Abstract
This paper presents a new adaptive square-root unscented particle filtering algorithm by combining the adaptive filtering and square-root filtering into the unscented particle filter to inhibit the disturbance of kinematic model noise and the instability of filtering data in the process of nonlinear filtering. To prevent particles from degeneracy, the proposed algorithm adaptively adjusts the adaptive factor, which is constructed from predicted residuals, to refrain from the disturbance of abnormal observation and the kinematic model noise. Cholesky factorization is also applied to suppress the negative definiteness of the covariance matrices of the predicted state vector and observation vector. Experiments and comparison analysis were conducted to comprehensively evaluate the performance of the proposed algorithm. The results demonstrate that the proposed algorithm exhibits a strong overall performance for integrated navigation systems.
Highlights
Nonlinear filtering is ubiquitous in many areas such as integrated navigation system, geodetic positioning, automatic control, information fusion and signal processing
This paper presents a new adaptive square-root unscented particle filtering (ASUPF) algorithm by combining adaptive filtering and square-root filtering into UPF
This paper presents a new ASUPF for nonlinear systems by combining adaptive filtering and square-root filtering into UPF
Summary
Nonlinear filtering is ubiquitous in many areas such as integrated navigation system, geodetic positioning, automatic control, information fusion and signal processing. The unscented particle filtering (UPF) is a method to obtain a better importance sampling density function using UT to approximate the posterior probability density function of the state [17,18,19,20] This method still suffers from the particle degeneracy phenomenon if the dynamic system is affected by the disturbances of abnormal observation and kinematic model noise [10,17,20]. In order to prevent particles from degeneracy, this algorithm adaptively determines the equivalent weight function according to robust estimation and adaptively adjusts the adaptive factor constructed from predicted residuals to inhibit the disturbances of abnormal observation and kinematic model noise. This paper presents a new adaptive square-root unscented particle filtering (ASUPF) algorithm by combining adaptive filtering and square-root filtering into UPF This algorithm uses adaptive factors to reasonably control the statistics of observation and kinematic models to inhibit the disturbances of systematic noises, preventing particles from degeneracy. Simulation and experimental analyses as well as comparison analysis with the existing nonlinear filtering algorithms were conducted to comprehensively evaluate the performance of the proposed nonlinear filtering algorithm for dynamic navigation
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