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Active Sensing for Search and Tracking: A Review

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arxiv 2112.02381 v1 pith:RNHYGMAO submitted 2021-12-04 cs.RO cs.SYeess.SY

classification cs.ROcs.SYeess.SY
keywords sensingactiveestimationsearchtaskclassifymainplatforms
verification ladder T0 review T1 audit T2 compute T3 formal
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Active Position Estimation (APE) is the task of localizing one or more targets using one or more sensing platforms. APE is a key task for search and rescue missions, wildlife monitoring, source term estimation, and collaborative mobile robotics. Success in APE depends on the level of cooperation of the sensing platforms, their number, their degrees of freedom and the quality of the information gathered. APE control laws enable active sensing by satisfying either pure-exploitative or pure-explorative criteria. The former minimizes the uncertainty on position estimation; whereas the latter drives the platform closer to its task completion. In this paper, we define the main elements of APE to systematically classify and critically discuss the state of the art in this domain. We also propose a reference framework as a formalism to classify APE-related solutions. Overall, this survey explores the principal challenges and envisages the main research directions in the field of autonomous perception systems for localization tasks. It is also beneficial to promote the development of robust active sensing methods for search and tracking applications.

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  1. Probabilistic Active Goal Recognition

    cs.AI 2025-07 conditional novelty 5.0 of 10

    An observer that actively moves and uses 'not seen' signals as evidence can infer a hidden goal faster than passive recognition, with MCTS planning matching a domain-specific greedy baseline on grid-world tasks.

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