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

TL;DR

This paper introduces a novel sensor design framework that synthesizes measurement likelihoods to meet accuracy bounds while minimizing information, applicable to large-scale spatio-temporal systems with uncertain sensor models.

Contribution

It inverts traditional sensor design by synthesizing likelihoods directly from accuracy constraints, accommodating various discrepancy metrics and providing scalable solvers.

Findings

The framework reliably enforces accuracy constraints across different metrics.

The synthesized likelihoods effectively integrate into recursive sensor estimation.

Metric choice influences the distribution and amount of injected information.

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.