An SDR-based GNSS-R CubeSat payload: hardware development and optimization of the onboard processing

IEEE GEOSCIENCE AND REMOTE SENSING SOCIETY SECTION Received 16 January 2025, accepted 18 February 2025, date of publication 27 February 2025, date of current version 4 April 2025. Digital Object Identifier 10.1109/ACCESS.2025.3546358 An SDR-Based GNSS-R CubeSat Payload: Hardware Development and Optimization of the Onboard Processing SHAH ZAHID KHAN 1, (Student Member, IEEE), YASIR M. O. ABBAS 1, (Member, IEEE), EDWAR EDWAR 1, (Student Member, IEEE), ABDUL-HALIM JALLAD 1, (Member, IEEE), AND ADRIANO CAMPS 2,3,4, (Fellow, IEEE) 1Department of Electrical and Communication Engineering, College of Engineering, United Arab Emirates University, Al Ain, United Arab Emirates 2CommsSensLab-UPC, Department of Signal Theory and Communications, Universitat Politècnica de Catalunya-BarcelonaTech, 08034 Barcelona, Spain 3Institut d’Estudis Espacials de Catalunya—IEEC, CTE-UPC, 08034 Barcelona, Spain 4ASPIRE Visiting International Professor, College of Engineering, United Arab Emirates University, Al Ain, United Arab Emirates Corresponding author: Abdul-Halim Jallad ([email protected]) This work was supported in part by ASPIRE ViP21-004 grant; and in part by United Arab Emirates University (UAEU). ABSTRACT Recent developments in high-performance Software Defined Radios (SDRs) and their utilization in CubeSat payloads are transforming Earth Observation (EO), including Microwave Radiometers, Global Navigation and Satellite System – Radio Occultations (GNSS-RO), and – Reflectometry (GNSS-R). In recent years, GNSS-R has been increasingly used in land and marine environmental monitoring, with applications expanding to other emerging fields in EO. The so called Delay Doppler Map (DDM) is the primary observable of GNSS-R receivers, providing information on surface properties, i.e. dielectric constant and surface roughness. Efficient on-board processing is essential in CubeSat-based GNSS-R missions, due to the large volume of raw data and the constraints of limited downlink bandwidth. However, limited on-board computational resources present challenges, as DDM generation requires intensive Fast Fourier Transform (FFT) operations. This study presents the design and development of a cost-effective and compact 0.5U GNSS-R CubeSat payload that optimizes the GNSS-R data processing technique by using the auxiliary data from the reference signals, such as Pseudo-Random Noise (PRN) codes, and their Doppler frequencies in order to reduce the search space. This way the payload selectively processes the raw data, significantly reducing the computational load. The payload processing unit is implemented in Analog Devices ADRV9364 with a dual-core ARM Cortex-A9 processor with a Zynq-7000 Field-Programmable Gate Array (FPGA). INDEX TERMS CubeSats, delay Doppler map (DDM), GNSS-R, interferometry, optimization, reflectometry, software defined radios (SDRs). I. INTRODUCTION The miniaturization of electronic components over recent decades has driven significant advancements in CubeSat technology, establishing these small, modular satellites as essential tools in EO. CubeSats’ affordability and expanded access to space have led to widespread adoption in both scientific and commercial missions [1]. Despite their compact The associate editor coordinating the review of this manuscript and approving it for publication was Gerardo di Martino . size, CubeSats provide high scalability, fast development cycles, and modular designs with standard ‘‘units’’ (1U) measuring 10 ×10 ×10 cm3, allowing them to perform diverse tasks effectively, while their lower cost allows to launch constellations that allow high temporal and spatial resolutions. The use of SDRs provides a flexible architecture capable of operating across various frequencies. This makes them a preferred choice for communications and EO missions, including those leveraging GNSS Signals of Opportunity (SoOP). VOLUME 13, 2025 2025 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ 56045 S. Z. Khan et al.: SDR-Based GNSS-R CubeSat Payload: Hardware Development and Optimization