Using scattering signals of the global navigation satellite system (GNSS) for target detection has become one of the research hotspots. This technology faces the difficulty of low signal-to-noise ratio (SNR) target echoes. Since BeiDou geostationary orbit (GEO) satellites provide the opportunity to form a bistatic radar with some advantages, such as fixed coverage area and quasi monostatic configuration to avoid the interference from the direct signals, the target echoes may have regular phases that are beneficial to SNR enhancement. This study uses BeiDou GEO satellites and ground fixed receivers to form a bistatic radar and analyzes the phase changes in the reflected signal caused by the target, then gives two methods for SNR enhancement corresponding to two applications: deformation monitoring and ship detection. This paper first introduces the basic signal processing including the intermediate frequency (IF) signal collector and the range compression in the software receiver, then describes the basic SNR enhancement method, i.e., increasing coherence integration time (CIT), and shows its limitation by two target cases: static metal reflector on the land and ships in the water. After that, this study provides an improved SNR enhancement method based on Doppler and range compensation in the moving ship detection case. The experiment shows that by the SNR enhancement, the SNRs of target echo signals in range/Doppler domain increase more than 4 dB on average. This study also demonstrates the bistatic radar’s potential for monitoring surface deformation.

The use of delay-Doppler maps (DDMs) measured by Global Navigation Satellite System Reflectometry (GNSS-R) for target detection and location is a hot issue because of the potentially global coverage and short revisit periods of GNSS-R satellite missions. Existing researches have explored the detection of oil rigs, oil spills, sea ice and other targets with spaceborne GNSS-R. However, these researches may not fully consider advantages of multiple GNSS-R satellites, for example cyclone global navigation satellites system (CYGNSS) which has eight satellites. Two problems of using multiple GNSS-R satellites for detection and location are how to estimate the sea clutter that is used to cancel the clutter in DDMs and how to remove location ambiguity caused by the transformation from the delay-Doppler (DD) domain to the spatial domain. In this paper, an oil rig is taken as an example for detection and location with multiple GNSS-R satellites. To distinguish the oil rig from sea clutter, this paper uses subspace projection to estimate the clutter and subtract the estimation from the DDMs. Moreover, after the clutter cancelation, this paper projects the potential oil rig coordinates in the DD domain into a uniform coordinate system and removes the location ambiguity based on multi satellites fusion. The measured DDMs collected by CYGNSS are employed to conduct experiments to validate the feasibility of the proposed methods for detection and location with DDMs. By subtracting the estimated sea clutter from DDMs, the potential oil rig can be indicated in the DD domain. Two location experiments can calculate the unambiguous coordinate of the oil rig with a deviation less than 4 km.

Using global navigation satellite systems as transmitters of opportunity for target detection is a hot issue. The problems of this technology are the restricted power budget provided by navigation satellites and the defocusing caused by the movement of target. To refocus the echo and improve the signal-to-noise ratio under the condition of Beidou Geostationary satellites, this article uses jointly the short-time coherent integration and the long-time integration based on Fractional Fourier transform to correct the range migration and the Doppler frequency migration of signals for refocusing the signal energy. The effectiveness of the proposed algorithm for refocusing the echo from different types of maneuvering ships (a cargo ship and a ferry) is confirmed via an experimental campaign.

Airborne Global Navigation Satellite System-Reflectometry (GNSS-R) offers high-resolution observations but is constrained by time-varying satellite geometries, and currently lacks dedicated trajectory planning methods. To address this, this letter proposes a multi-objective trajectory planning method to balance the trade-off between coverage rate (CR) and flight cost. A trajectory optimization model is constructed by classifying flight parameters, where mission start time, track spacing, and flight speed are optimized using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) under cost constraints. To address computational challenges, efficient strategies such as satellite filtering and look-up table approximations are introduced, reducing processing time by over 90%. Validated against field data from the Rongowai mission over Lake Taupo, New Zealand, the optimized trajectory achieves a 96.6% CR within a single 130-minute mission. This significantly outperforms the 34% CR obtained from a 14-day historical campaign, demonstrating the proposed method's efficiency and practical value.

Soil moisture (SM) is one of the important driving factors for promoting photosynthesis of vegetation to produce organic carbon. Global satellite navigation system reflectometry (GNSS-R) has considerable potential in monitoring regional SM due to its penetrability and high temporal resolution. However, the existing researches have two problems: 1) the GNSS-R SM retrieval methods lack the uncertainty quantification for results; 2) the application potential of the retrieved GNSS-R SM has not been evaluated from the perspective of agricultural production. The core scientific basis for this evaluation is the tight biogeochemical coupling mechanism existing among SM, vegetation (crops grown in plain) physiology and atmospheric exchange (i.e., soil-plant-atmosphere continuum), which forms a quantitatively describable response relationship. This paper first proposes a Natural Gradient Boosting (NGBoost)-based framework to quantify the uncertainty and feed it back to improv the accuracy of retrieved results. Secondly, gross primary productivity (GPP) and evapotranspiration (ET) are used to conduct the potential evaluation of the retrieved GNSS-R SM. Compared with the frameworks based on the existing advanced uncertainty quantification techniques, such as Monte Carlo (MC) dropout-based and Deep Ensembles-based frameworks, the correlation (R) is improved by 4% 5%, the root mean square error (RMSE) is reduce by 17% 25%, and the uncertainty is decreased by 21% 25%. Compared with the official SM released by Cyclone GNSS, the R is increased by 0.2 and the RMSE is decreased by 0.01 cm3/cm3. From the responses of SM to GPP and ET, the retrieved SM has better response (9% 17% enhancement) to vegetation productivity compared with that of other frameworks. Meanwhile, compared with the responses of other remote sensing SM data to vegetation productivity, such as Soil Moisture Active and Passive, Climate Change Initiative, and the official SM released by Cyclone GNSS, R has increased by 20% to 60%. The results shows that the proposed framework can improve the accuracy of GNSS-R SM retrieval by quantifying uncertainty and the retrieved GNSS-R SM can better reflect the agricultural production. In addition, by analyzing the importance of physical parameters in the GNSS-R SM retrieval, we conclude that the signal reflectivity under different roughness conditions should be considered at the regional scale.