It is an iterative mathematical process that uses a set of equations and consecutive data input to quickly estimate the true value, position, velocity, etc of the object being measured when the measured values contain unpredicted or random error, uncertainty or variation.

The below flow chart is working of Kalman Filter

Now let’s move towards understanding the block by block. So first we take interest in the calculation of Kalman Gain.

Kalman Gain:

Kalman Gain may be expressed as a ratio between Error in Estimate to the sum of Error in Estimate and Error in Data Measurement. If the data provided by the sensor in the form of measurement is accurate, then Kalman Gain will attain a higher value and it will more rely on the data provided by the sensor. However, if the error in data measurement is more then the value of Kalman Gain will be small and it will more rely on the Estimates. In the Equation form, it may be expressed as

  • KG = Kalman Gain
  • EEST = Error in Estimate
  • EMEA = Error in Measurement

The Value of Kalman Gain will be between 0 to 1. The higher the value of Kalman Gain, the accurate is the data provided by the sensor and the estimates are more unstable.

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