English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

There is an increasing demand in real-time imagery applications such as rapid response to disaster rescue and security screening to name a few. The throughput of a radar imaging system is mainly controlled by two parameters; data acquisition time and signal processing time. To minimize the data acquisition time, various methods are being tried and tested by researchers worldwide. Among those is the computational imaging (CI) technique, which relies on using coded apertures to encode the radar back-scattered measurements onto a set of spatio-temporally incoherent radiation patterns. Such a CI-based imaging approach eliminates the requirement for a raster scan and can substantially simplify the physical hardware architecture. Equally important is the processing time needed to retrieve the scene information from the coded back-scattered measurements. In CI, the simplification in the hardware layer comes at the cost of increased complexity in the signal processing layer due to the indirect mapping and compression of the scene information through the spatio-temporally incoherent transfer function of the coded apertures. To address this particular challenge, this paper presents a hardware-based solution for CI signal processing using a Field Programmable Gate Array (an Xilinx Virtex-7 (XC7VX485T) FPGA chip) architecture. In particular, the proposed method consists of calculating the CI sensing matrix using the FPGA chip and storing it on the FPGA platform for image reconstruction. For the adjoint operation, the calculated sensing matrix is applied on the measured back-scattered waves from the target object. We demonstrate that the FPGA based calculation can reach 21.9 times faster speed than conventional brute-force solutions.

HARDWARE ENABLED ACCELERATION OF NEAR-FIELD CODED APERTURE RADAR PHYSICAL MODEL FOR MILLIMETRE-WAVE COMPUTATIONAL IMAGING