FIGURE 1. Payload block diagram. A. GNSS: AN OVERVIEW In GNSS-R, signals from GNSS satellites that are reflected off the Earth’s surface are then received by dedicated instruments. This technique, initially proposed by Hall and Cordey et al. in 1988 [2], and then refined over the years, leverages on these reflections to measure various surface parameters, including soil moisture, sea state, and ice cover [3]. In the so called conventional GNSS-R (cGNSS-R) technique, the reflected signal (SR(t)) is cross-correlated with a locally generated replica of the transmitted GNSS signal (a(t)) across various delays and (τ) Doppler frequency shifts (fd). Mathematically, this process is expressed as [4]: Yc(t0, τ, fd)=1 TcZt0+Tc t0 SR(t)a∗(t−τ)e−j2π(fc+fd)tdt, (1) where: •t0is the start time of the integration interval. •Tcis the integration period, which typically equals to the duration of the pseudo-random signal being used (e.g. 1 ms for GPS C/A code), or a multiple of it, and •fcbeing the carrier frequency. Due to the generally low amplitude of Yc, and low Signalto-Noise Ratio (SNR), incoherent averaging over Niintervals of duration equal to Tcis performed to improve the signal quality [4],[5]: DDM(t0, τ, fd)=1 Ni Ni−1 X n=0 |Yc(t0+n·Tc, τ, fd)|2.(2) The result is the so-called DDM, which is the primary observable in GNSS-R receivers. The DDM represents the power of the reflected signals as a function of both the delay and the Doppler frequency shift, which are related to the locations where the reflections take place offering a detailed view of surface reflection characteristics. B. GNSS-R-BASED CUBESAT MISSIONS AND DATA PROCESSING CHALLENGES Large-scale and commercial GNSS-R missions such as the UK’s Disaster Monitoring Constellation-1 (DMC-1), launched in 2003, served as a pioneering In-orbit Demonstration (IoD) of a GNSS-R instrument [6]. Subsequent missions such as TechDemoSat-1 (TDS-1), developed by Surrey Satellite Technology Limited (SSTL), was launched in 2014 and featured the Space GNSS Receiver Remote Sensing Instrument (SGR-ReSI) [7]. NASA’s Soil Moisture Active Passive (SMAP) mission followed in 2015, utilizing GNSS-R for soil moisture and freeze-thaw state measurements [8]. The Cyclone Global Navigation Satellite System (CYGNSS) mission, launched in 2016, marked a significant milestone as a larger-scale constellation designed to study tropical cyclones [9]. More recently, the BuFeng-1 (BF-1) mission, a twin satellite system launched in 2019, performed observations of the sea and the land surface, including soil moisture [10],[11]. Another notable mission is the Spire Global 3U satellite constellation, which integrates GNSS-R capabilities in some of its satellites [12],[13]. These earlier GNSS-R missions initially relied heavily on ground-based processing, but later incorporated on-board processing strategies, enabled by hardware and software optimization advances. For example, instruments in [7], highlighted leveraging factors such as the geometry of the receiver and the size of the surface scattering area to optimize 56046 VOLUME 13, 2025 S. Z. Khan et al.: SDR-Based GNSS-R CubeSat Payload: Hardware Development and Optimization data handling. CYGNSS underscores the growing emphasis on on-board processing, driven by constraints such as limited downlink bandwidth and faster data delivery [14]. On-board processing becomes particularly critical in smaller CubeSat missions, where size and power limitations require efficient use of computational resources. Furthermore, only a few other GNSS-R instruments have been developed for nanosatellites [15],[16],[17]. In [15] the 3Cat-2 mission, which was 6U CubeSat, developed at Universitat Politécnica de Catalunya (UPC) as an educational project, is discussed. On-board the mission was the PYCARO (P(Y) and C/A Reflectometer), which implemented the conventional and reconstructed code techniques [18]. It aimed to perform ocean altimetry and is currently inactive. In [16], the 3Cat-4 satellite, a 1U CubeSat with a similar mission is discussed, which encountered a solar panel technical failure shortly after launch. In [17] the FMPL-2 instrument