“当前”统计模型自适应算法并非真正意义上的自适应目标跟踪算法，其性能与其中关键参数的选择有着直接的关系。本文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，提出了简化的自协方差最小二乘噪声协方差估计方法，该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”统计模型自适应算法。

Adaptive “current” statistical model algorithm is not the really adaptive target tracking algorithm, the performance of the algorithm depends on the key parameters. In this paper, the maneuvering targets are modeled by the constant acceleration model, and considering the special structure of the process noise covariance matrix, a simplified autocovariance least-squares method is proposed to estimate noise covariances. And this method establishes a relationship between the autocovairnace of the innovation and the unknown covariances, thus, the noise covariance can be estimated by the least-squares method. The simulation results show that, when the maneuvering targets with unit-step acceleration or variable acceleration, the accuracy of the proposed method is better than the adaptive “current” statistical model algorithm.