并行分布下采样时空检测前跟踪算法

P. Mazurek
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引用次数: 3

摘要

昏暗物体的跟踪,如小行星或卫星,需要高性能算法。TBD (Track-Before-Detect)算法可以用于这种场景,但对计算能力要求很高。需要处理的轨迹数量庞大是主要问题。GPGPU的应用允许对一小部分图像序列使用优化的下采样时空TBD (ST-TBD)算法进行计算。消息传递接口(Message Passing Interface, MPICH2)用于计算机和gpgpu之间的图像传输和状态空间交换。针对带宽有限的网络,给出了多处理机系统的分析。系统常常受到网络接口带宽的限制,而不是GPGPU的限制。该系统的推导公式允许对gpgpu进行最佳优化设计。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Parallel distributed downsampled spatio-temporal track-before-detect algorithm
Dim objects tracking, like asteroids or satellites, require high performance algorithm. Track-Before-Detect (TBD) algorithms could be applied for such scenario, but the computation power is very demanding. Giant amount of trajectories that could be processed is the main problem. The application of GPGPU allows the computation using optimized Downsampled Spatio-Temporal TBD (ST-TBD) algorithm for small part of image sequences. Message Passing Interface (MPICH2) is applied for the image transmission and state-space exchange between computers and GPGPUs. Analysis of the system with multiple processing computers, for bandwidth limited network, is provided. The system could be limited by the bandwidth of network interface, not by GPGPU, quite often. The derived formula for this system allows the design with best optimization of GPGPUs.
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