A new multi-target tracking algorithm for a large number of orbiting objects

Delande, Emmanuel; Houssineau, Jérémie; Franco, Jose; Frueh, Carolin; Clark, D.; Jah, Moriba

doi:10.1016/j.asr.2019.04.012

Cited by 20 publications

(20 citation statements)

References 16 publications

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“…The equation of satellite motion is assumed to be [ 11 ]:

where

represents perturbation forces produced by different sources, and

represents non-modeled forces.

and

are the position and velocity components, respectively, of the state vector

, i.e.,

.…”

Section: So Kinematic Prediction Modelmentioning

confidence: 99%

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Space Debris Tracking with the Poisson Labeled Multi-Bernoulli Filter

Cament

Adams

Barrios

2021

Sensors

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This paper presents a Bayesian filter based solution to the Space Object (SO) tracking problem using simulated optical telescopic observations. The presented solution utilizes the Probabilistic Admissible Region (PAR) approach, which is an orbital admissible region that adheres to the assumption of independence between newborn targets and surviving SOs. These SOs obey physical energy constraints in terms of orbital semi-major axis length and eccentricity within a range of orbits of interest. In this article, Low Earth Orbit (LEO) SOs are considered. The solution also adopts the Partially Uniform Birth (PUB) intensity, which generates uniformly distributed births in the sensor field of view. The measurement update then generates a particle SO distribution. In this work, a Poisson Labeled Multi-Bernoulli (PLMB) multi-target tracking filter is proposed, using the PUB intensity model for the multi-target birth density, and a PAR for the spatial density to determine the initial orbits of SOs. Experiments are demonstrated using simulated SO trajectories created from real Two-Line Element data, with simulated measurements from twelve telescopes located in observatories, which form part of the Falcon telescope network. Optimal Sub-Pattern Assignment (OSPA) and CLEAR MOT metrics demonstrate encouraging multi-SO tracking results even under very low numbers of observations per SO pass.

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“…The equation of satellite motion is assumed to be [ 11 ]:

where

represents perturbation forces produced by different sources, and

represents non-modeled forces.

and

are the position and velocity components, respectively, of the state vector

, i.e.,

.…”

Section: So Kinematic Prediction Modelmentioning

confidence: 99%

“…Several Bayesian SO tracking algorithms have been developed using the CAR and PAR approaches, including the recent RFS based methods [ 9 , 10 , 11 ]. Jones et al.…”

Section: Introductionmentioning

confidence: 99%

Space Debris Tracking with the Poisson Labeled Multi-Bernoulli Filter

Cament

Adams

Barrios

2021

Sensors

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“…k is substituted to the TLE possibility h tle . However, because of the high accuracy of radar observations, implementing the data update mechanism leads to quick degeneracy among the particles, especially when the particle cloud has spread significantly after a long period without observations (Delande et al, 2017). Our approach is to find a parametrization of the predicted pdf p k|k−1 leading to a more robust data update mechanism unaffected by the scarcity of particles.…”

Section: Data Update Stepmentioning

confidence: 99%

“…Since pdfs representing orbital states can hardly be parametrized in a simple manner in the Cartesian coordinates X, we exploit an alternative space S knamely, the spherical coordinates in the sensor's local frame -in which a Gaussian approximation is more valid. The data update procedure can be summarized as follows (Delande et al, 2017): 1. Transform p k|k−1 in the spherical frame S k : {w…”

Section: Data Update Stepmentioning

confidence: 99%

Physics and human-based information fusion for improved resident space object tracking

Delande

Houssineau

Jah

2018

Advances in Space Research

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Maintaining a catalog of Resident Space Objects (RSOs) can be cast in a typical Bayesian multi-object estimation problem, where the various sources of uncertainty in the problem -the orbital mechanics, the kinematic states of the identified objects, the data sources, etc. -are modeled as random variables with associated probability distributions. In the context of Space Situational Awareness, however, the information available to a space analyst on many uncertain components is scarce, preventing their appropriate modeling with a random variable and thus their exploitation in a RSO tracking algorithm. A typical example are human-based data sources such as Two-Line Elements (TLEs), which are publicly available but lack any statistical description of their accuracy. In this paper, we propose the first exploitation of uncertain variables in a RSO tracking problem, allowing for a representation of the uncertain components reflecting the information available to the space analyst, however scarce, and nothing more. In particular, we show that a human-based data source and a physics-based data source can be embedded in a unified and rigorous Bayesian estimator in order to track a RSO. We illustrate this concept on a scenario where real TLEs queried from the U.S. Strategic Command are fused with realistically simulated radar observations in order to track a Low-Earth Orbit satellite.

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“…This filter can handle an unknown and time varying number of targets in the scene with targets birth, death, missdetections and false alarms, however, it has a high computational complexity. A low-complexity filter called Hypothesized and Independent Stochastic Population (HISP) filter has been derived from the DISP filter under some intuitive approximations and was adapted for space situational awareness in (Delande et al, 2017). This HISP filter has a linear complexity with both the number of hypotheses and the number of observations similar to the PHD filter, however, unlike the PHD filter, it can preserve the distinct tracks for detected targets.…”

Section: Introductionmentioning

confidence: 99%

Online Multi-target Visual Tracking using a HISP Filter

Baisa

2018

Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Application

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We propose a new multi-target visual tracker based on the recently developed Hypothesized and Independent Stochastic Population (HISP) filter. The HISP filter combines advantages of traditional tracking approaches like multiple hypothesis tracking (MHT) and point-process-based approaches like probability hypothesis density (PHD) filter, and has a linear complexity while maintaining track identities. We apply this filter for tracking multiple targets in video sequences acquired under varying environmental conditions and targets density using a tracking-by-detection approach. In addition, we alleviate the problem of two or more targets having identical label taking into account the weight propagated with each confirmed hypothesis. Finally, we carry out extensive experiments on Multiple Object Tracking 2016 (MOT16) benchmark dataset and find out that our tracker significantly outperforms several state-of-the-art trackers in terms of tracking accuracy.

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A new multi-target tracking algorithm for a large number of orbiting objects

Cited by 20 publications

References 16 publications

Space Debris Tracking with the Poisson Labeled Multi-Bernoulli Filter

Space Debris Tracking with the Poisson Labeled Multi-Bernoulli Filter

Physics and human-based information fusion for improved resident space object tracking

Online Multi-target Visual Tracking using a HISP Filter

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