An adaptive estimation of forecast error covariance matrices is proposed for Kalman filtering data assim- ilation. A forecast error covariance matrix is initially estimated using an ensemble of perturbation forecasts....An adaptive estimation of forecast error covariance matrices is proposed for Kalman filtering data assim- ilation. A forecast error covariance matrix is initially estimated using an ensemble of perturbation forecasts. This initially estimated matrix is then adjusted with scale parameters that are adaptively estimated by minimizing -2log-likelihood of observed-minus-forecast residuals. The proposed approach could be applied to Kalman filtering data assimilation with imperfect models when the model error statistics are not known. A simple nonlinear model （Burgers＇ equation model） is used to demonstrate the efficacy of the proposed approach.展开更多
针对基于图像的无人机运动跟踪方法存在因图像退化带来的错检和漏检问题,提出一种基于手机和无人机多传感器数据融合的运动目标跟踪方法;将手机IMU(inertial measurement unit,惯性测量单元)数据与无人机的IMU和图像数据作为扩展卡尔曼...针对基于图像的无人机运动跟踪方法存在因图像退化带来的错检和漏检问题,提出一种基于手机和无人机多传感器数据融合的运动目标跟踪方法;将手机IMU(inertial measurement unit,惯性测量单元)数据与无人机的IMU和图像数据作为扩展卡尔曼滤波的输入,其中IMU数据用于滤波器的状态估计,并通过将ORB(oriented FAST and rotated BRIEF)方法得到的运动目标图像坐标作为卡尔曼滤波的测量更新部分,再将扩展卡尔曼滤波之后的数据用于校正状态估计,进一步提高无人机运动目标跟踪的准确性;设计实验通过实测数据集来模拟无人机跟踪场景,验证该方法的可行性。实验表明,采用多传感器数据融合的无人机运动目标跟踪方法能够达到0.67m的定位误差,相比于基于图像的方法的精度高,验证了该方法的有效性。展开更多
基金The study has been continued under the support of the Foundation for Research Science and Technology of New Zealand under contract C01X0401
文摘An adaptive estimation of forecast error covariance matrices is proposed for Kalman filtering data assim- ilation. A forecast error covariance matrix is initially estimated using an ensemble of perturbation forecasts. This initially estimated matrix is then adjusted with scale parameters that are adaptively estimated by minimizing -2log-likelihood of observed-minus-forecast residuals. The proposed approach could be applied to Kalman filtering data assimilation with imperfect models when the model error statistics are not known. A simple nonlinear model （Burgers＇ equation model） is used to demonstrate the efficacy of the proposed approach.
文摘针对基于图像的无人机运动跟踪方法存在因图像退化带来的错检和漏检问题,提出一种基于手机和无人机多传感器数据融合的运动目标跟踪方法;将手机IMU(inertial measurement unit,惯性测量单元)数据与无人机的IMU和图像数据作为扩展卡尔曼滤波的输入,其中IMU数据用于滤波器的状态估计,并通过将ORB(oriented FAST and rotated BRIEF)方法得到的运动目标图像坐标作为卡尔曼滤波的测量更新部分,再将扩展卡尔曼滤波之后的数据用于校正状态估计,进一步提高无人机运动目标跟踪的准确性;设计实验通过实测数据集来模拟无人机跟踪场景,验证该方法的可行性。实验表明,采用多传感器数据融合的无人机运动目标跟踪方法能够达到0.67m的定位误差,相比于基于图像的方法的精度高,验证了该方法的有效性。
