基于Jerk输入估计的MCS模型及非线性跟踪算法

针对强机动目标跟踪问题,基于当前统计(CS,Current Statistical)模型、改进输入估计 (MIE,Modified Input Estimation)和无迹强跟踪滤波器,提出了一种新的自适应目标跟踪算法.该算法引入Jerk输入估计改进了当前统计模型的状态方程和机动加速度方差调整方法,利用改进的无迹强跟踪滤波器实现了状态协方差、状态噪声协方差和机动频率的联合自适应.在没有加速度先验知识的情况下,能够实时准确跟踪目标连续强机动、匀加速机动和匀速运动状态.仿真实验表明:相比CS模型无迹滤波算法、CS模型无迹强跟踪算法和交互多模型算法,该算法在对目标强机动的适应性、跟踪精度和对突变状态跟踪的收敛性方面都有更好的性能.

MCS model based on Jerk input estimation and nonlinear tracking algorithm

A new tracking algorithm was proposed for high maneuvering target based on current statistical (CS) model, modified input estimation (MIE) and unscented strong tracking filter (USTF). The state equation and adaption method of acceleration variance of the CS model were modified with Jerk input estimation. The state covariance, state noise covariance and maneuvering frequency were made jointly adaptive with the support of improved unscented strong tracking filter. The new algorithm can track continuous high maneuver, low maneuver and constant velocity motion accurately in real time when lacking prior information on acceleration. The simulation shows that the new algorithm has better adaptability, higher accuracy and faster convergence than CS-UKF, CS-USTF and interacting multiple-model (IMM) algorithms.