Knights, Vesna and Prchkovska, Marija and Gajdoš Kljusurić, Jasenka Uncertainty-Aware Multimodal Fusion and Bayesian Decision-Making for DSS. AppliedMath. ISSN 2673-9909
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Abstract
Uncertainty-aware decision-making increasingly relies on multimodal sensing pipelines that must fuse correlated measurements, propagate uncertainty, and trigger reliable control actions. This study develops a unified mathematical framework for multimodal data fusion and Bayesian decision-making under uncertainty. The approach integrates adaptive Covariance Intersection (aCI) for correlation-robust sensor fusion, a Gaussian state–space backbone with Kalman filtering, heteroskedastic Bayesian regression with full posterior sampling via an affine-invariant MCMC sampler, and a Bayesian likelihood-ratio test (LRT) coupled to a risk-sensitive proportional–derivative (PD) control law. Theoretical guarantees are provided by bounding the state covariance under stability conditions, establishing convexity of the aCI weight optimization on the simplex, and deriving a Bayes-risk-optimal decision threshold for the LRT under symmetric Gaussian likelihoods. A proof-of-concept agro-environmental decision-support application is considered, where heterogeneous data streams (IoT soil sensors, meteorological stations, and drone-derived vegetation indices) are fused to generate early-warning alarms for crop stress and to adapt irrigation and fertilization inputs. The proposed pipeline reduces predictive variance and sharpens posterior credible intervals (up to 34% narrower 95% intervals and 44% lower NLL/Brier score under heteroskedastic modeling), while a Bayesian uncertainty-aware controller achieves 14.2% lower water usage and 35.5% fewer false stress alarms compared to a rule-based strategy. The framework is mathematically grounded yet domain-independent, providing a probabilistic pipeline that propagates uncertainty from raw multimodal data to operational control actions, and can be transferred beyond agriculture to robotics, signal processing, and environmental monitoring applications.
| Item Type: | Article |
|---|---|
| Subjects: | Scientific Fields (Frascati) > Natural sciences > Mathematics |
| Divisions: | Faculty of Technology and Technical Sciences |
| Depositing User: | Prof. d-r. Vesna Knights |
| Date Deposited: | 01 Oct 2026 06:16 |
| Last Modified: | 01 Oct 2026 06:16 |
| URI: | https://eprints.uklo.edu.mk/id/eprint/11849 |
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