Data fusion is the process of combining information from multiple sources to produce information that is more accurate, complete, and actionable than could be obtained from individual sources alone. The three levels of data fusion are:
- Low-level data fusion: This level involves integrating raw sensor data from multiple sources without interpreting or significantly modifying the data. The goal is to improve data quality and reliability by reducing noise, improving resolution, and compensating for sensor errors.
Techniques used in low-level data fusion include time synchronization, data alignment, and sensor calibration to ensure consistent and synchronized sensor outputs.
- Mid-level data fusion: At this level, processed data from individual sensors is combined to form higher-level information or features that provide a more complete understanding of the environment or situation. Mid-level data fusion integrates pre-processed sensor data, such as detected targets, object tracks, or situational assessments, to derive more meaningful and actionable insights.
This level often involves tasks such as target identification, trajectory prediction, correlating tracks from different sensors, and contextual analysis to infer relationships and patterns.
- High-Level Data Fusion: This highest level involves integrating interpreted information from multiple sources to form a coherent, complete, and actionable representation of the environment or situation. High-level data fusion combines contextual information, knowledge and reasoning capabilities to support decision-making, situational awareness and mission planning.
It integrates processed data, such as identified threats, situational assessments and mission objectives, to provide commanders or operators with a holistic understanding of the operational environment and facilitate timely and informed decision-making.
Sensor fusion refers to the process of integrating data or information from multiple sensors to improve overall system performance, reliability and accuracy. Sensor fusion levels generally correspond to the data fusion levels described above.
These levels involve progressively higher degrees of processing and integration of sensor outputs to obtain more sophisticated and useful information for various applications, including navigation, surveillance, robotics, and autonomous systems.
Data fusion processes encompass a range of techniques and methodologies used to integrate and analyze data from various sources.
Key processes include:
- Data Alignment and Synchronization: Ensuring that data from different sensors is temporally and spatially aligned to facilitate meaningful integration.
- Feature extraction and representation: identify relevant features or patterns from sensor data that contribute to understanding the environment or detecting specific phenomena.
- Association and correlation: Link data elements across multiple sources to establish relationships and associations, such as matching sensor detections to form coherent tracks.
- Decision Fusion: Integrate and synthesize information from multiple sources to support decision-making processes, such as determining the presence of a target or assessing a threat level.
- Uncertainty management: addressing uncertainties inherent in sensor data through probabilistic modeling, fusion rules and validation techniques to improve the reliability and robustness of the fused information.
These processes collectively enable data fusion systems to transform disparate data streams into actionable intelligence, improving operational efficiency, situational awareness and decision support across various domains and applications