Riccardo Cipollone

Politecnico di Milano | Polimi · Department of Aerospace Engineering

Space Surveillance and Tracking (SST) is growing more and more important in the Space operations and mission analysis field, given how much the crowded environment building up near Earth can hamper them. Italy is involved in SST operations with collaborations both within and outside the EU. The Italian Space Operation Centre (ISOC) has recently upg...

The ability to simulate the behavior of different sensor configurations is critical for the development of a sensor network that provides data for Space Surveillance and Tracking (SST) services. Any software suite devoted to this shall be able to assess the performance of existing networks in terms of effectiveness and robustness, as well as to est...

In the last decades, the Near-Earth environment has grown increasingly crowded due to an exponential increase in launches and Space missions. As a direct consequence of that, fragmentations, impacts and debris have caused one of the most important issues of the commercial use of Space to rise: Space Traffic Management. In this framework, Space Surv...

The in-orbit overpopulation is currently fostering Space Surveillance and Tracking (SST) related applications, in which ground-based sensors (optical and radar) are typically used. This paper presents the orbit determination functions provided by the novel Italian SST Operation Centre (ISOC) Suite. First, a statistical index is computed to assess t...

One of the most crucial activities related to the RSO catalogue maintenance operations segment is Initial Orbit Determination (IOD), usually acting as last resort in this context. In this regard, the present work describes a novel development of the two-body integral-based IOD method, exploiting an Automatic Domain Splitting technique and Different...

In this study, a novel approach based on the U-Net deep neural network for image segmentation is leveraged for real-time extraction of tracklets from optical acquisitions. As in all machine learning (ML) applications, a series of steps is required for a working pipeline: dataset creation, preprocessing, training, testing, and post-processing to ref...

Performing real-time and robust space object detection by means of optical acquisitions is a challenging task in space surveillance, especially when tracking involves uncatalogued objects. Conventionally, this problem is addressed by means of traditional detection or segmentation based techniques to identify the tracklets relative position in the s...

The ability to simulate the behavior of different sensor configurations is critical for the development of a sensor network that provides data for Space Surveillance and Tracking (SST) services. Any software suite devoted to this shall be able to assess the performance of existing networks in terms of effectiveness and robustness, as well as to est...

The aim of this work is to exploit machine learning techniques to retrieve space debris angular velocity from light curves captured in night sky images. Considering that there are no real datasets providing this kind of information, a synthetic dataset emulating optical observation campaigns is generated. These mock acquisitions contain a tracklet,...

Multibeam surveillance radars are usually employed in staring mode and generate significant amount of data during the passage of a space object in their Field of View (FoV). Typically, the reconstruction of the object track is performed with deterministic methods based on heavy minimization processes where Signal to Noise Ratio (SNR), Doppler shift...

The ability to study the performance of different sensor configurations is crucial for the development of any sensor network that provides data for Space Surveillance and Tracking (SST) services. Any software suite devoted to this purpose shall be able to assess the performance of an existing network in terms of effectiveness and robustness, as wel...