arXiv subjects

Trevor N. Wolf

Publications and source records attributed to Trevor N. Wolf.

7 recordsLinked to original sources

arXiv2026

Approximate Relative Entropy Constraints for Nonlinear Covariance Steering Under Distribution Ambiguity

Covariance steering provides an efficient framework for designing linear stochastic feedback policies, but its extension to nonlinear systems relies on a Gaussian surrogate obtained through local linearization. Because this surrogate may differ substantially from the true nonlinear state distribution, risk-sensitive quantities such as collision probability and mean-squared error may be inaccurately estimated. This work develops a distributionally robust covariance-steering framework based on the relative entropy, also known as the Kullback-Leibler divergence (KLD), to account for ambiguity in the propagated probability density function. Using a variational representation of exponential integrals, we derive computable upper bounds on risk-sensitive quantities over a KLD ambiguity set. We then formulate an upper bound on the time rate of change of the KLD between the true nonlinear distribution and a Gaussian reference surrogate. Under some assumptions, this bound is controlled by decision variables within a covariance-steering formulation. The resulting constraints are incorporated into a sequential convex programming algorithm to design stochastic guidance policies that keep the true distribution close to its Gaussian surrogate while enforcing bounds on risk-sensitive performance measures. The proposed approach is demonstrated on a challenging nonlinear spacecraft transfer between two near-rectilinear halo orbits.

cs.RO↗

arXiv2026

Relative Entropy-Bounded Ambiguous Chance Constraints for Robust Planning in Nonlinear Systems

We consider defining risk probability in stochastic control problems under distribution ambiguity. Current approaches for chance-constrained control typically assume that the true state distribution is known and Gaussian distributed. These assumptions are not amenable to many real-world engineering applications where system dynamics are nonlinear and only approximately modeled. In this work, we define a distribution ambiguity set and, with a variational expression for exponential integrals, bound the expected risk value under an unknown distribution that resides within a relative entropy distance of a nominal Gaussian reference distribution. Our bound recovers the reference risk value in the zero-divergence limit. A method is presented to determine the relative entropy distance defining the ambiguity set that is a function of the reference covariance evolution and second-order dynamical truncation errors. The resulting contributions provide a framework for handling distributional ambiguity in nonlinear covariance steering problems. A stochastic spacecraft guidance example is presented to demonstrate our contributions.

math.OC↗

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↗

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↗

arXiv2024

JWST/NIRCam 4-5 $μ$m Imaging of the Giant Planet AF Lep b

With a dynamical mass of $3 \, M_\mathrm{Jup}$, the recently discovered giant planet AF Lep b is the lowest-mass imaged planet with a direct mass measurement. Its youth and spectral type near the L/T transition make it a promising target to study the impact of clouds and atmospheric chemistry at low surface gravities. In this work, we present JWST/NIRCam imaging of AF Lep b. Across two epochs, we detect AF Lep b in F444W ($4.4 \, \mathrm{μm}$) with S/N ratios of $9.6$ and $8.7$, respectively. At the planet's separation of $320 \, \mathrm{mas}$ during the observations, the coronagraphic throughput is ${\approx}7\%$, demonstrating that NIRCam's excellent sensitivity persists down to small separations. The F444W photometry of AF Lep b affirms the presence of disequilibrium carbon chemistry and enhanced atmospheric metallicity. These observations also place deep limits on wider-separation planets in the system, ruling out $1.1 \, M_\mathrm{Jup}$ planets beyond $15.6 \, \mathrm{au}$ ($0.58$ arcsec), $1.1 \, M_\mathrm{Sat}$ planets beyond $27 \, \mathrm{au}$ ($1$ arcsec), and $2.8 \, M_\mathrm{Nep}$ planets beyond $67 \, \mathrm{au}$ ($2.5$ arcsec). We also present new Keck/NIRC2 $L'$ imaging of AF Lep b; combining this with the two epochs of F444W photometry and previous Keck $L'$ photometry provides limits on the long-term $3{-}5 \, \mathrm{μm}$ variability of AF Lep b on months-to-years timescales. AF Lep b is the closest-separation planet imaged with JWST to date, demonstrating that planets can be recovered well inside the nominal (50\% throughput) NIRCam coronagraph inner working angle.

astro-ph.EP↗

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↗