Target Tracking Algorithm Using Two-Stage Cubature Kalman Filter

This study presents the two-stage cubature Kalman filter (TSCKF), which is a sophisticated technique designed to address the issue of variations in system models in real-life scenarios, and utilises nonlinear two-stage transformations to reorganise covariance matrices into a block-diagonal structure, effectively overcoming the limitations of conventional augmented methods. This technique effectively eliminates the need to calculate the cross-covariance between state variables and biases. This leads to a substantial reduction in computational load and facilitates seamless operation of the filter. The TSCKF design is underpinned by a robust theoretical framework, which ensures optimal computational efficiency while also ensuring precise estimations. This work demonstrates the mathematical equivalence between the TSCKF and the standard cubature Kalman filter (CKF) by utilising updated information equivalent transformations, and empirically verifies the equivalence through trajectory tracking experiments conducted on two-wheeled robotic systems subjected to random perturbations, thus affirming the greater accuracy and dependability of the TSCKF in tracking scenarios. Moreover, comparison evaluations offer further proof of the same performance between both methodologies. This study introduces a highly efficient approach in the domain of nonlinear systems and provides a dependable remedy for scenarios where traditional filtering procedures may be inadequate due to deficiencies in the system model.