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@article{202799,
        author = {Niranjith M and Nikitha S and Prajwal and Kavya B B and Kusuma G S},
        title = {Multi-Modal Object Detection and Classification Using Magnetic and Radar Sensors: A Comprehensive Survey},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {8433-8441},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=202799},
        abstract = {One of the most important technologies for defense, security and rescue operations is through-wall object detection. Although radar-based methods such as UWB (Ultra-Wideband) and FMCW (Frequency Modulated Continuous Wave) are excellent at identifying human presence behind obstacles, they are not capable of classifying objects. On the other hand, ferrous metallic things can be accurately identified by magnetic anomaly detection, but non-metallic or human objects cannot. In order to improve through-wall object identification and classification, this survey thoroughly examines more than eighteen significant scientific contributions investigating the combination of radar and magnetic sensing modalities. Advanced signal processing techniques, machine learning-based classification algorithms, sensor fusion architectures, magnetic anomaly detection systems, and through-wall radar technologies are all methodically examined. The results show that the most viable route toward dependable, real-time, affordable through-wall detection systems appropriate for practical deployment is multi-modal sensor fusion in conjunction with contemporary machine learning methods.},
        keywords = {Through-wall detection, radar sensors, magnetic sensors, sensor fusion, machine learning, FMCW, UWB, magnetic anomaly detection.},
        month = {May},
        }