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TI Autonomous Driving Algorithms (TIADALG) Library User Guide
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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>
Structure defining the state of the Kalman filter, 3x6 (3-Dimension Observation x 6-Dimension State Vectors) variant.
transition | State transition matrix, A. |
errorCov | A priori error covariance matrix, P. |
predictedErrorCov | Predicted error covariance matrix, P1. |
state | State of the process, X. |
predictedState | Predicted state of the process, X1. |
measurement | Measurement matrix (relating state to measurement), H. |
processNoiseCov | Process noise covariance matrix, Q |
measurementNoiseCov | Measurement noise covariance matrix, R. |
kalmanGain | Kalman gain, K. |
temp1 | Temporary buffer for intermediate results. |
temp2 | Temporary buffer for intermediate results. |
temp3 | Temporary buffer for intermediate results. |
scaleFactor | Scales 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 . |
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