The Global Navigation Satellite System (GNSS) has become a valuable resource as a remote sensing technique. In the past decade, the use of reflected GNSS signals for sensing the Earth, also known as GNSS reflectometry (GNSS-R), has grown rapidly. On the other hand, with the continuous development of GNSS, multi-frequency multi-modulation signals have been used to enhance not only positioning performance, but also remote sensing applications. It is known that for some constellations, navigation satellites broadcast signals employing BPSK (binary phase-shift keying) modulation and BOC (binary offset carrier) modulation at the same frequency band. This paper proposes a new GNSS-R measurement, called a composite delay-Doppler map (cDDM), by utilizing the received reflected GNSS signals with different modulation techniques for the purpose of retrieving wind speed. The GNSS-R receiver can receive BPSK and BOC signals simultaneously at the same frequency band (e.g., GPS III L1 C/A and L1C or QZSS L1 C/A and L1C) and process the signals to generate GNSS-R measurements. Exploration of the observable features extracted from the composite DDM and the wind speed retrieval algorithm are also provided. The simulation verifies the proposed method under a configuration that is specified for the orbital and instrument specification of the upcoming TRITON mission.

The use of reflected Global Navigation Satellite System (GNSS) signals has shown to be effective for some remote sensing applications. In a GNSS Reflectometry (GNSS-R) system, a set of delay-Doppler maps (DDMs) related to scattered GNSS signals is formed and serves as a measurement of ocean wind speed and roughness. The design of the DDM receiver involves a trade-off between computation/communication complexity and the effectiveness of data retrieval. A fine-resolution DDM reveals more information in data retrieval while consuming more resources in terms of onboard processing and downlinking. As a result, existing missions typically use a compressed or low-resolution DDM as a data product, and a high-resolution DDM is processed for special purposes such as calibration. In this paper, a deep learning, super resolution algorithm is developed to construct a high-resolution DDM based on a low-resolution DDM. This may potentially enhance the data retrieval results with no impact on the instrument design. The proposed method is applied to process the DDM products disseminated by the Cyclone GNSS (CYGNSS) and the effectiveness of wind speed retrieval is demonstrated.