Owing to the global coverage and high visibility of global navigation satellite systems (GNSS), passive radar using GNSS satellites as transmitters of opportunity has shown great potential for moving target detection, and its possibility to detect short-range maritime targets has been verified. Nevertheless, the existing methods always require tens of seconds for echo integration, which is not ideal for the detection of targets with high maneuverability. To solve this problem, a bistatic configuration using BeiDou geostationary earth orbit (GEO) satellites as illuminators of opportunity, which is suited for short-time coherent integration of echo signals, is presented. In addition, the corresponding signal processing method is introduced. To achieve a more continuous trajectory of the target, a method used to fuse range-Doppler maps obtained individually from multiple BeiDou GEO satellites is also described. The effectiveness and functionality of our proposed algorithms are demonstrated with an experiment in which a ferry was used as a moving target and its real-time location data were adopted as a reference.

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.

Airborne passive interferometric microwave sensors (PIMSs) were proposed as a powerful complementary tool for ship detection, especially in low visibility and high sea clutter environments. At present, there is only one algorithm for ship detection by airborne PIMSs, and this algorithm is only based on the “cold” brightness temperature (TB) characteristic of metal ships with wakes in passive interferometric microwave images (PIMIs). Moreover, it has been found that ship wakes always exhibit a “hot” TB characteristic in PIMIs. In this article, an improved algorithm is innovatively proposed for ship detection by airborne PIMSs to improve detection performance, and it is simultaneously based on the “cold” and “hot” TB characteristics of metal ships with wakes. The microwave TB model of metal ships with wakes is established to analyze its TB characteristics. Given the TB model and its characteristics, an improved algorithm is introduced, which comprises five steps: searching targets, improving TB maps, clustering targets, estimating ship headings, and estimating ship trajectories. The improved algorithm can detect ships with a low false-alarm probability (FAR) and accurately estimate ship headings using only a single snapshot and the ship trajectory. The practicability and detection performance of the improved algorithm is verified by airborne experiments using an X-band PIMS. The experimental results demonstrate that the improved algorithm can accurately detect ships, estimate ship headings using only a single snapshot, and estimate ship trajectories. The impact of the algorithm parameters on the detection performance is analyzed and discussed. Moreover, the detection performance of the improved algorithm is analyzed and compared with the performance of the only existing algorithm, and the experimental results indicate that the improved algorithm has a better detection performance with a lower FAR value than the only existing algorithm, as expected.

In this letter, we proposed a method based on cyclone global navigation satellite system (CYGNSS) for improving the accuracy of soil moisture (SM) estimation through the selection of auxiliary data and a four-step quality control. We investigate the impact of elevation and vegetation, as well as evaluating the quality of Doppler delay map (DDM) and different ground terrains. The program adopts the support vector machine (SVM) algorithm, input CYGNSS, and auxiliary data. With the hourly SM data on Wuhan Baoxie site from January 2020 to August 2020 as an example, the effectiveness of quality control was verified. A substantial improvement in correlation coefficient of ~0.46 for average between CYGNSS reflectivity and in situ SM was obtained compared with ~0.03 before quality control, resulting in better SM estimation accuracy compared with that of in situ measurements (from $R = 0.67$ to 0.87).

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.