Basis of the Kalman Filter [pdf](github.com)

  function transpose(a) { return a } // 1x1 matrix eg a single value.
  function invert(a) { return 1/a }

  const qExternalNoiseVariance = 0.1
  const rMeasurementNoiseVariance = 0.1
  const fStateTransition = 1

  let pStateError = 1
  let xCurrentState = rawDataArray[0]
  for (const zMeasurement in rawDataArray) {
    const xPredicted = fStateTransition * xCurrentState
    const pPredicted = fStateTransition * pStateError * transpose(fStateTransition) + qExternalNoiseVariance
    const kKalmanGain = pPredicted * invert(pPredicted + rMeasurementNoiseVariance)

    pStateError = pPredicted - kKalmanGain * pPredicted
    xCurrentState = xPredicted + kKalmanGain * (zMeasurement - xPredicted) // Output!
  }