A New Nonlinear Filter Method for Ballistic Target Tracking

Abstract

In order to track the ballistic re-entry target, a new kind of ballistic target tracking algorithm, square-root quadrature Kalman filter (SRQKF) algorithm, was proposed. The proposed algorithm is the square-root implementation of the quadrature Kalman filter (QKF). The quadrature Kalman filter is a recursive, nonlinear filtering algorithm developed in the Kalman filtering framework and computes the mean and covariance of all conditional densities using the Gauss-Hermite quadrature rule. The square-root quadrature Kalman filter propagates the mean and the square root of the covariance. It guarantees the symmetry and positive semi-definiteness of the covariance matrix, improved numerical stability and the numerical accuracy, but at the expense of increased computational complexity slightly.

Multisensor Distributed Dynamic Programming Method for Collaborative Warning and Tracking

“…In the nonlinear and non-Gaussian environment, the continuous and stable tracking of ballistic targets is particularly important, and it has been a hot subject of study. In terms of the filtering algorithm, since the motion equation and radar measurement equation of ballistic targets are usually nonlinear, tracking is a nonlinear filtering problem [17] and thus requires high precision and a stable nonlinear filtering method. Currently, the filtering algorithms used for ballistic target tracking mainly include EKF, UF, PF, and related improved filtering algorithms.…”

“…erefore, this paper uses a generalized particle filter, that is, a box particle filter (BPF) for target tracking [14][15][16][17]. Based on the phased characteristics of ballistic target flight, an interacting multimodel box particle filter is designed to solve the problem of full-phase, continuous, and stable tracking of the ballistic target.…”