Active Sensing for Search and Tracking: A Review

TL;DR

This review paper systematically classifies and discusses the state of the art in active position estimation (APE), highlighting challenges, research directions, and the importance of active sensing for search and tracking tasks.

Contribution

It introduces a formal framework for classifying APE solutions and critically analyzes current methods, promoting robust active sensing development.

Findings

Classification framework for APE solutions

Analysis of pure-exploitative and pure-explorative control laws

Identification of key challenges and future research directions

Abstract

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.