Non-myopic GOSPA-driven Gaussian Bernoulli Sensor Management

TL;DR

This paper introduces a non-myopic sensor management algorithm for Bernoulli filtering that optimizes future actions based on GOSPA error, utilizing Monte Carlo tree search for efficient decision-making.

Contribution

It presents a novel non-myopic sensor management method for Bernoulli filtering using GOSPA error minimization and Monte Carlo tree search, with practical implementation details.

Findings

Demonstrates improved target tracking accuracy in simulations.

Provides an efficient algorithm for approximate optimal sensor actions.

Shows the effectiveness of GOSPA-based cost function in sensor management.

Abstract

In this paper, we propose an algorithm for non-myopic sensor management for Bernoulli filtering, i.e., when there may be at most one target present in the scene. The algorithm is based on selecting the action that solves a Bellman-type minimisation problem, whose cost function is the mean square generalised optimal sub-pattern assignment (GOSPA) error, over a future time window. We also propose an implementation of the sensor management algorithm based on an upper bound of the mean square GOSPA error and a Gaussian single-target posterior. Finally, we develop a Monte Carlo tree search algorithm to find an approximate optimal action within a given computational budget. The benefits of the proposed approach are demonstrated via simulations.