Sensor Fusion
What is Sensor Fusion?
Sensor fusion, also called multi-sensor integration, is the process of merging data from multiple sensors to produce more accurate, reliable, and actionable insights. In Edge AI systems, sensor fusion enhances real-time decision-making by reducing noise, resolving inconsistencies, and improving situational awareness at the edge. It is the technique of integrating inputs from multiple sensors—such as cameras, LiDAR, radar, and IoT devices—to generate a unified, precise understanding of the environment in real time.
Why Is It Used?
Sensor fusion is used to improve data accuracy, reduce uncertainty, and enable intelligent, real-time decision-making in autonomous systems, industrial IoT, and smart edge devices.
How Is It Used?
Data preprocessing: Cleaning and normalizing raw sensor inputs.
Data integration: Combining inputs using algorithms like Kalman filters, Bayesian networks, or neural networks.
Decision-making: Feeding fused data into Edge AI models for predictive analytics, anomaly detection, or automated actions.
Types of Sensor Fusion
Complementary fusion: Merges sensors providing different types of data for a complete picture.
Redundant fusion: Combines multiple sensors capturing similar data to improve accuracy.
Cooperative fusion: Uses multiple sensors to enhance reliability in dynamic environments.
Benefits of Sensor Fusion
Enhanced accuracy and reliability
Reduced false alarms and errors
Faster real-time insights at the edge
Improved performance of autonomous and IoT systems