Improving D-Optimal Sensor Placement for Bearing-Only Localization via Maximum-Entropy Reweighting

TL;DR

This paper introduces a two-layer sensor placement architecture that enhances bearing-only localization by combining particle reweighting with D-optimal placement, leading to reduced localization errors.

Contribution

The novel two-layer approach decouples reweighting from placement, improving localization accuracy across sensing modalities and noise levels.

Findings

Reweighting reduces localization error on average.

Performance improves as sensor-to-source ratio increases.

Benefits persist as the posterior concentrates.

Abstract

In this paper, we present a two-layer architecture for bearing-only sensor placement that improves upon classical D-optimal design. The first layer reweights particles by minimizing Kullback-Leibler divergence from the current distribution subject to a distributional accuracy bound, concentrating mass on regions where the posterior is likely to settle, without reference to the sensor model. The second layer performs D-optimal sensor placement with respect to the reweighted Fisher information matrix, steering sensors toward geometrically informative configurations. Because the two layers are structurally decoupled, the reweighting generalizes across sensing modalities while the placement remains specific to bearing geometry. Systematic experiments on multi-source localization at two noise levels show that this reweighting reduces localization error on average, with the benefit growing as the sensor-to-source ratio increases and as measurements become more informative. The improvement is established in the first few iterations of the sequential procedure and persists as the posterior concentrates.