flew onboard 3Cat5/A, as part of the FSSCAT mission, ESA S3 winner of the Copernicus Masters’ 2017 Competition. The FMPL-2 is a combined L-band microwave radiometer and GNSS-R instrument. This mission provided soil moisture, sea ice extent and coverage, and sea salinity maps. Moreover, in [5], PRETTY a 3U CubeSat payload utilizing an SDR is presented. This European Space Agency (ESA) mission aims to demonstrate GNSS-R altimetry and sea ice detection applications. The future of GNSS-R-based remote sensing using nanosatellites is highly promising, driven by advancements in miniaturized payloads, enhanced onboard processing algorithms, and the deployment of large-scale satellite constellations. One significant recent development is the Tianmu-1 constellation, which represents a major step forward in using nanosatellites for Earth remote sensing. These developments are expected to greatly enhance the temporal and spatial resolution of GNSS-R data, enabling more precise monitoring of critical environmental parameters such as soil moisture, ocean surface winds, and sea ice extent. Furthermore, the cost-effectiveness, scalability, and rapid deployment of nanosatellites make them an ideal solution for global and continuous Earth monitoring, addressing the limitations of traditional remote sensing platforms. While nanosatellites are instrumental for GNSS-R missions, they come with trade-offs, especially regarding on-board processing and limited downlink capabilities. To address these challenges, GNSS-R receivers can leverage on auxiliary data obtained from the reference signal, such as the PRN codes and the Doppler frequencies of the signals from the satellites in view. This way, the Doppler frequency search range can be reduced to a few kHz. This approach optimizes the DDM generation, reducing the demands for computationally extensive FFT calculations. Following this approach, this paper presents the development of an SDR-based GNSS-R payload for CubeSats in a compact 0.5U form factor. Section II discusses the design of the payload, covering both hardware and software aspects. Section III presents the experimental results and evaluates instrument performance. Section IV provides concluding insights including potential future research directions. II. HARDWARE AND SOFTWARE DESIGN The developed GNSS-R instrument utilizes a Commercial Off-The-Shelf (COTS) FPGA-based SDR integrated with a custom-designed interface board and a Radio Frequency Front-End (RF-FE) module. Figure 1shows the block diagram of the design. Each component of the design is discussed in detail hereafter. A. SDR The SDR utilized is the ADRV9364 from Analog Devices with System-on-Chip (SoC) architecture. It features a compact design, with dimensions of 100 ×66 mm2, and integrates a single transceiver channel for RF operations [19]. The core of the SoC is a high-performance dual-core ARM Cortex-A9 processor integrated with the hardware programmable FPGA (Zynq-7000), running a Linux operating system optimized for embedded systems. This setup facilitates the implementation of the custom-developed Python-based GNSS-R receiver and additional supporting scripts. To function autonomously, the SoC requires a connection to an interface board, which enables debugging, testing, and data streaming. B. INTERFACE BOARD The interface board is a custom-designed PCB that serves as the primary management and control unit for the GNSS-R payload. It provides a physical interface to the SDR, houses the digital components, and integrates essential power, control, and storage electronics. The board includes a serial communication port with a temporarily installed Ethernet port for testing and debugging, which will be removed later in the flight model. Figure 2shows the interface board developed, having the PC104 form factor, with crucial components and functionalities that support payload operations: •Interface: Facilitates the connections between the SDR and the CubeSat’s On-Board Computer (OBC). •Power Supply: Distributes dedicated power to the SDR, digital components, and RF board. •Microcontroller (MCU): Serves as the primary command and control