255 Power versus Performance Trade-Off Study for a Low-SWaP, UAV Mounted Radiometer for Ocean Salinity Applications

Monday, 13 January 2020
Hall B (Boston Convention and Exhibition Center)
Daniel E. Mera Romo, Univ. of Puerto Rico at Mayaguez, Mayaguez, PR; and R. A. Rodriguez Solis and R. Lorenzo

Power consumption is a critical constraint in the design of mobile sensor systems, and in particular for Unmanned Aerial Vehicle (UAV) applications. While high-level optimizations can be used as an option to reduce the power consumption of the processing system, some of these optimizations can adversely affect the system performance. This paper presents a study of power and performance of a low size, weight and power (SWaP) L-band radiometer, exploring two different architectures for processing the radiometric data; the Beaglebone Black (BB) architecture (with an ARM A8 embedded microprocessor) and the hybrid ZYNQ 7000 architecture (ARM 9 embedded microprocessor connected with a Xilinx 7-series FPGA). In both architectures, high level optimizations were applied to our first prototype of a L-band radiometer for ocean salinity measurements on a UAV, previously presented in [1]. The system configuration is shown in Fig 1. Fig. 2 shows the behavior of the power consumption of the two architectures processing radiometric data at 1 MS/s and 600 MHz clock rate; the power consumption of the ZYNQ was 18% higher than the BB (about 2.7 W for the ZYNQ and 2.2 W for the BB), but the execution time of the algorithm was reduced by more than 50% in the best case (about 20 ms for the ZYNQ and 45 ms for the BB) as shown in Fig. 3. This improvement in execution time will allow flexibility in the determination of the integration time for the radiometer and to process more samples for averaging. With the UAV flying at 8 m/s, and a height of 60 m, the antenna footprint will be 60 m. With a flight time 30 min, sampling speed 1 MS/s, integration time τ = 4, radiometric resolution ∆T= 1.7 K,and clock rate of 600 MHz, the number of scanned footprints can be 600 using the ZYNQ and 300 for the BB. The results show that the optimizations where significant in reducing the power consumption in the case of the BB but not in the case of the ZYNQ architecture. In addition, Fig. 4 presents the validation test of our small L-band total power radiometer measuring salinity through controlled experiments in a tank and at the sea at Magueyes Island at17°58'12.5"N 67°02'46.5"W, the physical temperature of water in Magueyes was 32.0 oC. The salinity results were compared with the theoretical model of [2] and the error was between 1% and 8%. The brightness temperature calculated from the radiometer voltage output was Tb = 103.04 K, corresponding to a salinity of 36.8 PSU using the model in [2]. This was compared with the YSI 599501-02 commercial salinometer and the error obtained was 11.8%. This result did not take into consideration the wind nor the sea surface roughness. In the near future, the corrections for the wind effects will be incorporated into the model. A future version of the radiometer will include a receiver in the Ka-band to provide for an estimate of the sea surface roughness.


[1]D. E. Mera-Romo and R. A. Rodríguez-Solís, "Low Power and Miniaturized Back-End Processing System for an L-Band Radiometer based on ARM Embedded Microprocessor," 2019 IEEE Radio and Wireless Symposium (RWS), Orlando, FL, USA, 2019, pp. 1-4. DOI: 10.1109/RWS.2019.8714392.

[2]L. K. and C. Swift, “An improved model for the dielectric constant of sea water at microwave frequencies,” IEEE J. Ocean. Eng., vol. 2, no. 1, pp. 104–111, 1977.

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