Model‐switched Gaussian sum cubature Kalman filter for attitude angle‐aided three‐dimensional target tracking

Abstract

Only target kinematic information is used in most conventional tracking systems, such as a radar or a sonar. Target attitude angles, which provide information about future trajectory curvature before radar measurement, can be used to improve tracking accuracy. The aim of this study is to track three-dimensional (3D) target with attitude angles (yaw and pitch) and radar measurement. Target velocity variations in each coordinate under attitude angles are derived after motion analysis under yaw and pitch angles separately. Then tracking models, the state vector of which includes attitude angles, are presented for target tracking. Targeting at non-zero mean characteristics of attitude measurement, and based on analysing Gaussian sum filter (GSF) and cubature Kalman filter (CKF), a Gaussian sum CKF (GSCKF) is presented to improve the filtering ability of non-linear non-Gaussian systems. Meanwhile, tracking models with different attitude components are established according to the changing law during target motion, and manoeuvering attitude angles are estimated through model switch. A comparison of performance with and without the use of attitude angles shows the benefits of attitude angleaided 3D target tracking. Simulation results of GSFs with different sub-filters demonstrate that the performance of the presented GSCKF has improved over conventional GSFs.

Iterated Orthogonal Simplex Cubature Kalman Filter and Its Applications in Target Tracking

“…In order to reduce the computational complexity, the sparse-grid integral rule can be utilized to derive the SGKF, and the number of integral points is increased polynomially [17]. However, the CKF is widely used in engineering [21][22][23][24]. The CKF helps the intractable Gaussian-weighted integral to decompose and is composed of a spherical integral and a radial integral, and a set of cubature points with equal weights are deduced [25].…”

Robust and accurate UWB‐based indoor robot localisation using integrated EKF/EFIR filtering

“…Most frequently, various modifications of the Kalman filter (KF) are used with this aim. For example, fusion of the KF estimates is provided in [17, 18], the extended KF (EKF) estimates in [7, 19], the iterated KF estimates in [20], the unscented KF estimates in [21], and the cubature KF estimates in [22] for both the linear and non‐linear systems. A common flaw of the KF‐based localisation schemes is that the recursive KF algorithm requires the underlying noise to be white Gaussian, the noise statistics to be known exactly, and the model to match perfectly the underlying process.…”

A Novel Fifth-Degree Strong Tracking Cubature Kalman Filter for Two-Dimensional Maneuvering Target Tracking

“…Arasaratnam [12,13] puts forward the cubature Kalman filter (CKF), where the intractable integral in nonlinear Kalman filter is decomposed into the spherical integral and the radial integral, which are approximated using different numerical integration rules. CKF contains a set of cubature points with equal weights; thus the numerical stability is guaranteed [14][15][16]. CKF can be regarded as a special case of UKF [17]; however, CKF gives the rigorous reason for the selection of parameters for the first time.…”