unit. •Storage: Provides shared memory for DDMs, accessible by the MCU, SDR, and the OBC. •External Clock: Supplies precise and stable clocks for the microprocessor and SDR for accurate GPS signal processing. C. RADIO FREQUENCY FRONT-END The RF-FE module consists of amplifiers, filters, and switches, serving as the Signal Condition Unit (SCU) for both the zenith and nadir-looking sides. It is designed with a double-stage low-noise amplifier to achieve the required signal gain for the reflected GNSS signals. It also VOLUME 13, 2025 56047 S. Z. Khan et al.: SDR-Based GNSS-R CubeSat Payload: Hardware Development and Optimization FIGURE 2. Interface board for the GNSS-R payload. incorporates a high dynamics GNSS receiver connected to the reference signal (zenith-looking antenna). In addition to its primary function as an SCU, the unit can switch its output between two orientations – zenith and nadir sides – based on a control signal received from the interface board. Furthermore, each branch includes two internal loads: an Active Cold Load (ACL), a Matched Load (ML), and a temperature sensor for periodic calibration of gain and noise figure drifts [20]. Figure 3presents the RF-FE board, designed to align with the dimensions of the interface board. Figure 4illustrates the RF-FE receiving chain and details FIGURE 3. RF-FE PCB for the GNSS-R payload. FIGURE 4. Overview of the RF-FE receiving chain. the signal routing paths for the zenith and nadir-looking channels. D. ANTENNAS A Right-Hand Circularly Polarized (RHCP) active patch antenna is used on the zenith side. On the nadir side, a four-element Left-Hand Circularly Polarized (LHCP) patch antenna array will be used. The design combines a patch antenna on one side and solar cells on the other side on a deployable panel. The four-patch array enhances the performance and increases the overall gain. The antennas PCB is a four-layer stack designed to ensure low losses and proper matching of all RF components. Figure 5depicts an artist’s view of the patch antenna array in its deployed configuration on a 1U CubeSat, achieving a total gain of ≈12.9 dBi. FIGURE 5. Deployable nadir-looking four-element LHCP patch antenna array for the GNSS-R payload. E. GNSS-R RECEIVER The GNSS-R receiver utilized in this study leverages on techniques employed in previous CubeSat missions developed by the UPC NanoSatLab. A new core design based on a Python-scripted receiver running on an SDR-hosted Linux environment has been implemented. This cGNSS-R receiver integrates Python-based blocks from the GNU Radio Companion (GRC) [21] to capture the raw In-phase and Quadrature (I/Q) GPS L1 data. An additional parsing script embedded within the GNSS-R receiver fetches the auxiliary information data from a high-dynamics GNSS receiver housed in the same RF-FE module. F. PAYLOAD CONTROLLER The Payload control script serves as the central command and control for the payload. It manages various tasks, including: •Power Cycling: Turning payload modules such as the SDR on and off. •Data Storage: Saving generated DDMs to a local memory. 56048 VOLUME 13, 2025 S. Z. Khan et al.: SDR-Based GNSS-R CubeSat Payload: Hardware Development and Optimization FIGURE 6. (a) Integrated payload except for the nadir-facing LHCP patch array, and (b) Block diagram of the payload stack. •Communication and Coordination: Facilitating the OBC in forwarding data upon request. •RF-FE Control: Sending control signals to the RF-FE to switch between zenith and nadir branches and ACL and ML for calibration purposes. G. INTEGRATION OF THE PAYLOAD The integrated hardware components are shown in Fig. 6a. In this stacked configuration, the SDR is positioned on top, followed by the interface board in the middle, and the RF board at the bottom, as depicted in Fig. 6b. The overall operation of the payload is depicted in the flow graph presented in Fig. 7. FIGURE 7. Overview of the GNSS-R payload’s operation. III. EXPERIMENTAL RESULTS AND DISCUSSION Each payload module, including the interface board and the RF-FE, have been individually tested for functionality and performance. The following sections detail