基于 SNVA 的机动目标状态估计

摘要

By using the difference between the location forecast estimation and the corrected location estimation,the acceleration variance was adjusted adaptively.A maneuvering target state estimation method based on state noise variance adaptive(SNVA)was proposed.The SNVA was used to adaptively adjust the system noise variance in target state estimation system,which can improve the current statistical model.The extended Kalman filter algorithm was used to estimate the target state.Simulation results show that the extended Kalman filter algorithm based on SNVA can estimate the target state accurately.The absolute estimation error of velocity is less than 0.1 m/s,and the absolute estimation error of acceleration is less than 0.1 m/s2 .The target state can be accurately estimated by the proposed method.%利用位置预测估计值与位置滤波估计值之间的偏差进行加速度方差自适应调节,提出一种基于状态噪声方差自适应(SNVA)的机动目标状态估计方法。采用 SNVA 对目标加速度噪声方差进行自适应调整,实现了对当前统计模型的改进；利用扩展卡尔曼滤波算法对目标状态进行估计。仿真结果表明,基于 SNVA 的扩展卡尔曼滤波算法对机动目标速度估计的绝对误差小于0.1 m/s,加速度估计的绝对误差小于0.1 m/s2,能够对机动目标的状态进行准确的估计。