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高分辨率雷达传感器图像识别的计算负担。

Computational burden resulting from image recognition of high resolution radar sensors.

机构信息

National Institute for Aerospace Technology (INTA), Torrejón de Ardoz, 28850 Madrid, Spain.

出版信息

Sensors (Basel). 2013 Apr 22;13(4):5381-402. doi: 10.3390/s130405381.

DOI:10.3390/s130405381
PMID:23609804
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3673143/
Abstract

This paper presents a methodology for high resolution radar image generation and automatic target recognition emphasizing the computational cost involved in the process. In order to obtain focused inverse synthetic aperture radar (ISAR) images certain signal processing algorithms must be applied to the information sensed by the radar. From actual data collected by radar the stages and algorithms needed to obtain ISAR images are revised, including high resolution range profile generation, motion compensation and ISAR formation. Target recognition is achieved by comparing the generated set of actual ISAR images with a database of ISAR images generated by electromagnetic software. High resolution radar image generation and target recognition processes are burdensome and time consuming, so to determine the most suitable implementation platform the analysis of the computational complexity is of great interest. To this end and since target identification must be completed in real time, computational burden of both processes the generation and comparison with a database is explained separately. Conclusions are drawn about implementation platforms and calculation efficiency in order to reduce time consumption in a possible future implementation.

摘要

本文提出了一种高分辨率雷达图像生成和自动目标识别的方法，重点强调了该过程所涉及的计算成本。为了获得聚焦逆合成孔径雷达（ISAR）图像，必须将某些信号处理算法应用于雷达感知到的信息。从雷达实际采集的数据中，对获取 ISAR 图像所需的各个阶段和算法进行了修正，包括高分辨率距离剖面生成、运动补偿和 ISAR 形成。目标识别是通过将生成的实际 ISAR 图像集与电磁软件生成的 ISAR 图像数据库进行比较来实现的。高分辨率雷达图像生成和目标识别过程繁琐且耗时，因此为了确定最合适的实现平台，对计算复杂度的分析具有重要意义。为此，由于目标识别必须实时完成，因此分别解释了生成过程和与数据库进行比较的计算负担。得出了关于实现平台和计算效率的结论，以便在未来的可能实现中减少时间消耗。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5149/3673143/63f5066958f8/sensors-13-05381f14.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5149/3673143/21cae760ba6e/sensors-13-05381f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5149/3673143/3201d5ba17d2/sensors-13-05381f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5149/3673143/52df174f1ff3/sensors-13-05381f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5149/3673143/faa88584907f/sensors-13-05381f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5149/3673143/6577ba042791/sensors-13-05381f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5149/3673143/994adf9f19ae/sensors-13-05381f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5149/3673143/b80830ac1608/sensors-13-05381f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5149/3673143/e4d0f255c255/sensors-13-05381f8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5149/3673143/ad56c005a806/sensors-13-05381f9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5149/3673143/a8d662d3d4c6/sensors-13-05381f10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5149/3673143/06efd1b7ff65/sensors-13-05381f11.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5149/3673143/d3747c4c79a3/sensors-13-05381f12.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5149/3673143/22644b6c3e5d/sensors-13-05381f13.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5149/3673143/63f5066958f8/sensors-13-05381f14.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5149/3673143/21cae760ba6e/sensors-13-05381f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5149/3673143/3201d5ba17d2/sensors-13-05381f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5149/3673143/52df174f1ff3/sensors-13-05381f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5149/3673143/faa88584907f/sensors-13-05381f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5149/3673143/6577ba042791/sensors-13-05381f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5149/3673143/994adf9f19ae/sensors-13-05381f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5149/3673143/b80830ac1608/sensors-13-05381f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5149/3673143/e4d0f255c255/sensors-13-05381f8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5149/3673143/ad56c005a806/sensors-13-05381f9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5149/3673143/a8d662d3d4c6/sensors-13-05381f10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5149/3673143/06efd1b7ff65/sensors-13-05381f11.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5149/3673143/d3747c4c79a3/sensors-13-05381f12.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5149/3673143/22644b6c3e5d/sensors-13-05381f13.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5149/3673143/63f5066958f8/sensors-13-05381f14.jpg

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Fast normalized cross correlation for motion tracking using basis functions.使用基函数进行运动跟踪的快速归一化互相关
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