Discrete Kalman Filter Using LabVIEW

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Overview

This VI implements a Kalman filter that estimates the states of a stochastic system (Set up for order 2 but should work for any order)

Description

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 that tend to be more accurate than those based on a single measurement alone, by using Bayesian inference and estimating a joint probability distribution over the variables for each time frame. The Kalman filter has numerous applications in technology. A common application is for guidance, navigation, and control of vehicles, particularly aircraft and spacecraft. Furthermore, the Kalman filter is a widely applied concept in time series analysis used in fields such as processing and econometric . In this example, we implement a Kalman filter that estimates the states of a stochastic system. It is implemented as a single Input/output systems only at present, meaning it sets up for SISO systems only at present.

Requirements

LabVIEW 2012 or compatible

Steps to Implement or Execute Code

1. Provide the coefficients, Noise Variance and Po
2. Manually input A,B,C,D
3. Run the VI

Additional Information or References

VI Block Diagram

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