Active Sensing and Deferred-Decision Trajectory Optimization for Robust Target Identification

2026-06-20

AI Summary

This work addresses the challenge of efficiently identifying true targets while ensuring multi-target reachability in resource-constrained mobile sensing scenarios. The authors propose an Active Sensing–Delayed Decision Trajectory Optimization (AS-DDTO) framework that embeds trajectory-dependent information gain into the planning objective, steering shared trajectory segments toward regions conducive to early target discrimination. The approach integrates Bayesian inference, conformal prediction, and a distance-dependent perception model, and provides guarantees on recursive feasibility, belief concentration, and fixed-time coverage. Numerical experiments demonstrate that under distance-dependent perception uncertainty and limited sensing budgets, the proposed method significantly outperforms standard DDTO in target identification performance.

Abstract

We study trajectory optimization in mobile sensing systems that must identify which member of a finite candidate set is the true target, while maintaining reachability to all potential candidate targets, under resource constraints. Deferred-Decision Trajectory Optimization (DDTO) addresses this setting by computing trajectories that reach individual targets but remain coincident for as long as possible before separating toward different targets. We propose Active-Sensing DDTO (AS-DDTO), which extends DDTO by adding a trajectory-dependent information-acquisition term to the planning objective. The resulting planner maintains reachability to candidate targets while biasing the coincident portion of the trajectories toward regions that enable earlier target identification. The framework supports Bayesian updates and conformal candidate-set updates for distance-dependent sensing. We derive a mixed-integer conic reformulation and provide guarantees on recursive feasibility, belief concentration, and fixed-time coverage for the raw conformal candidate set. Numerical simulations show improved target identification compared with standard DDTO under distance-dependent sensing uncertainty and limited sensing budget.

Problem

• Active Sensing
• Trajectory Optimization
• Target Identification
• Deferred Decision
• Reachability

Innovation

• Active Sensing
• Deferred-Decision Trajectory Optimization
• Trajectory Optimization
• Conformal Prediction
• Information Acquisition