TI Autonomous Driving Algorithms (TIADALG) Library User Guide
VLIB_kalmanFilter_3x6_F32 Struct Reference

Structure defining the state of the Kalman filter, 3x6 (3-Dimension Observation x 6-Dimension State Vectors) variant. More...

#include <tiadalg_vl_alg_int.h>

Detailed Description

Structure defining the state of the Kalman filter, 3x6 (3-Dimension Observation x 6-Dimension State Vectors) variant.

Parameters
transitionState transition matrix, A.
errorCovA priori error covariance matrix, P.
predictedErrorCovPredicted error covariance matrix, P1.
stateState of the process, X.
predictedStatePredicted state of the process, X1.
measurementMeasurement matrix (relating state to measurement), H.
processNoiseCovProcess noise covariance matrix, Q
measurementNoiseCovMeasurement noise covariance matrix, R.
kalmanGainKalman gain, K.
temp1Temporary buffer for intermediate results.
temp2Temporary buffer for intermediate results.
temp3Temporary buffer for intermediate results.
scaleFactorScales the matrix M = (H*P1*H' + R) to ensure that its inverse does not overflow 32 bits. The scaling is done by right shifting each element of M by the quantity assigned to scaleFactor. The computed inverse is then scaled back to ensure the correct result based on the identity inv(M) = inv(M/k)/k .

The documentation for this struct was generated from the following file:

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