• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE Transactions on Parallel and Distributed Systems >Parallel implementation of the extended square-root covariance filter for tracking applications
【24h】

Parallel implementation of the extended square-root covariance filter for tracking applications

机译：扩展的平方根协方差滤波器的并行实现，用于跟踪应用程序

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Parallel implementations of the extended square-root covariance filter (ESRCF) for tracking applications are developed. The decoupling technique and special properties used in the tracking Kalman filter (KF) are employed to reduce computational requirements and to increase parallelism. The application of the decoupling technique to the ESRCF results in the time and measurement updates of m decoupled (n/m)-dimensional matrices instead of one coupled n-dimensional matrix, where m denotes the tracking dimension and n denotes the number of state elements. The updates of m decoupled matrices are found to require approximately m fewer processing elements and clock cycles than the updates of one coupled matrix. The transformation of the Kalman gain which accounts for the decoupling is found to be straightforward to implement. The sparse nature of the measurement matrix and the sparse, band nature of the transition matrix are explored to simplify matrix multiplications.
机译：开发了用于跟踪应用程序的扩展平方根协方差滤波器（ESRCF）的并行实现。跟踪卡尔曼滤波器（KF）中使用的解耦技术和特殊属性可减少计算需求并提高并行度。将解耦技术应用于ESRCF会导致m个解耦（n / m）维矩阵而不是一个耦合n维矩阵的时间和测量更新，其中m表示跟踪维，n表示状态元素的数量。发现与一个耦合矩阵的更新相比，m个解耦矩阵的更新所需的处理元素和时钟周期大约少m。发现用于解耦的卡尔曼增益的转换很容易实现。探索了测量矩阵的稀疏性质和过渡矩阵的稀疏带性质，以简化矩阵乘法。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号