On Generalized Optimal Hard Decision Fusion

TL;DR

This paper introduces a generalized decision fusion framework for centralized hard decision sensing, unifying existing rules and solving the complex problem efficiently using dynamic programming, with verified numerical results.

Contribution

It formulates a unified GDFP model that encompasses various fusion rules and applies dynamic programming for polynomial-time solutions.

Findings

The GDFP includes many existing fusion rules as special cases.

Dynamic programming effectively solves the GDFP in polynomial time.

Numerical results confirm the proposed method's effectiveness.

Abstract

In this letter, we formulate a generalized decision fusion problem (GDFP) for sensing with centralized hard decision fusion. We show that various new and existing decision fusion rules are special cases of the proposed GDFP. We then relate our problem to the classical $0-1$ Knapsack problem (KP). Consequently, we apply dynamic programming to solve the exponentially complex GDFP in polynomial time. Numerical results are presented to verify the effectiveness of the proposed solution.