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无源测距的扩展卡尔曼滤波

EXTENDED KALMAN FILTER FOR PASSIVE LOCALIZATION

  • 摘要: 本文用扩展卡尔曼滤波对无源测距作后置处理,并结合了线性最小方差滤波来估计目标声源的距离,速度和航向.后置处理器把接续的时间延迟观测转换为目标的轨迹,同时下降了偏度误差和方差.目标运动的基本假设是常速度,运动的机动部分用随机速度扰动作模型.三个计算机模拟实验结果示出了这种方法测距测速测航向的良好性能及适应目标机动的能力,示出了用最小方差滤波改善初始条件估计后性能提高的情况。

     

    Abstract: Extented Kalman Filter (EKP) combined with Linear Minimum Variance method is utilized for post-processing of passive localization. The combination of the two methods reduces bias and variance seriously, and overcomes the shortcoming of poor initial condition causing big estimate error in EKF. A basic assumption concerning source motion is to consider it to-be of constant velocity over the successive observation intervals. The maneuvreing portions are modeled as random velocity perturbation. Adaptive technique is able to track maneuvering target. Computer simulation results show good performance to estimate range, speed, course of target, and the adaptation to target maneuvre.

     

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