The Role of Artificial Intelligence in Enhancing Multisensor Data Fusion
Multisensor data fusion is a key technology in modern systems. It unites data obtained by several sensors to obtain a more complete and accurate picture of an environment or situation. Networks that combine multiple sensors can be used to monitor the environment in healthcare, fully autonomous systems, and many others.
Nevertheless, managing and analyzing the large amounts of information such sensors generate is a major concern. This is where Artificial Intelligence (AI) comes in, enabling multisensor data fusion to deliver better performance and accuracy levels.
Multisensor Data Fusion – Overview
Multisensor data fusion is a method of acquiring data and information from more than one sensor and then integrating the acquired information for decision-making rather than relying on single-sensor information.
This includes data collection, processing, merging, and analyzing, involving a range of steps. Some difficulties include inconsistency, redundancy, and noise in the data, which require sophisticated computational techniques. However, the unattended ground sensors (UGS) have curbed some of these difficulties. The UGS has wireless, battery-powered sensors and a camera.
The Role of AI in Data Fusion
AI, especially with Machine Learning (ML) and deep learning algorithms, has a comparative advantage when dealing with large data and examining correlations and patterns that classical computation methods could ignore. Here’s how AI is revolutionizing multisensor data fusion:
Improved data integration
Machine learning algorithms, in particular, can process objects of different nature, such as numbers, pictures, or texts, and ensure they fit into the same model. This makes it easier to fuse data because this sensor might be operating under different conditions from the others.
The decrease in noise and the improvement in the quality of data
AI models can help to remove noise and improve the quality of data obtained from sensors by identifying various forms of noises and inconsistencies that may affect the meaningful data that will be fused.
Real-time processing
Real-time decision-making is necessary for many applications, like self-driving cars and security cameras. Multisensor data is processed almost immediately as AI algorithms focus on speed, and an immediate response is always possible.
Predictive analytics
Historical data, in particular, can be used to show trends and possible consequences, which can help make informed decisions in situations such as disaster preparedness or manufacturing.
Applications and New Directions
Machine intelligence in multisensor data fusion has continued to advance in areas like smart city applications, where it collects data from cameras, weather sensors, and IoT objects to enhance the quality of city life and safety. It also improves efficiency in healthcare analysis by integrating information from wearable gadgets, imaging equipment, and the patient's record.
With enhanced AI algorithms and increasing availability in the future, possibilities of multisensor data fusion will persist. Federated learning edge computing and other new developments offer opportunities to enhance AI solutions' scalability and security. AI has also been integrated into radiation portal monitor systems.
Conclusion
By combining multisensor data fusion with AI, industries can significantly create more intelligent systems that perform better and are more efficient and accurate.
