Trajectory Estimation of Reentry Vehicles by Use of On-Line Input Estimator

Lee, Sou-Chen; Liu, Chengyu

doi:10.2514/2.4482

Cited by 21 publications

(11 citation statements)

References 10 publications

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“…where Û´Øµ is small white noise, ¾´Ü µ is given in (18), and Ì ENU LOS´Ü µ is defined by (36). To our knowledge this is the most comprehensive dynamic model for MaRV tracking used in the literature.…”

Section: Motion Modelsmentioning

confidence: 99%

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Survey of maneuvering target tracking: II. Ballistic target models

2001

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Section: Motion Modelsmentioning

confidence: 99%

“…where « is a nominal value of the zero-lift drag parameter, discussed before, and Ì ENU LOS´Ü µ Ù ´Üµ Ù ´Üµ Ù ´Üµ (36) is the LOS-to-ENU coordinate transformation matrix, determined by evaluating Ù Ù Ù in terms of the kinematic state vector Ü in the ENU-CS. Detailed information can be found in [11].…”

Section: Models In Los-csmentioning

confidence: 99%

Survey of maneuvering target tracking: II. Ballistic target models

2001

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“…Otherwise, the input is absent. The confidence interval can be obtained from the cumulative normal distribution table for a certain preset confidence level 1-a 10 . Once the input is estimated, the EKF is corrected with the estimated input at the same time.…”

Section: E N O T E T H E Updated State For the Ekf With No Input Atmentioning

confidence: 99%

“…IE has been successfully employed to estimate inputs for solving tracking [1][2][3][4] , inverse heat conduction [5][6][7] and initial leveling problems 8,9 . Lee and Liu proposed a filter associating the EKF with IE to handle model validation problems, and provided an accurate trajectory estimation approach for the RV [10][11][12][13][14] . Their proposed filter has lower estimation errors than the original EKF, and is helpful for this problem.…”

Section: Introductionmentioning

confidence: 99%

Tracking the Warhead Among Objects Separation from the Reentry Vehicle in a Clear Environment

Liu¹

2009

DSJ

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Separating a reentry vehicle into warhead, main body, and debris is a conventional and efficient means of producing a huge decoy and increasing the kinetic energy of the warhead. This procedure causes the radar to track the main body and debris, which radar cross section are large, and ignore the warhead, is the most important part of the reentry vehicle. The warhead is difficult to identify after separation using standard tracking criteria. This study presents a novel tracking algorithm by integrating input estimation and modified probabilistic data association filter to identify warhead among objects separation from the reentry vehicle in a clear environment. The proposed algorithm provides a good tracking capability for the warhead ignoring the radar cross section. Simulation results reveal that the errors between the updated and warhead trajectories are reduced to a small interval in a short time. Therefore, the radar can generate a beam to illuminate the right area and keep tracking the warhead all the time. This algorithm is worthy of further study and application.

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“…Lee and Liu 11 proposed a method based on the EKF with a recursive input estimator, named the adaptive Kalman filter (AKF), for executing online trajectory estimation for a reentry vehicle 11,12 . The robustness and accuracy of this method have been verified by simulation and flight tests.…”

Section: Changmentioning

confidence: 99%

Algorithm of Impact Point Prediction for Intercepting Reentry Vehicles

Liu¹,

Liu²,

Tuan³

2006

DSJ

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Intercepting reentry vehicles is difficult because these move nearly at hypersonic speeds that traditional interceptors cannot match. Counterparallel guidance law was developed for defending a high speed target that guides the interceptor to intercept the target at a 180° aspect angle. When applying the counterparallel guidance law, it is best to predict the impact point before launch. Estimation and prediction of a reentry vehicle path are the first steps in establishing the impact point prediction algorithm. Model validation is a major challenge within the overall trajectory estimation problem. The adaptive Kalman filter, consising of an extended Kalman filter and a recursive input estimator, accurately estimates reentry vehicle trajectory by means of an input estimator which processes the model validation problem. This investigation presents an algorithm of impact point prediction for a reentry vehicle and an interceptor at an optimal intercept altitude based on the adaptive Kalman filter. Numerical simulation using a set of data, generated from a complicated model, verifies the accuracy of the proposed algorithm. The algorithm also performs exceptionally well using a set of flight test data. The presented algorithm is effective in solving the intercept problems.

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Trajectory Estimation of Reentry Vehicles by Use of On-Line Input Estimator

Cited by 21 publications

References 10 publications

Survey of maneuvering target tracking: II. Ballistic target models

Survey of maneuvering target tracking: II. Ballistic target models

Tracking the Warhead Among Objects Separation from the Reentry Vehicle in a Clear Environment

Algorithm of Impact Point Prediction for Intercepting Reentry Vehicles

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