Splitting method for spatio-temporal search efforts planning

TL;DR

This paper introduces a novel stochastic optimization algorithm based on the generalized splitting method for planning spatio-temporal sensor deployment to maximize detection probability of a moving target, avoiding state-space discretization.

Contribution

It presents a new splitting-based optimization approach for sensor deployment in surveillance, handling constraints without state-space discretization.

Findings

Effective in maximizing detection probability

Handles various constraints efficiently

Avoids state-space discretization

Abstract

This article deals with the spatio-temporal sensors deployment in order to maximize detection probability of an intelligent and randomly moving target in an area under surveillance. Our work is based on the rare events simulation framework. More precisely, we derive a novel stochastic optimization algorithm based on the generalized splitting method. This new approach offers promising results without any state-space discretization and can handle various types of constraints.