these evaluations. A. PERFORMANCE ANALYSIS OF THE RF-FE Tests on the manufactured RF-FE module confirmed the required signal gain and demonstrated promising performance. Figure 8shows the measured frequency responses of the zenith and nadir channels, achieving signal gains of ≈30 dB. FIGURE 8. Measured frequency response of the zenith-looking branch of the RF-FE. B. EVALUATION OF THE HIGH-DYNAMICS OPERATION CAPABILITIES OF THE GNSS RECEIVER The LabSat GNSS simulator [22] was used to simulate a high-dynamic Low-Earth Orbit (LEO) environment to assess the GNSS receiver performance of the Precise Orbit Determination (POD) and the DDM tracking. The test results confirmed that the GNSS receiver reliably locked onto the GPS L1 signal by utilizing the zenith branch of the RF-FE, confirming the receiver’s robust tracking ability in high-dynamic conditions typical of LEO missions. C. GNSS-R RECEIVER PERFORMANCE The DDM generation capabilities of the GNSS-R receiver were tested by injecting LabSat’s synthetic data in diverse settings, including static, low-dynamic, airborne, and high-dynamic spaceborne scenarios. Figure 9presents a generated DDM, highlighting a distinct correlation peak achieved with 1 ms of coherent and Ni =500 incoherent integration. The trade-off between coherent and incoherent integration times significantly impacts the DDM’s spatial resolution, influencing the Doppler frequency resolution, and along-track blurring [23]. VOLUME 13, 2025 56049 S. Z. Khan et al.: SDR-Based GNSS-R CubeSat Payload: Hardware Development and Optimization TABLE 1. GNSS-R processing metrics between full satellite set, targeted PRN codes, and targeted PRN codes with reduced doppler search range. FIGURE 9. The sample generated DDM by the GNSS-R receiver. D. GNSS-R PROCESSING IMPROVEMENT BY USING AUXILIARY INFORMATION The GNSS-R receiver leverages on auxiliary parameters extracted from the Zenith-looking POD GNSS receiver to enhance the processing efficiency. This reference signal information includes the PRN code number of the satellites in view with the strongest signals and/or within a certain elevation angle. This limits the DDM generation to these specific PRN codes only, significantly reducing the GNSS-R receiver’s computational burden. Further enhancements were achieved by incorporating Doppler frequency shift information of the selected PRN codes. This information helps narrow the Doppler frequency search range around the DDM peaks, reducing the FFT processing requirements for generating a DDM. Table 1presents that combined optimization i.e. reducing the number of PRN codes and narrowing the Doppler search range, resulted in a significant cumulative processing optimization improvement by an ≈99% reduction in total execution time, an ≈87% reduction in I/O read operations, and ≈98% reduction in I/O write operations. This optimization facilitates the on-board processing by significantly reducing the computational demands, which is crucial given the limited processing power available on-board. Additionally, it reduces the data transfer requirements for ground processing by generating compact DDMs instead of requiring large volumes of raw data to be downloaded. IV. CONCLUSION In CubeSats-based GNSS-R missions, efficient on-board data processing is crucial due to limited computational resources and the limited downlink capabilities. This study has presented the design and development of a 0.5U GNSS-R payload with optimized DDM generation, utilizing auxiliary information obtained from the reference signal to facilitate the on-board processing. The details of the developed hardware and software have been presented. The tests demonstrated that in the GNSS-R receiver, processing efficiency was enhanced by ≈99% in case 4 out of the 32 PRN Codes were available in the reflected signal, through the utilization of auxiliary information on the reference signal, such as the available PRN codes and their Doppler frequency, due to the targeted DDM generation for the PRN codes and their reduced Doppler search bin while processing the raw data. Furthermore, the integrated hardware was tested to process GNSS-R data