Kalman filtering

What is the definition of Kalman filtering?

Kalman filtering was invented by Rudolf Kalman in 1967 and is a popular technology used in a vast array of applications, including improving navigation system accuracy. In statistics and control theory, Kalman filtering, also known as linear quadratic estimation (LQE), is an algorithm that uses a series of measurements observed over time, containing statistical noise and other inaccuracies, and produces estimates of unknown variables.

Kalman filtering tends to be more accurate than estimations based on a single measurement, by estimating a joint probability distribution over the variables for each timeframe.

Oppose to Kalman filtering, Artificial Neural Network (ANN) and machine learning appears to be more accurate when applied to navigation systems. read “How is AI revolutionising inertial navigation?“

What are the applications for Kalman filtering?

• Autonomous Systems
• Autonomous Agriculture
• Aerospace
• Automotive
• Defence
• Marine
• Subsea
• Surveying

What other terms are related to Kalman filtering?

• Accuracy
• Artificial Neural Network (ANN)
• Navigation 
• LQE
• Precision

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