Space Debris Tracking with the Poisson Labeled Multi-Bernoulli Filter

Cament, Leonardo A.; Adams, Martin; Barrios, Pablo Artaza

doi:10.3390/s21113684

Cited by 8 publications

(2 citation statements)

References 31 publications

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“…Camet et al [22] presented a comprehensive review of various estimation algorithms and proposed a solution to the RSO tracking problem. Their method utilizes simulated optical telescope observations and employs a Bayesian filter-based approach.…”

Section: Introductionmentioning

confidence: 99%

A PCA-SVM Regression Model for LEO Space Debris Orbit Prediction in an Optical Space-Based Space Surveillance Network

Hamidian,

Kosari

2024

IEEE Access

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The primary objective of this study is to investigate the efficacy of Support Vector Machine (SVM) regression method to enhance the accuracy of Low Earth Orbit (LEO) space debris orbit prediction using the historical data. Principal Component Analysis (PCA) is employed to efficiently reduce the dimensionality of dataset's feature space and hence optimize the model's performance. This investigation is motivated by the limitations of conventional orbit prediction methods, which often rely on dynamic models with unknown coefficients of perturbation forces and other relevant characteristics of space debris, leading to errors during the prediction process. On the other hand, while the Collision Avoidance Maneuver (CAM) strategy remains crucial for mitigating the threat posed by such debris, precise knowledge of debris coordinates is essential for effective CAM implementation. However, traditional ground-based optical equipment encounters challenges in observing fastmoving debris within the dynamic LEO environment, including atmospheric interference and limited Field of View (FOV). To address these limitations, the secondary objective of this study involves exploring the potential of an in-orbit optical space surveillance network as a promising solution. The system utilizes optical sensors distributed across multiple spacecraft within the Above the Horizon (ATH) constellation, specifically designed to continuously monitor the most densely populated altitude band in LEO. Simulations under different conditions demonstrate that the proposed scheme successfully complements groundbased equipment and dynamic models for debris tracking, thereby improving orbit prediction accuracy. The results of simulations under different conditions demonstrate that proposed scheme successfully complements ground-based equipment and dynamic models for debris tracking, and improving orbit prediction accuracy.

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Section: Introductionmentioning

confidence: 99%

A PCA-SVM Regression Model for LEO Space Debris Orbit Prediction in an Optical Space-Based Space Surveillance Network

Hamidian,

Kosari

2024

IEEE Access

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“…The second category is labeled filters, which can output the track of each target, mainly including labeled multiple targets multi-Bernoulli filter (LMB) [ 13 ] and

-generalized labeled multiple targets multi-Bernoulli (

-GLMB) filter [ 14 ]. In recent years, the filter based on RFS theory has attracted extensive attention of scholars due to its superior performance [ 15 ]. It has been widely used in radar tracking [ 16 ], video tracking [ 17 ], cell tracking [ 18 ], and other fields.…”

Section: Introductionmentioning

confidence: 99%

Robust Measurement-Driven Cardinality Balance Multi-Target Multi-Bernoulli Filter

Yang

Zhu

et al. 2021

Sensors

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The multi-target tracking filter under the Bayesian framework has strict requirements on the prior information of the target, such as detection probability density, clutter density, and target initial position information. This paper proposes a novel robust measurement-driven cardinality balance multi-target multi-Bernoulli filter (RMD-CBMeMBer) for solving the multiple targets tracking problem when the detection probability density is unknown, the background clutter density is unknown, and the target’s prior position information is lacking. In RMD-CBMeMBer filtering, the target state is first extended, so that the extended target state includes detection probability, kernel state, and indicators of target and clutter. Secondly, the detection probability is modeled as a Beta distribution, and the clutter is modeled as a clutter generator that is independent of each other and obeys the Poisson distribution. Then, the detection probability, kernel state, and clutter density are jointly estimated through filtering. In addition, the correlation function (CF) is proposed for creating new Bernoulli component (BC) by using the measurement information at the previous moment. Numerical experiments have verified that the RMD-CBMeMBer filter can solve the multi-target tracking problem under the condition of unknown target detection probability, unknown background clutter density and inadequate prior position information of the target. It can effectively estimate the target detection probability and the clutter density.

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