in a simulated high-dynamics environment as in a LEO, demonstrating the potential for reliable actual operation. Furthermore, several challenges related to the hardware and techniques utilized have been identified. One key challenge is ensuring the compatibility of the SDR form factor within the CubeSat standard. Although the payload is designed to fit within a 1U CubeSat, the SDR module slightly protrudes beyond the standard PC/104 board rails. To address this, a specific structure will have to be considered for improved integration. Another consideration involves further optimizing the GNSS-R data processing by leveraging on the FPGA’s capacity to handle data-intensive operations such as the FFT computation independently of the software layer. Off-loading the FFTs to the FPGA component of the SDR will allow to compute more DDMs per second. REFERENCES [1] A. Camps, Nanosatellites and Applications To Commercial and Scientific Missions. London, U. K.: IntechOpen, 2020. [2] C. D. Hall and R. A. Cordey, ‘‘Multistatic scatterometry,’’ in Proc. Int. Geosci. Remote Sens. Symp., ’Remote Sensing, Moving Toward 21st Century’, vol. 1, Sep. 1988, pp. 561–562. [3] S. Jin, A. Camps, Y. Jia, F. Wang, M. Martin-Neira, F. Huang, Q. Yan, S. Zhang, Z. Li, K. Edokossi, D. Yang, Z. Xiao, Z. Ma, and W. Bai, ‘‘Remote sensing and its applications using GNSS reflected signals: Advances and prospects,’’ Satell. Navigat., vol. 5, no. 1, p. 19, Dec. 2024. 56050 VOLUME 13, 2025 S. Z. Khan et al.: SDR-Based GNSS-R CubeSat Payload: Hardware Development and Optimization [4] J. Darrozes, N. Roussel, and M. Zribi, ‘‘The reflected global navigation satellite system (GNSS-R): From theory to practice,’’ in Microwave Remote Sensing of Land Surface. Amsterdam, The Netherlands: Elsevier, 2016, pp. 303–355. [Online]. Available: https://www. sciencedirect.com/science/article/pii/B9781785481598500074 [5] H. Fragner, A. Dielacher, M. Moritsch, F. Zangerl, P. Beck, O. Koudelka, P. Høeg, J. Wickert, E. Cardellach, and M. Wenger, ‘‘Recycling GPS signals and radiation monitoring: The two payloads onboard PRETTY,’’ Proc. SPIE, vol. 11131, pp. 142–153, Aug. 2019. [6] S. Gleason, S. Hodgart, Y. Sun, C. Gommenginger, S. Mackin, M. Adjrad, and M. Unwin, ‘‘Detection and processing of bistatically reflected GPS signals from low Earth orbit for the purpose of ocean remote sensing,’’ IEEE Trans. Geosci. Remote Sens., vol. 43, no. 6, pp. 1229–1241, Jun. 2005. [7] R. de Vos van Steenwijk, M. Unwin, and P. Jales, ‘‘Introducing the SGRReSI: A next generation spaceborne GNSS receiver for navigation and remote-sensing,’’ in Proc. 5th ESA Workshop Satell. Navigat. Technol. Eur. Workshop GNSS Signals Signal Process. (NAVITEC), Dec. 2010, pp. 1–7. [8] H. Carreno-Luengo, S. Lowe, C. Zuffada, S. Esterhuizen, and S. Oveisgharan, ‘‘Spaceborne GNSS-R from the SMAP mission: First assessment of polarimetric scatterometry over land and cryosphere,’’ Remote Sens., vol. 9, no. 4, p. 362, Apr. 2017. [9] C. S. Ruf, R. Atlas, P. S. Chang, M. P. Clarizia, J. L. Garrison, S. Gleason, S. J. Katzberg, Z. Jelenak, J. T. Johnson, S. J. Majumdar, A. O’brien, D. J. Posselt, A. J. Ridley, R. J. Rose, and V. U. Zavorotny, ‘‘New ocean winds satellite mission to probe hurricanes and tropical convection,’’ Bull. Amer. Meteorological Soc., vol. 97, no. 3, pp. 385–395, Mar. 2016. [10] C. Jing, W. Li, W. Wan, F. Lu, X. Niu, X. Chen, A. Rius, E. Cardellach, S. Ribó, B. Liu, Z. Guo, and Y. Nan, ‘‘A review of the BuFeng1 GNSS-R mission: Calibration and validation results of sea surface and land surface,’’ Geo-spatial Inf. Sci., vol. 27, no. 3, pp. 638–652, May 2024. [11] W. Wan, B. Liu, Z. Guo, F. Lu, X. Niu, H. Li, R. Ji, J. Cheng, W. Li, X. Chen, J. Yang, and Z. Bai, ‘‘Initial evaluation of the first Chinese GNSS-R mission BuFeng-1 A/B for soil moisture estimation,’’ IEEE Geosci. Remote Sens. Lett., vol. 19, pp. 1–5, 2022. [12] P. Jales, S. Esterhuizen, D. Masters, V. Nguyen, O. N. Correig, T. Yuasa, and J. Cartwright, ‘‘The new SPIRE GNSS-R satellite missions and products,’’ Proc. SPIE, vol. 11533, Sep. 2020, Art. no. 1153316. [13] P. Jales, J. Cartwright, M. Talpe, J. Mashburn, T. Yuasa, O. Nogues-Correig, V. Nguyen, and