arXiv subjects

Brandon A. Jones

Publications and source records attributed to Brandon A. Jones.

12 recordsLinked to original sources

arXiv2026

Information-Based Trajectory Planning for Spacecraft-to-Spacecraft Tracking and Navigation in Cislunar Space

We present a trajectory planning method that balances information collection and control effort to improve cislunar spacecraft-to-spacecraft absolute tracking. Expanding use of cislunar space requires alternative navigation and tracking procedures that minimize reliance on ground-based support. Among efforts to address this need, spacecraft-to-spacecraft tracking exploits nonlinear dynamical tracers encoded in a series of relative measurements to infer absolute states of both an observer and a target. The geometry between spacecraft operating under this mode can significantly influence tracking performance. This work considers this geometrical impact by designing observer trajectories that jointly balance control effort and an information-theoretic quantification of the expected spacecraft-to-spacecraft tracking performance. Our methods are designed for multiple low-thrust observation platforms of various sensing modalities and can incorporate multiple space object targets in planning. By leveraging information gain in the optimal control problem, we report almost an order of magnitude improvement in the expected navigation and tracking errors for an optical observer operating in a Distant Retrograde Orbit (DRO). This work demonstrates the feasibility and value of information-optimal low-thrust spacecraft trajectory design for current and upcoming cislunar missions.

cs.RO↗

arXiv2026

Multi-Fidelity Uncertainty Propagation with Model Adaptation to Local Cislunar Dynamics

As the number of missions to cislunar space increases, the population of space objects in this region is expected to grow, making efficient uncertainty propagation essential for space situational awareness (SSA). This is complicated by the cislunar domain's vastness, chaotic dynamical environment, and limited availability of measurements. This paper presents an adaptive multi-fidelity uncertainty propagation method that dynamically adjusts the included perturbing forces based on position in cislunar space, minimizing computation time while maintaining a prescribed modeling accuracy. The proposed adaptive method is then integrated into a multi-target tracking framework to reduce the computational cost of track prediction without sacrificing accuracy, which is important for managing the growing number of objects in cislunar space. The effectiveness of the approach is demonstrated in simulated test cases relevant to upcoming cislunar missions and SSA applications, resulting in a significant reduction in computational cost compared to a non-adaptive approach while achieving equivalent or superior accuracy.

eess.SY↗

arXiv2024

A Bayesian Approach to Low-Thrust Maneuvering Spacecraft Tracking

Bayesian estimation with an explicit transitional prior is required for a tracking algorithm to be embedded in most multi-target tracking frameworks. This paper describes a novel approach capable of tracking maneuvering spacecraft with an explicit transitional prior and in a Bayesian framework, with fewer than two observations passes per day. The algorithm samples thrust profiles according to a multivariate Laplace distribution. It is shown that multivariate Laplace distributions are particularly suited to track maneuvering spacecraft, leading to a log probability function that is almost linear with the thrust. Principles from rare event simulation theory are used to propagate the tails of the distribution. Fast propagation is enabled by multi-fidelity methods. Because of the diffuse transitional prior, a novel k-nearest neighbor-based ensemble Gaussian mixture filter is developed and used.The method allows Bayesian tracking of maneuvering spacecraft for several scenarios with fewer than two measurement passes per day, and with a mismatch between the true and expected thrust magnitude of up to a factor of 200. The validity domain and statistical significance of the method are shown by simulation through several Monte Carlo trials in different scenarios and with different filter settings.

eess.SY↗

arXiv2024

Information-Driven Search and Track of Novel Space Objects

Space surveillance depends on efficiently directing sensor resources to maintain custody of known catalog objects. However, it remains unclear how to best utilize these resources to rapidly search for and track newly detected space objects. Provided a novel measurement, a search set can be instantiated through admissible region constraints to inform follow-up observations. In lacking well-constrained bounds, this set rapidly spreads in the along-track direction, growing much larger than a follow-up sensor's finite field of view. Moreover, the number of novel objects may be uncertain, and follow-up observations are most commonly corrupted by false positives from known catalog objects and missed detections. In this work, we address these challenges through the introduction of a joint sensor control and multi-target tracking approach. The search set associated to a novel measurement is represented by a Cardinalized Probability Hypothesis Density (CPHD), which jointly tracks the state uncertainty associated to a set of objects and a probability mass function for the true target number. In follow-up sensor scans, the information contained in an empty measurement set, and returns from both novel objects and known catalog objects is succinctly captured through this paradigm. To maximize the utility of a follow-up sensor, we introduce an information-driven sensor control approach for steering the instrument. Our methods are tested on two relevant test cases and we provide a comparative analysis with current naive tasking strategies.

