An object correlation and maneuver detection approach for space surveillance

Jian, Huang; Hu, Weidong; Xin, Qin; Du, Xiaoyong

doi:10.1088/1674-4527/12/10/003

Cited by 10 publications

(4 citation statements)

References 12 publications

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“…Focusing on the problem of tracklet-to-object matching for maneuvering satellites, various methods have been explored. Huang et al (2012) mathematically formulated the object correlation and maneuver detection problem as a maximum a posterior (MAP) estimation and proposed an approach to solving the MAP estimation. Holzinger et al (2012) demonstrated the utility of control metrics to correlate object observations, and detect and correlate tracklets for unknown objects via hypothesis testing and non-Gaussian boundary condition.…”

Section: Introductionmentioning

confidence: 99%

Tracklet-to-object Matching for Climbing Starlink Satellites through Recursive Orbit Determination and Prediction

Li¹,

Liu²,

Sang³

2022

Res. Astron. Astrophys.

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Concerns for the collision risk involving Starlink satellites have motivated the interest in obtaining their accurate orbit knowledge. However, accurate orbit determination (OD) and prediction (OP) of Starlink satellites confront two main challenges: mismatching or missed matching of sparse tracklets to maneuvering satellites, and unknown or unmodeled orbit maneuvers. How to exactly associate a tracklet to the right satellite is the primary issue, since a maneuvering satellite does not follow the naturally evolving orbit during the maneuvering, while more tracklets are needed for developing an accurate orbit maneuver model. If these two challenges are not well addressed, it may lead to catalog maintenance failure or even loss of objects. This paper proposes a method to correctly match tracklets to the climbing Starlink satellites. It is based on the recursive OD and OP, in which the orbit maneuver is modeled and the thrust is estimated, such that the subsequent OP accuracy guarantees the correct match of tracklets shortly after the OD time. Experiments with climbing Starlink satellites demonstrate that the tracklets within three days of the last TLE (two-line element) are all correctly matched to the right satellites. With the matched tracklets, the thrust accelerations of climbing Starlink satellites can be precisely estimated through an orbit control approach, and the position prediction accuracy over 48 hours is at the level of a few kilometers, providing accurate orbit knowledge for reliable collision warning involving Starlink satellites.

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Section: Introductionmentioning

confidence: 99%

Tracklet-to-object Matching for Climbing Starlink Satellites through Recursive Orbit Determination and Prediction

Li¹,

Liu²,

Sang³

2022

Res. Astron. Astrophys.

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“…As a result, operational satellites are more likely involving space collisions, and the space sustainability is increasingly threatened (Xu & Xiong 2014). It is a demand to not only maintain a catalogue of space objects but also catalogue new objects from their surveillance data as quickly as possible for the sake of space safety management (Huang et al 2012).…”

Section: Introductionmentioning

confidence: 99%

Applying Lambert problem to association of radar-measured orbit tracks of space objects

Liu¹,

Li²,

Chen³

et al. 2021

Res. Astron. Astrophys.

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Thousands of orbit tracks of space objects are collected by a radar each day, and many may be from uncatalogued objects. As such, it is an urgent demand to catalogue the uncatalogued objects, which requires to determine whether two or more un-correlated tracks (UCTs) are from the same object. This paper proposes to apply the Lambert problem to associate two radar-measured orbit tracks of LEO and HEO objects. A novel method of position correction is proposed to correct the secular and short periodic effects caused by the J 2 perturbation, making the Lambert problem applicable to perturbed orbit tracks. After that, an orbit selection method based on the characteristics of residuals solves the multiple-revolution Lambert problem. Extensive experiments with simulated radar measurements of LEO and HEO objects are carried out to assess the performance of the proposed method. It is shown that the semi-major axis can be determined with an error less than 200 m from two tracks separated by 4 days. The true positive (TP) rates for associating two LEO tracks apart by less than 6 days are 94.2%. The TP rate is still at 73.1% even for two tracks apart by 8–9 days. The results demonstrate the strong applicability of the proposed method to associate radar measurements of uncatalogued objects.

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“…Lemmens and Krag [7] also used the TLE data to check any inconsistency of the spacecraft's orbital elements due to maneuvers. Changes in orbital elements have also been explicitly used in [8] to estimate the maneuver time and magnitude of thrust using the Gaussian variation of parameters equations (see [9], pp. 619-637).…”

Section: Introductionmentioning

confidence: 99%

Maneuvering Spacecraft Tracking via State-Dependent Adaptive Estimation

Lee

Hwang

2016

Journal of Guidance, Control, and Dynamics

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In this study, an adaptive estimation algorithm is developed to estimate the state of a spacecraft that performs impulsive maneuvers. The accurate tracking of a maneuvering spacecraft with impulsive burns is a challenging problem since the magnitude and the time of occurrence of impulsive maneuvers are usually unknown a priori. To deal with this problem, an adaptive state estimation algorithm is developed in this study using a bank of extended Kalman filters along with interacting multiple models that account for spacecraft motion with and without impulsive maneuvers. Motivated by the fact that impulsive maneuvers usually occur when certain conditions on the spacecraft state are satisfied, the multiple extended Kalman filters are systematically blended using a state-dependent transition probability. Since the information about the conditions based on which impulsive maneuvers occur is explicitly used in the state-dependent transition probability, the proposed algorithm can predict the impulsive maneuvers more accurately and thus produce more accurate state estimates. The proposed algorithm is demonstrated with two illustrative examples: 1) tracking of a geostationary satellite performing station-keeping maneuvers and 2) tracking of a spacecraft performing orbital transfers. Nomenclature B, G = disturbance and control matrix, respectively inc d = maximum allowed inclination error for geostationary satellite k = discrete-time index m i = mode probability for ith mode m ijj = mixing probability from ith mode to jth mode P = state covariance matrix Q, R = covariance matrix of w and v, respectively q = discrete mode index u = control input vector w, v = process and measurement noise, respectively x, y = state and measurement vector, respectivelŷ x = state estimatê x i , P i = ith mode-conditioned state estimate and covariance matrix, respectively y d = maximum allowed along-track error for geostationary satellite ΔT = sampling time πx = state-dependent jump probability π ij x = state-dependent mode transition probability from ith mode to jth mode

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An object correlation and maneuver detection approach for space surveillance

Cited by 10 publications

References 12 publications

Tracklet-to-object Matching for Climbing Starlink Satellites through Recursive Orbit Determination and Prediction

Tracklet-to-object Matching for Climbing Starlink Satellites through Recursive Orbit Determination and Prediction

Applying Lambert problem to association of radar-measured orbit tracks of space objects

Maneuvering Spacecraft Tracking via State-Dependent Adaptive Estimation

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