adopted. The uniform modelsets are designed by determining the representative points from the constrained characteristic parameters to ensure the stability of the tracking system. Moreover, the model-sets are then augmented by the real-time updates of the motion estimations to further improve the tracking accuracy. The simulation studies indicate that the proposed tracking method outperforms the method with dynamic pressure models in both position and velocity estimations, the position estimation accuracy is increased by about 23%-53% and the velocity estimation accuracy is increased by about 42%-56% in the given examples. INDEX TERMS Hypersonic gliding vehicle, aerodynamic modeling, multiple model tracking, model-set design.

Aerodynamic Statistics-Based Trajectory Estimation of Hypersonic Maneuvering Target

“…(2) The maximum lift-to-drag ratio of the HTV-2-like HGV is around 4, which is in accordance with the results of the existing literature [27]. (3) In general, the lift-drag ratio of the HGV positively correlates with the angle of attack. (4) The lift-to-drag ratios of HGV are basically independent of the Mach number.…”

“…Generally, trajectory tracking and estimation are fulfilled through a filter, which combines measured data received from the radar and that of the reference model [2]. Accordingly, the reference model has a significant impact on the estimation accuracy of the trajectory [3], [4]. This is especially more pronounced for state variables, which cannot be measured directly.…”

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

“…In this paper, the IMM-UKF algorithm [37] is used to track observed 20, 000 trajectories at the position (0.4 °E, 0 °N, 100). Assuming that the observational error of radar is Gaussian error with ½ð30mÞ 2 , ð0:1 °Þ2 , ð0:1 °Þ2 , the original trajectory data of all trajectories from 400 to 600 s are generated as observation data set and filtering data set.…”

Motion State Recognition and Trajectory Prediction of Hypersonic Glide Vehicle Based on Deep Learning

“…HGV is an aircraft with a speed above Mach 5 and flying in near space [1]. Compared with a conventional ballistic missile, HGV has stronger maneuverability and can strike any target in the world within two hours [2].…”