cs.RO↗

arXiv2024

Information-Based Trajectory Planning for Autonomous Absolute Tracking in Cislunar Space

The resurgence of lunar operations requires advancements in cislunar navigation and Space Situational Awareness (SSA). Challenges associated to these tasks have created an interest in autonomous planning, navigation, and tracking technologies that operate with little ground-based intervention. This research introduces a trajectory planning tool for a low-thrust mobile observer, aimed at maximizing navigation and tracking performance with satellite-to-satellite relative measurements. We formulate an expression for the information gathered over an observation period based on the mutual information between augmented observer/target states and the associated measurement set collected. We then develop an optimal trajectory design problem for a mobile observer, balancing information gain and control effort, and solve this problem with a Sequential Convex Programming (SCP) approach. The developed methods are demonstrated in scenarios involving spacecraft in the cislunar regime, demonstrating the potential for improved autonomous navigation and tracking.

cs.RO↗

arXiv2023

Direct Exoplanet Detection Using Deep Convolutional Image Reconstruction (ConStruct): A New Algorithm for Post-Processing High-Contrast Images

We present a novel machine-learning approach for detecting faint point sources in high-contrast adaptive optics imaging datasets. The most widely used algorithms for primary subtraction aim to decouple bright stellar speckle noise from planetary signatures by subtracting an approximation of the temporally evolving stellar noise from each frame in an imaging sequence. Our approach aims to improve the stellar noise approximation and increase the planet detection sensitivity by leveraging deep learning in a novel direct imaging post-processing algorithm. We show that a convolutional autoencoder neural network, trained on an extensive reference library of real imaging sequences, accurately reconstructs the stellar speckle noise at the location of a potential planet signal. This tool is used in a post-processing algorithm we call Direct Exoplanet Detection with Convolutional Image Reconstruction, or ConStruct. The reliability and sensitivity of ConStruct are assessed using real Keck/NIRC2 angular differential imaging datasets. Of the 30 unique point sources we examine, ConStruct yields a higher S/N than traditional PCA-based processing for 67$\%$ of the cases and improves the relative contrast by up to a factor of 2.6. This work demonstrates the value and potential of deep learning to take advantage of a diverse reference library of point spread function realizations to improve direct imaging post-processing. ConStruct and its future improvements may be particularly useful as tools for post-processing high-contrast images from the James Webb Space Telescope and extreme adaptive optics instruments, both for the current generation and those being designed for the upcoming 30 meter-class telescopes.

astro-ph.IM↗

arXiv2023

A Gaussian Integral Filter with Multivariate Laplace Process Noise

This paper introduces the concept of the Gaussian integral filter (GIF), the limit of the Gaussian sum filter (GSF) for when the number of mixands tends to infinity. The GIF is obtained via a combination of GSF, quadrature, and interpolation. While it is a very general concept, in this paper the GIF is used to represent multiviariate Laplace (ML) distributions defining the process noise when tracking a maneuvering target. The filter is first applied to a linear three-dimensional toy problem, and then to a maneuvering target tracking problem in Earth orbit. For the more complex maneuvering target tracking problem, the filter requires only 1.4 times the computational resources of an unscented Kalman filter (UKF), while having errors up to 11 times smaller. For the same problem, the UKF slowly diverges.

stat.AP↗

arXiv2022

Astrometric Accelerations as Dynamical Beacons: Discovery and Characterization of HIP 21152 B, the First T-Dwarf Companion in the Hyades

Benchmark brown dwarf companions with well-determined ages and model-independent masses are powerful tools to test substellar evolutionary models and probe the formation of giant planets and brown dwarfs. Here, we report the independent discovery of HIP~21152~B, the first imaged brown dwarf companion in the Hyades, and conduct a comprehensive orbital and atmospheric characterization of the system. HIP~21152 was targeted in an ongoing high-contrast imaging campaign of stars exhibiting proper motion changes between Hipparcos and Gaia, and was also recently identified by Bonavita et al. (2022) and Kuzuhara et al. (2022). Our Keck/NIRC2 and SCExAO/CHARIS imaging of HIP~21152 revealed a comoving companion at a separation of $0.37^{\prime\prime}$ (16 au). We perform a joint orbit fit of all available relative astrometry and radial velocities together with the Hipparcos-Gaia proper motions, yielding a dynamical mass of $24^{+6}_{-4}\,\mathrm{M_{Jup}}$, which is $1{-}2σ$ lower than evolutionary model predictions. Hybrid grids that include the evolution of cloud properties best reproduce the dynamical mass. We also identify a comoving wide-separation ($1837^{\prime\prime}$ or $7.9 \times 10^4 \, \mathrm{au}$) early-L dwarf with an inferred mass near the hydrogen-burning limit. Finally, we analyze the spectra and photometry of HIP~21152~B using the Saumon & Marley (2008) atmospheric models and a suite of retrievals. The best-fit grid-based models have $f_{\mathrm{sed}}=2$, indicating the presence of clouds, $T_{\mathrm{eff}}=1400 \, \mathrm{K}$, and $\log{g}=4.5 \, \mathrm{dex}$. These results are consistent with the object's spectral type of $\mathrm{T0\pm1}$. As the first benchmark brown dwarf companion in the Hyades, HIP~21152~B joins the small but growing number of substellar companions with well-determined ages and dynamical masses.