V. Freeman, ‘‘Spire Global’s operational GNSS-reflectometry constellation for Earth surface observations,’’ in Proc. IEEE Int. Geosci. Remote Sens. Symp., Jul. 2023, pp. 884–887. [14] C. Ruf, M. Unwin, J. Dickinson, R. Rose, D. Rose, M. Vincent, and A. Lyons, ‘‘CYGNSS: Enabling the future of Hurricane prediction [Remote sensing Satellites],’’ IEEE Geosci. Remote Sens. Mag., vol. 1, no. 2, pp. 52–67, Jun. 2013. [15] H. Carreno-Luengo and A. Camps, ‘‘First dual-band multiconstellation GNSS-R scatterometry experiment over boreal forests from a stratospheric balloon,’’ IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 9, no. 10, pp. 4743–4751, Oct. 2016. [16] J. F. Munoz-Martin, N. Miguelez, R. Castella, L. Fernandez, A. Solanellas, P. Via, and A. Camps, ‘‘3Cat-4: Combined GNSS-R, L-band radiometer with RFI mitigation, and AIS receiver for a I-unit cubesat based on software defined radio,’’ in Proc. IEEE Int. Geosci. Remote Sens. Symp., Jul. 2018, pp. 1063–1066. [17] A. Camps et al., ‘‘FSSCat: The federated satellite systems 3Cat mission: Demonstrating the capabilities of CubeSats to monitor essential climate variables of the water cycle [Instruments and missions],’’ IEEE Geosci. Remote Sens. Mag., vol. 10, no. 4, pp. 260–269, Dec. 2022. [18] V. U. Zavorotny, S. Gleason, E. Cardellach, and A. Camps, ‘‘Tutorial on remote sensing using GNSS bistatic radar of opportunity,’’ IEEE Geosci. Remote Sens. Mag. (replaces Newsletter), vol. 2, no. 4, pp. 8–45, Dec. 2014. [19] Analog-Devices-Website. (2018). ADRV9364-Z7020 Evaluation Board. [Online]. Available: https://www.analog.com/en/design-center/evaluationhardware-and-software/evaluation-boards-kits/ADRV9364-Z7020.html [20] S. Gleason, C. S. Ruf, A. J. O’Brien, and D. S. McKague, ‘‘The CYGNSS level 1 calibration algorithm and error analysis based on on-orbit measurements,’’ IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 12, no. 1, pp. 37–49, Jan. 2019. [21] GNU-Radio-Website. (2024). GNU Radio—The Free & Open Source Radio. [Online]. Available: https://www.gnuradio.org/ [22] I. Drewitz. (2024). LabSat 3. [Online]. Available: https://www.labsat.co. uk/index.php/en/products/labsat-3 [23] H. Park, D. Pascual, A. Camps, F. Martin, A. AlonsoArroyo, and H. Carreno-Luengo, ‘‘Analysis of spaceborne GNSS-R delay-Doppler tracking,’’ IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 7, no. 5, pp. 1481–1492, May 2014. SHAH ZAHID KHAN (Student Member, IEEE) received the degree in electronics engineering from Karachi Institute of Economics and Technology (KIET) and the M.Sc. degree in avionics engineering from the National University of Sciences and Technology (NUST), Pakistan. He is currently pursuing the Ph.D. degree with United Arab Emirates University (UAEU), specializing in GNSS-reflectometry payloads and data processing techniques. His research interests include GNSS security, drone navigation systems, GNSS-R, and small satellite systems. He is currently part of the Remote Sensing Laboratory (RSL), UAEU. He has been actively involved in projects related to GNSS-R payloads and synthetic aperture radar (SAR) and has authored or co-authored several articles in peer-reviewed journals and conferences. In 2024, their team was honored with the Best Paper Award for innovative research in space technologies at the Space Research Conference, UAE. YASIR M. O. ABBAS (Member, IEEE) was born in Sudan, in 1988. He received the B.Sc. degree in telecommunications engineering from the University of Khartoum, Sudan, in 2010, the M.Sc. degree in aerospace engineering from Istanbul Technical University (ITU), Türkiye, in 2015, and the Engineering Doctorate degree in embedded systems and space engineering from Kyushu Institute of Technology (Kyutech), Japan, in 2021. He is currently a Postdoctoral Researcher with UAE University, Al Ain, United Arab Emirates. He has been actively involved in the development of CubeSats, focusing on microwave remote sensing satellite missions, telecommunication subsystems, and on-board computers. His research interest includes around satellite engineering, with a particular