常加速度模型的简化自协方差最小二乘法

Simplified Autocovariance Least-squares Method for Constant Acceleration Model

以常加速度模型对机动载体进行建模,顾及其状态噪声协方差矩阵满足特定结构,提出简化的自协方差最小二乘噪声协方差估计方法。通过建立新息的相关函数序列与未知噪声协方差矩阵间的函数模型,并结合最小二乘法进行噪声协方差估计。数值仿真结果表明,当载体进行阶跃加速度运动或变加速度运动时,本文所提方法的目标跟踪精度均优于"当前"统计模型和常加速度模型的自适应算法。

The constant acceleration model is used to model the maneuvering targets ,the process noise covariance matrix satisfies a special structure .Therefore , a simplified autocovariance least‐squares method is proposed to estimate noise covari ances .And this method establishes a rel ationship between the autocovariance of the innovation and the unknown covariances , thus , the noise covariance can be estimated by the least‐squares method .The simulation results show the accuracy of the proposed method is better than the adaptive“current” statistical model algorithm and the adaptive constant acceleration model algorithm ,when the maneuvering targets with unit‐step acceleration or variable acceleration .