astro-ph.SR↗

arXiv2022

MAGAL Constellation -- Using a Small Satellite Altimeter Constellation to Monitor Local and Regional Ocean and Inland Water Variations

MAGAL lays the foundations for a future constellation of small satellites carrying radar altimeters aiming to improve the understanding of ocean circulation variability at local, regional, and global scales. All necessary tools will be developed, including a new small, low-power altimeter payload and a miniaturized satellite platform, grounded on the Space 4.0 industry, to be manufactured inseries, minimizing production, operational and launch costs. To implement a collaborative constellation, and better tackle the gaps of large radar altimeter programmes, MAGAL will use a Data Analysis Centre, based on cloud services, for storage and process of data, based on known and improved algorithms, including overlay of layers from multiple sources (e.g. meteorology and opensource data). As a constellation of six satellites, MAGAL increases the density of sea surface topography measurements, enabling more data for altimetry products, when used in synergy with other missions, in coastal areas and over mesoscale features. This results in scientific and commercial information aggregated into a single platform, displayed in various graphical interfaces, allowing overlaid correlations. MAGAL is aligned with the insights from the EU agenda for sustainable development, adding value, alongside the underlying technology development, bringing together the sea's economy and its sustainable growth.

physics.ao-ph↗

arXiv2018

Stochastic Expansions Including Random Inputs on the Unit Circle

Stochastic expansion-based methods of uncertainty quantification, such as polynomial chaos and separated representations, require basis functions orthogonal with respect to the density of random inputs. Many modern engineering problems employ stochastic circular quantities, which are defined on the unit circle in the complex plane and characterized by probability density functions on this periodic domain. Hence, stochastic expansions with circular data require corresponding orthogonal polynomials on the unit circle to allow for their use in uncertainty quantification. Rogers-Szego polynomials enable uncertainty quantification for random inputs described by the wrapped normal density. For the general case, this paper presents a framework for numerically generating orthogonal polynomials as a function of the distribution's characteristic function and demonstrates their use with the von Mises density. The resulting stochastic expansions allow for estimating statistics describing the posterior density using the expansion coefficients. Results demonstrate the exponential convergence of these stochastic expansions and apply the proposed methods to propagating orbit-state uncertainty with equinoctial elements. The astrodynamics application of the theory improves robustness and accuracy when compared to approximating angular quantities as variables on the real line.

stat.CO↗

arXiv2017

A Generalized Labeled Multi-Bernoulli Filter with Object Spawning

Previous labeled random finite set filter developments use a motion model that only accounts for survival and birth. While such a model provides the means for a multi-object tracking filter such as the Generalized Labeled Multi-Bernoulli (GLMB) filter to capture object births and deaths in a wide variety of applications, it lacks the capability to capture spawned tracks and their lineages. In this paper, we propose a new GLMB based filter that formally incorporates spawning, in addition to birth. This formulation enables the joint estimation of a spawned object's state and information regarding its lineage. Simulations results demonstrate the efficacy of the proposed formulation.

stat.CO↗

arXiv2015

Spawning Models for the CPHD Filter

In its classical form, the Cardinalized Probability Hypothesis Density (CPHD) filter does not model the appearance of new targets through spawning, yet there are applications for which spawning models more appropriately account for newborn objects when compared to spontaneous birth models. In this paper, we propose a principled derivation of the CPHD filter with spawning from the Finite Set Statistics framework. A Gaussian Mixture implementation of the CPHD filter with spawning is then presented, illustrated with three applicable spawning models on a simulated scenario involving two parent targets spawning a total of five objects. Results show that filter implementations with spawn models provide more accurate results when compared to a birth model implementation.

stat.ME↗