Strong Tracking Spherical Simplex-Radial Cubature Kalman Filter for Maneuvering Target Tracking

Area of Science:

• Control Systems Engineering
• Signal Processing
• Estimation Theory

Background:

• Conventional spherical simplex-radial cubature Kalman filters (SSRCKFs) struggle with maneuvering target tracking accuracy during abrupt state changes.
• Target maneuvers can lead to filter divergence and reduced estimation precision.

Purpose of the Study:

• To develop a novel algorithm, the strong tracking spherical simplex-radial cubature Kalman filter (STSSRCKF), for enhanced maneuvering target tracking.
• To improve filter robustness and accuracy when dealing with targets exhibiting abrupt state changes.

Main Methods:

• The proposed STSSRCKF integrates the spherical simplex-radial (SSR) rule for higher accuracy with the strong tracking filter (STF) principles.
• A time-varying fading factor is introduced to adjust the predicted state error covariance, enabling online gain matrix modification.

Main Results:

• The STSSRCKF demonstrates superior estimation accuracy compared to the standard cubature Kalman filter (CKF).
• The algorithm exhibits enhanced robustness and improved capability in handling target uncertainties and abrupt maneuvers.

Conclusions:

• The STSSRCKF effectively addresses the limitations of conventional filters in maneuvering target tracking scenarios.
• The combined strengths of STF robustness and SSRCKF accuracy make STSSRCKF a promising solution for complex tracking problems.