A catalogue of over 22,000 objects in Earth’s orbit is currently maintained, and that number is expected to double within the next decade. Novel data collection regimes are needed to scale our ability to detect, track, classify and characterize resident space objects in a crowded low Earth orbit. This research presents RSOnet, an image-processing framework for space domain awareness using star trackers. Star trackers are cost-effective, flight proven, and require basic image processing to be used as an attitude-determination sensor. RSOnet is designed to augment the capabilities of a star tracker by becoming an opportunistic space-surveillance sensor. Our research demonstrates that star trackers are a feasible source for RSO detections in LEO by demonstrating the performance of RSOnet on real detections from a star-tracker-like imager in space. RSOnet convolutional-neural-network model architecture, graph-based multi-object classifier and characterization results are described in this paper.

In recent years, we have seen significant improvements in the digital sun sensor (DSS) design enabled by advanced micro-systems fabrication and optical sensing technologies. In this paper, we propose a simple single-slit DSS concept with improved accuracy using sub-pixel interpolation. In considering the DSS design, we focused on several characteristics of the sun sensor, including field-of-view, sensor accuracy, complexity, and computational requirements. First, the optimal mask design was determined based on the simple geometry of the slit size, mask height and pixel width. Then, a two-step pixel read-out algorithm was implemented for sub-pixel level interpolation to determine the illumination ratio using 1-, 2-, 4- and 8-bit readouts. Lastly, the improved DSS was integrated with typical CubeSat, commercial-grade attitude sensors suite and a simple TRIAD method to determine the attitude of a CubeSat in LEO. Compared to standard 1-bit read-out mode (0.32deg RMSE), 8-bit DSS achieves better than 0.01deg RMSE). In a CubeSat scenario, improvements in attitude knowledge and control accuracy are marginal when using TRIAD, due to the significantly lower accuracy in other CubeSat-scale sensors (magnetometer, for example).

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This paper describes a computation method for obtaining dielectric constant using Global Navigation Satellite System reflectometry (GNSS-R) products. Dielectric constant is a crucial component in the soil moisture retrieval process using reflected GNSS signals. The reflectivity for circular polarized signals is combined with the dielectric constant equation that is used for radiometer observations. Data from the Cyclone Global Navigation Satellite System (CYGNSS) mission, an eight-nanosatellite constellation for GNSS-R, are used for computing dielectric constant. Data from the Soil Moisture Active Passive (SMAP) mission are used to measure the soil moisture through its radiometer, and they are considered as a reference to confirm the accuracy of the new dielectric constant calculation method. The analyzed locations have been chosen that correspond to sites used for the calibration and validation of the SMAP soil moisture product using in-situ measurement data. The retrieved results, especially in the case of a specular point around Yanco, Australia, show that the estimated results track closely to the soil moisture results, and the Root Mean Square Error (RMSE) in the estimated dielectric constant is approximately 5.73. Similar results can be obtained when the specular point is located near the Texas Soil Moisture Network (TxSON), USA. These results indicate that the analysis procedure is well-defined, and it lays the foundation for obtaining quantitative soil moisture content using the GNSS reflectometry results. Future work will include applying the computation product to determine the characteristics that will allow for the separation of coherent and incoherent signals in delay Doppler maps, as well as to develop local soil moisture models.