Sensor fusion or multi-sensor fusion has its roots in military intelligence. Today, the technology is being employed in several non-military applications including robotics, automotive, medical, and even entertainment. The recent implementation interest stems from the need for more robust performance, increasing system complexity, and the ability of suppliers to deliver increasing sensor and processing capabilities. However, many variations on the fusion theme may distract engineers whose next design could benefit from sensor fusion. When you go beyond the misconceptions, there are real differences that can improve systems.
CON-FUSION FACTORS
It seems that the term fusion is at least part of the problem. According to one of Merriam-Webster’s online dictionary definitions, fusion is the merging of diverse, distinct, or separate elements into a unified whole. How the merging occurs and what gets merged is still evolving.
In “Sensor Data Fusion for Context-Aware Computing Using Dempster-Shafer Theory,” Huadong Wu of the Robotics Institute at Carnegie Mellon University recognized that “since it [sensor fusion] is an interdisciplinary technology independently growing out of various applications research, its terminology has not reached a universal agreement yet. Generally speaking, the terms sensor fusion, sensor data fusion, multi-sensor data fusion, data fusion, and information fusion have been used in various publications without much discrimination.” The misuse of terminology goes beyond these terms.
One of the pioneers in the area of multi-sensor information fusion is Belur Dasarathy, the owner of Information Fusion Technologies Consultants, a consultancy with internationally recognized expertise in information fusion and related technologies. He also is the originator of the Information Fusion (including Multi-Sensor and Multi-Source Sensor Fusion & Data Fusion) group on Linked-In. More importantly, he is the founding and current editor-in-chief of Information Fusion, published by Elsevier. Dasarathy was among the first who brought the phrase “information fusion” to the fore and accordingly titled the journal he founded in 2000.
As one of the founding directors of the International Society of Information Fusion, he developed a formal definition that has since been quoted at several Web sites: “Information Fusion, in the context of its use by the (Information Fusion) Society, encompasses the theory, techniques and tools conceived and employed for exploiting the synergy in the information acquired from multiple sources (sensor, databases, information gathered by humans, etc.) such that the resulting decision or action is in some sense better than (qualitatively or quantitatively, in terms of accuracy, robustness and etc.) than would be possible if any of these sources were used individually without such synergy exploitation.”
“It used to be called data fusion, sensor fusion, and more, and everybody was using their own definitions,” says Dasarathy. The term information fusion encompasses all the different fusion terms, including sensor fusion.
“Fusion can occur at different levels,” explains Dasarathy. “The inputs can be at one level and the outputs can be at another level.” To avoid ambiguity, he developed what has since come to be known as the Dasarathy model (Fig. 1).
“Fusion involves the input and the output,” says Dasarathy. “You have to characterize a process based on both the input and the output, rather than just one or the other.” In general, the fusion algorithms can range from very simple to very complex.
Information fusion is useful whenever the integration of the information is more than just the sum of the components. “You can oversell information fusion just like any other thing,” cautions Dasarathy. Garbage in still equals garbage out. As part of the information system development, developers have to make sure that they are indeed getting a benefit from the fusion process. “That involves a certain amount of experimentation in the context of the application,” he says.
“You have to show in a qualitative and quantitative way that the decision you are making is going to be better than what you would have done than if you had used any one of the information sources by itself,” says Dasarathy. An approach that Dasarathy developed for determining the value of implementing the fusion process is called an elucidative fusion system.
“By doing fusion I improve the results in some fashion. But you need to know what was the contribution being made by each of the contributing sensors because there is a cost associated with doing this function,” says Dasarathy. For example, in a system with four or five sensors, the contribution of each sensor needs to be determined. If the contribution of one sensor is very small or even negative, it would be counterproductive for it to be part of the fusion processing.
NEW CAPABILITIES, NEW APPLICATIONS
Priyabrata Sinha, principal applications engineer in the High-Performance Microcontroller Division at Microchip Technology, acknowledges that sensor fusion is somewhat of a broad term that encompasses different sensors. “To me, it’s basically a scenario where you combine the data from multiple sensors and combine them in a concurrent sense,” he says.