Understanding Sensor Fusion and Tracking, Part 1Understanding Sensor Fusion and Tracking, Part 1
This Matlab video explains the concept of sensor fusion and its importance to applications such as autonomous systems, where data from various sources are merged.
Sensor fusion is taking on greater importance as more applications require the combination of data from different sensor inputs. Self-driving cars, radar tracking systems, and the IoT are some applications. Sensor fusion combines two or more data sources in way that generates a better understanding of the system.
In the first of this two-part video series, Matlab notes that sensors plan a key role by collecting and interpreting data to help systems plan and take appropriate actions. As an autonomous system must interpret data to take the correct action, sensor fusion straddles the path between interpreting and acting upon the data collected.
Sensor fusion can play a key role in the effectiveness of autonomous systems such as self-driving cars.
The Matlab video further explains that for autonomous systems, sensor fusion can increase reliability, improve the quality of the data, estimate unmeasured states, and increase the coverage area.
You can view the video here.
Spencer Chin is a Senior Editor for Design News covering the electronics beat. He has many years of experience covering developments in components, semiconductors, subsystems, power, and other facets of electronics from both a business/supply-chain and technology perspective. He can be reached at [email protected].
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