Sensor Design for Accuracy-Bounded Estimation via Maximum-Entropy Likelihood Synthesis

2026-05-11

AI Summary

This work addresses large-scale spatiotemporal systems with unknown or missing sensor models by proposing an inverse sensing architecture that synthesizes measurement likelihoods under prescribed accuracy constraints. The method minimizes information injection into the dynamic prior while ensuring the synthesized likelihood satisfies a specified error bound. Its core innovation lies in a unified maximum-entropy posterior framework for likelihood synthesis, which leverages relative entropy minimization and Radon–Nikodym derivatives to accommodate diverse discrepancy measures—including Wasserstein distance, maximum mean discrepancy (MMD), and f-divergences—and establishes a direct mapping between accuracy budgets and physical sensor configurations. Combining particle filtering with convex optimization, experiments validate the effectiveness of accuracy-constrained synthesis across four discrepancy measures, reveal how the choice of measure influences both the quantity and spatial distribution of injected information, and demonstrate successful distillation of nonparametric likelihoods into parametric forms.

Abstract

Designing the sensing architecture for large-scale spatio-temporal systems is hard when accuracy requirements are specified but sensor models are uncertain or unavailable. Classical design treats sensor placement and estimation sequentially, requiring valid forward models for each sensing modality. This paper inverts the design flow: given an error budget, synthesize the measurement likelihood that enforces it while injecting minimal information beyond the dynamical prior. The likelihood is constructed by constrained optimization: among all posteriors satisfying a prescribed accuracy bound relative to a target, select the one minimizing Kullback-Leibler divergence from the prior. The solution is a maximum-entropy posterior in relative-entropy form, and the induced likelihood is the Radon-Nikodym derivative. The framework accommodates arbitrary discrepancies and is instantiated for Wasserstein distance, maximum mean discrepancy, $f$-divergences, moment constraints, and hybrid metrics. For each, we derive the discrete particle-level problem, analyze its convex or convex-relaxed structure, and present solvers with complexity scaling. A closed-form solution exists for the symmetric exponential-tilt case, and a distillation procedure converts nonparametric likelihood samples into parametric forms. A two-layer sensor design architecture embeds the synthesized likelihood in the recursive predict-update loop, connecting accuracy budgets to physical sensor placement, precision, and configuration. Numerical experiments comparing four metrics on unimodal and multimodal scenarios confirm the accuracy constraints are reliably enforced and reveal how metric choice determines the amount and spatial distribution of injected information.

Problem

• sensor design
• accuracy-bounded estimation
• maximum-entropy likelihood
• spatio-temporal systems
• uncertain sensor models

Innovation

• maximum-entropy likelihood
• accuracy-bounded estimation
• sensor design
• constrained posterior synthesis
• Radon-Nikodym derivative