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Everything about Sensor fusion

Combining of sensor data from disparate sources

Sensor fusion is a process of combining sensor data or data derived from disparate sources so that the resulting information has less uncertainty than would be possible if these sources were used individually. For instance, one could potentially obtain a more accurate location estimate of an indoor object by combining multiple data sources such as video cameras and WiFi localization signals. The term uncertainty reduction in this case can mean more accurate, more complete, or more dependable, or refer to the result of an emerging view, such as stereoscopic vision. Wikipedia →

🧭 Start here: a 6-step reading path

  1. Read the overview: Sensor fusionA 5-minute orientation
  2. Skim a recent paper: Multi-sensor information fusion based on machine learning for real applications in human activity recognition: State-of-the-art and research challengesInfluential work since 2022
  3. Pick up a classic book: Distributed Sensor NetworksThe most-reprinted book on the subject
  4. Read the landmark paper: An introduction to multisensor data fusionCited 2,022 times
  5. Go sideways: Alpha beta filterA closely related topic
  6. Go sideways: Wireless sensor networkA closely related topic

📖 Definitions

No dictionary entry for this exact phrase.

🗂️ The subject at a glance

  1. Examples of sensors
  2. Algorithms
  3. Example calculations
  4. Centralized versus decentralized
  5. Levels
  6. Applications

🏆 Landmark papers (most cited)

🆕 Influential since 2022

📚 Books worth reading

Ranked by number of editions on Open Library, a rough measure of how lasting a book is.

🔗 Related topics