A Novel Data Credibility-based Machine Learning Approach for UAV Capability Evaluation

Abstract

Abstract The Unmanned Aerial Vehicle (UAV) has become more and more important in both civil use and military operations. The overall reconnaissance capability of the UAV swarm is often affected by multiple signals. A new approach is proposed by recognizing data credibility (DC) using multiple machine learning (ML) techniques, i.e., a novel DCML approach. There are two major components (and major theoretical contributions) of the proposed approach. The first component is the initial identification of less-credible data using a single ML technique. The second component is the cross-identification of less-credible data using multiple ML techniques based on the initial identification results. A practical case is studied for validating the proposed DRML approach. Case study results show that (1) The proposed approach in this paper demonstrates a proficient ability to identify less credible data, (2) The validation with various machine learning methods proves effective, but the efficacy of the method is not necessarily proportional to the quantity of methods employed, (3) The combination of BPNN and GPR yields the most favorable outcomes.