Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.06654

Continuous-Time Estimation-Aware Optimal Control of Mobile Sensors for Target Tracking

Michael States·Sharveshwaran Umashankar·Minsen Yuan·Yue Yu
View original source ↗Read source document ↗Export metadata ↓

Abstract

Target tracking is the problem of estimating the state of a target system using measurements collected (oftentimes) by mobile sensors. Trajectory optimization for mobile sensors must account not only for dynamical and operational constraints, but also for uncertainty in estimating the target system's state. We propose an estimation-aware optimal control method for target tracking that accounts for the nonlinear dynamics of both the mobile sensor and target system, the nonlinear covariance dynamics in extended Kalman filtering, and nonlinear measurement models. Furthermore, it ensures constraints on the mobile sensor are satisfied in continuous time (within numerical precision). In numerical simulations, the proposed method achieves estimation-aware target tracking while satisfying the mobile-sensor constraints in continuous time, even under sparse time discretization.

Explore related subjects

eess.SYcs.SY

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Michael States, Sharveshwaran Umashankar, Minsen Yuan, Yue Yu. 2026-10-05. Continuous-Time Estimation-Aware Optimal Control of Mobile Sensors for Target Tracking. https://arxiv.org/abs/2610.06654

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Neural Networks for AC Optimal Power Flow: Improving Worst-Case Guarantees during Training

The AC Optimal Power Flow (AC-OPF) problem is central to power system operations but remains computationally challenging to solve due to its non-convex, nonlinear nature. Neural networks (NNs) offer rapid surrogates; however, their black-box behavior introduces severe risk of operational constraint violations that compromise grid safety. This paper introduces a verification-informed NN training framework that embeds global worst-case violations directly into the training objective, allowing to train models with rigorous safety guarantees. We evaluate our method across two established NN architectures for AC-OPF predictions, benchmarking both their performance and safety margins. Through rigorous post-hoc verification, we achieve substantial reductions in worst-case violations and, for the first time, successfully verify all operational constraints of large-scale AC-OPF proxies. Experiments on systems ranging from 57 to 793 buses demonstrate scalability, speed, and reliability, bridging the gap between machine learning (ML) acceleration and verified, real-time deployment of AC-OPF solutions, paving the way toward safe data-driven optimal control.

eess.SY↗
arXiv2026

On Port-Hamiltonian Formulation of Hysteretic Energy Storage Elements: The Backlash Case

This paper presents a port-Hamiltonian formulation of backlash-driven hysteretic energy storage elements. First, we revisit the passivity property of backlash-driven storage elements by presenting a family of storage functions. We explicitly derive the corresponding available storage and required supply functions in the sense of Willems, and show the interlacing property of the aforementioned family of storage functions sandwiched between the available storage and required supply functions. Second, using the obtained family of storage functions, we present a port-Hamiltonian formulation of hysteretic inductors. In particular, we show how an appropriate Hamiltonian function is defined using the family of storage functions and how the hysteretic elements can be expressed as a port-Hamiltonian system with feedthrough term, where the feedthrough term represents energy dissipation. Correspondingly, we illustrate its applicability in describing an RC circuit (in parallel and in series) containing a backlash inductor.

eess.SY↗
arXiv2026

Integrated Discovery and State-Aware Servicing for Mobile AUVs With UOWC: Modeling and Performance Analysis

Underwater wireless optical communication (UWOC) is an enabling technology for high-throughput subsea networks, yet its long-term deployment is constrained by the finite energy budget of underwater nodes. To address this challenge, we investigate a mobile system wherein an autonomous underwater vehicle (AUV) performs joint wireless information transfer (WIT) and wireless power transfer (WPT) for a network of randomly distributed sensor nodes. This paper develops \textcolor{blue}{an integrated mission-level framework} that combines stochastic node discovery with state-aware servicing. First, we present an analytical model for node discovery based on a signal-to-noise ratio (SNR) analysis, deriving performance metrics that include the probability distribution of the discovery distance. Second, we introduce \textcolor{blue}{a threshold-based scheduling framework}, termed State-Aware Optimal Point Servicing (SA-OPS), which \textcolor{blue}{selects one of three actions according to the node's real-time energy state: preemptive charging, communication followed by charging, or communication only.} Simulations and multi-criteria decision analysis show that, \textcolor{blue}{under the considered assumptions and parameter ranges}, SA-OPS can improve the tradeoff between AUV energy expenditure and network-wide energy health relative to the adopted baseline strategies. The results also indicate that the selected charging threshold can be approximated by \textcolor{blue}{a simple state-dependent heuristic}, providing a practical guideline for autonomous energy replenishment in underwater networks.

eess.SY↗