Active Sensing and Deferred-Decision Trajectory Optimization for Robust Target Identification
For mobile sensing systems needing to identify a target from a finite set while maintaining reachability, this method improves identification speed under resource constraints.
This work extends Deferred-Decision Trajectory Optimization (DDTO) by incorporating an information-acquisition term to improve target identification in mobile sensing systems. The proposed AS-DDTO method achieves earlier target identification compared to standard DDTO under distance-dependent sensing uncertainty and limited sensing budget.
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.