Maximum Likelihood-Based Measurement Noise Covariance Estimation Using Sequential Quadratic Programming for Cubature Kalman Filter Applied in INS/BDS Integration

<div>Mean attitude errors of the UAV by the CKF, MCSCKF, and MLCKF for the case of non-Gaussian noise.</div>

Mathematical Problems in Engineering

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Figure 16

Figure 16: Maximum Likelihood-Based Measurement Noise Covariance Estimation Using Sequential Quadratic Programming for Cubature Kalman Filter Applied in INS/BDS Integration 

Figure 16 | Maximum Likelihood-Based Measurement Noise Covariance Estimation Using Sequential Quadratic Programming for Cubature Kalman Filter Applied in INS/BDS Integration 