弹道学报
彈道學報
탄도학보
JOURNAL OF BALLISTICS
2015年
1期
53-58
,共6页
机动目标%当前统计模型%扩展卡尔曼滤波%状态估计
機動目標%噹前統計模型%擴展卡爾曼濾波%狀態估計
궤동목표%당전통계모형%확전잡이만려파%상태고계
maneuering target%current statistical model%extended Kalman filter%state estimation
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。
利用位置預測估計值與位置濾波估計值之間的偏差進行加速度方差自適應調節,提齣一種基于狀態譟聲方差自適應(SNVA)的機動目標狀態估計方法。採用 SNVA 對目標加速度譟聲方差進行自適應調整,實現瞭對噹前統計模型的改進；利用擴展卡爾曼濾波算法對目標狀態進行估計。倣真結果錶明,基于 SNVA 的擴展卡爾曼濾波算法對機動目標速度估計的絕對誤差小于0.1 m/s,加速度估計的絕對誤差小于0.1 m/s2,能夠對機動目標的狀態進行準確的估計。
이용위치예측고계치여위치려파고계치지간적편차진행가속도방차자괄응조절,제출일충기우상태조성방차자괄응(SNVA)적궤동목표상태고계방법。채용 SNVA 대목표가속도조성방차진행자괄응조정,실현료대당전통계모형적개진；이용확전잡이만려파산법대목표상태진행고계。방진결과표명,기우 SNVA 적확전잡이만려파산법대궤동목표속도고계적절대오차소우0.1 m/s,가속도고계적절대오차소우0.1 m/s2,능구대궤동목표적상태진행준학적고계。
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.
