Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{196464,
        author = {Ms.Kayalvizhi S and Ms.Anisha A and Ms.Aarthi K and DR.PRABAHARAN P},
        title = {MULTI-GAS DETECTION USING SENSOR FUSION TECHNOLOGY},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {4680-4683},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196464},
        abstract = {The accurate identification and quantification of multiple gases in complex environments is critical for industrial safety, environmental monitoring, and medical diagnostics. Traditional single-sensor approaches often suffer from cross- sensitivity, drift, and limited selectivity. This paper presents a novel multi-gas detection system based on sensor fusion technology that integrates an array of heterogeneous gas sensors metal oxide (MOX), electrochemical (EC), and non-dispersive infrared (NDIR)with a multi-stage data fusion architecture. The proposed system employs a hybrid fusion framework comprising a Kalman filter for signal-level denoising, a support vector machine (SVM) for classification, and an artificial neural network (ANN) for concentration estimation. Experimental results for a target gas set including methane (CH4), carbon monoxide (CO), and nitrogen dioxide (NO2) demonstrate that the fusion approach achieves a classification accuracy of 98.4% and a mean absolute error (MAE) of 1.2 ppm for concentration estimation, significantly outperforming individual sensor modalities. The system’s robust- ness against sensor drift and environmental interference is also validated.},
        keywords = {Sensor fusion, multi-gas detection, metal-oxide sensor, electrochemical sensor, NDIR, machine learning, Kalman filter, artificial neural network.},
        month = {April},
        }