Retrieval Analysis of Atmospheric Water Vapor for K-Band Ground-Based Hyperspectral Microwave Radiometer

Liu, Dawei; Lv, Changchun; Liu, Kai; Xie, Yan; Miao, Jungang

doi:10.1109/lgrs.2014.2311833

Cited by 9 publications

(1 citation statement)

References 18 publications

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“…The main limitations of ground-based radiometers are mobility and portability, due to their size, weight in the tens of kilograms, and high power consumption (>200 W), like the radiometers built in [1] and [2]. This limitation affects data acquisition in the field, the scope of the field of view, the resolution, and the quality of the data [2].…”

Section: Introductionmentioning

confidence: 99%

A Power and Performance Study of CompactL-Band Total Power Radiometers for UAV Remote Sensing Based in the Processing on ZYNQ and ARM Architectures

Romo¹,

Rodríguez-Solís²,

Reyes³

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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This paper presents a study on the power consumption and performance analysis of a small, portable, and ultra-low power total power L-band radiometer. The study explores two processing architectures: the ZYNQ 7010 and the ARM A8 embedded microprocessor. The processing algorithm based in C++ was tested for different clock frequencies, ADC sampling speeds, and sizes of the ADC buffer. To reduce the power consumption and the algorithm execution time, high-level and system-level optimizations, along with fixed-point Q(16,16) data representation, were applied to the main code running on LINUX Debian V8. In the case with the ZYNQ 7010, the optimizations had no notable impact on reducing power or execution time in comparison with the ARM A8, where significant variations were measured, showing a tradeoff between power consumption and algorithm performance that limits the processing capability and the system flight time. The ZYNQ 7010 runs the algorithm faster, but the power consumption is higher than the ARM A8. Using the fixed-point Q(16,16) implementation reduced the power consumption and the execution time in both architectures.Based on these results, we developed a heuristic methodology to minimize power consumption and increase the performance. Energy consumption savings for the radiometer during 20 minutes of flight was 48%. The size of the radiometer was reduced to 30cm x 30cm x 10cm, with a weight of 1.36 kg, (3 lb) allowing the system be carried by small drones. The results were validated measuring salinity at two locations in Western Puerto Rico.

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Section: Introductionmentioning

confidence: 99%