Robust Kalman Filter Algorithm

In GPS/INS integrated navigation systems, the application of robust filtering algorithms provides significant benefits for studying navigation systems. By conducting comparative analysis between robust filtering and standard Kalman filtering, researchers can better understand the advantages, limitations, and suitable application scenarios of each method. The MATLAB implementation includes key functions for handling system uncertainties and measurement outliers, using robust estimation techniques like M-estimation or Huber's method to improve filter stability. Although implemented in MATLAB, the algorithm demonstrates remarkable effectiveness in navigation applications. During initial alignment phases, the robust filtering algorithm leverages its inherent advantages to enhance system accuracy and robustness against disturbances and modeling errors. The code typically incorporates adaptive covariance tuning and outlier detection mechanisms. Therefore, in-depth investigation of robust filtering principles and their implementation scenarios can provide valuable insights and methodologies for advancing integrated navigation research.