A Review of Nonlinear Filtering Algorithms in Integrated Navigation Systems.

Area of Science:

• Navigation Systems Engineering
• Signal Processing
• Control Theory

Background:

• Nonlinear filtering algorithms are crucial for accurate navigation state estimation.
• Integrated navigation systems require robust and reliable performance.

Purpose of the Study:

• To survey developments in nonlinear filtering algorithms for integrated navigation.
• To analyze the principles, applications, and challenges of various filtering techniques.
• To provide a comparative analysis and future outlook for these algorithms.

Main Methods:

• Review of extended Kalman filtering (EKF).
• Review of unscented Kalman filtering (UKF).
• Review of Cubature Kalman filtering (CKF).
• Review of particle filtering (PF).
• Review of neural network filtering (NNF).
• Analysis of adaptive/robust Kalman filtering (KF).

Main Results:

• Detailed examination of the principles and applications of EKF, UKF, CKF, PF, and NNF.
• Identification of existing problems and limitations within these nonlinear filtering algorithms.
• Comparative analysis highlighting the strengths and weaknesses of each method.

Conclusions:

• Nonlinear filtering algorithms significantly impact navigation system accuracy and robustness.
• Further research is needed to address existing challenges and enhance algorithm performance.
• Comparative analysis provides a foundation for selecting appropriate algorithms for specific navigation applications.