Verfahren und System zur Multisensordatenfusion unter Verwendung von Kontrastivem Lernen Basierendem Tiefen Faltungstransformationslernen

Details

Veröffentlichungs-Nr.

EP4804084

Anmeldetag

19. Dezember 2025

Veröffentlichung

9. September 2026

Rechtsraum

EP

Abstract

Multi-sensor fusion combines data from different sensors to improve accuracy and reliability. Existing methods for multi-sensor fusion utilize Deep Neural Networks which need large, labeled datasets and heavy computational resources. Hence, present disclosure provide a method and system for multi-sensor data fusion using contrastive learning based deep convolutional transform learning for limited data. The method collects data from multiple sensors which is processed by Deep Convolutional Transform Learning (DCTL) encoders, each specific to a sensor, to extract sensor-specific features. Further, a DCTL loss is computed for each DCTL encoder, and a contrastive loss is computed between all feature pairs generated by the DCTL encoders. The method jointly optimizes parameters of the sensor-specific DCTL encoders and contrastive loss function to extract fused features effectively, enabling robust multi-sensor fusion with limited data.

Anmelder

Firma

Tata Consultancy Services Limited

Land

🇮🇳 Indien