{"id":16540,"date":"2016-09-10T14:14:51","date_gmt":"2016-09-10T11:14:51","guid":{"rendered":"http:\/\/hgpu.org\/?p=16540"},"modified":"2016-09-10T14:14:51","modified_gmt":"2016-09-10T11:14:51","slug":"an-implementation-of-real-time-phased-array-radar-fundamental-functions-on-a-dsp-focused-high-performance-embedded-computing-platform","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=16540","title":{"rendered":"An Implementation of Real-Time Phased Array Radar Fundamental Functions on a DSP-Focused, High-Performance, Embedded Computing Platform"},"content":{"rendered":"<p>This paper investigates the feasibility of a backend design for real-time, multiple-channel processing digital phased array system, particularly for high-performance embedded computing platforms constructed of general purpose digital signal processors. First, we obtained the lab-scale backend performance benchmark from simulating beamforming, pulse compression, and Doppler filtering based on a Micro Telecom Computing Architecture (MTCA) chassis using the Serial RapidIO protocol in backplane communication. Next, a field-scale demonstrator of a multifunctional phased array radar is emulated by using the similar configuration. Interestingly, the performance of a barebones design is compared to that of emerging tools that systematically take advantage of parallelism and multicore capabilities, including the Open Computing Language.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>This paper investigates the feasibility of a backend design for real-time, multiple-channel processing digital phased array system, particularly for high-performance embedded computing platforms constructed of general purpose digital signal processors. First, we obtained the lab-scale backend performance benchmark from simulating beamforming, pulse compression, and Doppler filtering based on a Micro Telecom Computing Architecture (MTCA) chassis [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[90,3,41],"tags":[809,841,1793,252,1789],"class_list":["post-16540","post","type-post","status-publish","format-standard","hentry","category-opencl","category-paper","category-signal-processing","tag-dsp","tag-filtering","tag-opencl","tag-openmp","tag-signal-processing"],"views":2382,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/16540","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/users\/351"}],"replies":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=16540"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/16540\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=16540"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=16540"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=16540"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}