Improved Robust High-Degree Cubature Kalman Filter Based on Novel Cubature Formula and Maximum Correntropy Criterion with Application to Surface Target Tracking

Abstract

Robust nonlinear filtering is an important method for tracking maneuvering targets in non-Gaussian noise environments. Although there are many robust filters for nonlinear systems, few of them have ideal performance for mixed Gaussian noise and non-Gaussian noise (such as scattering noise) in practical applications. Therefore, a novel cubature formula and maximum correntropy criterion (MCC)-based robust cubature Kalman filter is proposed. First, the fully symmetric cubature criterion and high-order divided difference are used to construct a new fifth-degree cubature formula using fewer symmetric cubature points. Then, a new cost function is obtained by combining the weighted least-squares method and the MCC loss criterion to deal with the abnormal values of non-Gaussian noise, which enhances the robustness; and statistical linearization methods are used to calculate the approximate result of the measurement process. Thus, the final fifth-degree divided difference–maximum correntropy cubature Kalman filter (DD-MCCKF) framework is constructed. A typical surface-maneuvering target-tracking simulation example is used to verify the tracking accuracy and robustness of the proposed filter. Experimental results indicate that the proposed filter has a higher tracking accuracy and better numerical stability than other common nonlinear filters in non-Gaussian noise environments with fewer cubature points used.

Tracking a Maneuvering Object by Indirect Observations with Random Delays

“…The main statements of the conditionally minimax nonlinear filtering (CMNF) theory are in the works [33,34]. Further, they are formulated briefly, considering the features of the surveillance system under consideration.…”

“…An excellent attempt to systematize the results in this area was made in [28]. To date, many more works have appeared devoted to the research and improvement in such suboptimal filters [29][30][31][32], including those applied to target tracking Drones 2023, 7, 468 2 of 17 tasks of AUVs [33]. However, no fundamentally new ideas have been proposed, and the initially existing shortcomings remain.…”

Robust IMM Filtering Approach with Adaptive Estimation of Measurement Loss Probability for Surface Target Tracking

“…(1) Gaussian noise modeling (1) Ignores the measurement outliers [24] (2) VB inference (2) Incapable of addressing the (3) UMLP adaptive estimation system-model mismatch…”

“…State estimation holds significant importance for the maneuvering target tracking of conventional vessels and surface autonomous vessels [1]. Aiming to estimate the states of hidden dynamic systems from noisy measurements according to proper criteria, state estimation has attracted wide attention in signal processing communities.…”

Special Issue on Smart Control of Ship Propulsion System

“…Wang et al [6] present a novel cubature formula and maximum correntropy criterion (MCC)-based robust cubature Kalman filter. The fifth-order split-difference-maximum entropy cubic Kalman filter (DD-MCCKF) framework is finally constructed by utilizing the fully symmetric cubic criterion and higher-order split-difference.…”