emphasis on the design and implementation systems for space applications. VOLUME 13, 2025 56051 S. Z. Khan et al.: SDR-Based GNSS-R CubeSat Payload: Hardware Development and Optimization EDWAR EDWAR (Student Member, IEEE) received the bachelor’s degree in telecommunication engineering from Telkom University and the master’s degree from Bandung Institute of Technology. He is currently pursuing the Ph.D. degree in electrical and communication engineering with the College of Engineering, United Arab Emirates University (UAEU). He is currently an Assistant Professor with the School of Electrical Engineering, Telkom University, where his research focuses on nanosatellite subsystems and payload development, radar instrumentation, antennas, and RF devices. He is also a member of the Remote Sensing Laboratory (RSL). His doctoral research is centered on P-band radar development, contributing to advancements in nanosatellite technology, and radar instrumentation. ABDUL-HALIM JALLAD (Member, IEEE) received the B.Eng. degree in electronics engineering from the University of Kent, U.K., in 2003, and the Ph.D. degree from Surrey Space Centre, University of Surrey, U.K., in 2009. His thesis focused on ‘‘Distributed Computing for Space-Based Wireless Satellite Networks.’’ Currently, he is an Associate Professor with the Department of Electrical and Communications Engineering, United Arab Emirates University (UAEU), and an Affiliate Professor with the National Space Science and Technology Center, UAEU. Previously, he has served in various academic and leadership roles at American University of Ras Al-Khaimah and AlZaytoonah University of Jordan. His research interests include hardware design for space systems, spacecraft onboard command and data handling systems, the IoT and wireless sensor networks, and efficient FPGA-based designs. He has led numerous projects funded by prominent organizations, such as UAE Space Agency and ASPIRE UAE. ADRIANO CAMPS (Fellow, IEEE) was born in Barcelona, Spain, in 1969. He received the degree in telecommunications engineering and Ph.D. degree in telecommunications engineering from the Universitat Politècnica de Catalunya (UPC), Barcelona, Spain, in 1992 and 1996, respectively. In 1991 to 1992, he was at the ENS des Télécommunications de Bretagne, France, with an Erasmus Fellowship. Since 1993, he has been with the Electromagnetics and Photonics Engineering Group, Department of Signal Theory and Communications, UPC, where he was a first Assistant Professor, an Associate Professor in 1997, and a Full Professor since 2007. In 1999, he was on sabbatical leave at the Microwave Remote Sensing Laboratory, University of Massachusetts, Amherst. Since 1993, he has been deeply involved in the European Space Agency SMOS Earth Explorer Mission, from the instrument and algorithmic points of view, performing field experiments, and more recently studying the use of GNSS-R techniques to perform the sea state correction needed to retrieve salinity from radiometric observations. His research interests are focused in microwave remote sensing, with special emphasis in microwave radiometry by aperture synthesis techniques and remote sensing using signals of opportunity (GNSS-R). His publication record includes over 268 papers in peer-reviewed journals, nine book chapters, and the book Emery and Camps, ‘‘Introduction to Satellite Remote Sensing: Atmosphere, Ocean, Land and Cryosphere Applications,’’ Elsevier, 2017, 860 pages), and more than 541 conference presentations. According to Google Scholar, his hindex is 66, and his publications have received more than 17338 citations. According to the October 2023 Stanford ranking, he is among the top 2% of researchers in all categories. He holds 12 patents and has advised 33 Ph.D. Thesis students (+10 ongoing), and more than 150 B.Eng. final degree and M.Eng. Theses. These Ph.D. students have now responsibility positions at Universities, companies, and research centers, including NASA/JPL, ESA, and Airbus and two have started their own companies with Prof. Camps’ participation, having transferred a total of five patents to them. 56052 VOLUME 13, 2025