Layman Description of the Kalman Filter

Simply put, the Kalman Filter is a generic algorithm that is used to estimate system parameters. It can use inaccurate or noisy measurements to estimate the state of that variable or another unobservable variable with greater accuracy.

1. What is Kalman filter in simple terms?
2. What does the Kalman filter do?
3. Is Kalman filter important?
4. Is Kalman filter signal processing?

What is Kalman filter in simple terms?

The Kalman Filter is an efficient optimal estimator (a set of mathematical equations) that provides a recursive computational methodology for estimating the state of a discrete-data controlled process from measurements that are typically noisy, while providing an estimate of the uncertainty of the estimates.

What does the Kalman filter do?

Kalman filters are used to optimally estimate the variables of interests when they can't be measured directly, but an indirect measurement is available. They are also used to find the best estimate of states by combining measurements from various sensors in the presence of noise.

Is Kalman filter important?

The Kalman Filter is one of the most important and common estimation algorithms. The Kalman Filter produces estimates of hidden variables based on inaccurate and uncertain measurements. Also, the Kalman Filter predicts the future system state based on past estimations.

Is Kalman filter signal processing?

The Kalman filtering algorithm is widely applied in signal processing. It is based on the best estimate rule for the estimation of least mean-square error in search of a recursive estimate. It is suitable for real-time processing and computer operation.