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• Third Long March 5B R/B re-entry campaign. • Radar technologies for space surveillance and tracking related applications. • Orbit determination of uncatalogued objects. • Stochastic satellites re-entry monitoring and prediction. • Stochastic ballistic coefficient estimation. The current overpopulation of orbiting objects requires Space Surveillance and Tracking (SST) services. Among these, the monitoring of re-entry events plays a crucial role in ensuring the safety of people and infrastructure on Earth. The work discusses the third Long-March 5B re-entry campaign, which took place from October 31st to November 4th, 2022. This was coordinated, at a European level, by the Space Situational Awareness Centre (C-SSA) of the Italian Air Force (Aeronautica Militare, AM), in collaboration with the AM Flight Test Department, the Italian Space Agency (ASI) and the Astrophysics National Institute (INAF). Support was granted by other Italian institutions, like Politecnico di Milano (PoliMi). Besides the description of the overall re-entry campaign, this work focuses on the innovative contribution given by the Bistatic Radar for LEO survey (BIRALES), an Italian bistatic radar system managed by INAF with the support of PoliMi. Although the challenging off-nominal conditions, BIRALES was capable to acquire observation data throughout the entire campaign, providing both range, range-rate and angular track measurements to the C-SSA. The latter measurements were obtained through a novel approach based on an adaptive beamforming technique, which allows to reach a high-quality track. To obtain a re-entry epoch prediction, orbit determination results are computed starting from the last BIRALES observations without transit prediction, by also including measurements from the MFDR-MR sensor, a tracking radar managed by AM that participated in the campaign. A novel approach is also presented to estimate the ballistic coefficient in a stochastic way, and this estimation is used in both the orbit determination and the re-entry analysis. The re-entry epoch prediction computed in this way is fully consistent with the reference one provided by the U.S. Space Command. [ABSTRACT FROM AUTHOR]

In the last few years, many space surveillance initiatives have started to consider the problem represented by resident space object overpopulation. In particular, the European Space Surveillance and Tracking (EUSST) consortium is in charge of providing services like collision avoidance, fragmentation analysis, and re-entry, which rely on measurements obtained through ground-based sensors. BIRALES is an Italian survey radar belonging to the EUSST framework and is capable of providing measurements including Doppler shift, slant range, and angular profile. In recent years, the Music Approach for Track Estimate and Refinement (MATER) algorithm has been developed to retrieve angular tracks through an adaptive beamforming technique, guaranteeing the generation of more accurate and robust measurements with respect to the previous static beamforming approach. This work presents the design of a new data processing chain to be used by BIRALES to compute the angular track. The signal acquired by the BIRALES receiver array is down-converted and the receiver bandwidth is split into multiple channels, in order to maximize the signal-to-noise ratio of the measurements. Then, the signal passes through a detection block, where an isolation procedure creates, for each epoch, signal correlation matrices (CMs) related to the channels involved in the detection and then processes them to isolate the data stream related to a single detected source. Consequently, for each epoch and for each detected source, just the CM featuring the largest signal contribution is kept, allowing deriving the Doppler shift measurement from the channel illumination sequence. The MATER algorithm is applied to each CM stream, first estimating the signal directions of arrival, then grouping them in the observation time window, and eventually returning the target angular track. Ambiguous estimates may be present due to the configuration of the receiver array, which cause spatial aliasing phenomena. This problem can be addressed by either exploiting transit prediction (in the case of cataloged objects), or by applying tailored criteria (for uncatalogued objects). The performance of the new architecture was assessed in real operational scenarios, demonstrating the enhancement represented by the implementation of the channelization strategy, as well as the angular measurement accuracy returned by MATER, in both nominal and off-nominal scenarios. [ABSTRACT FROM AUTHOR]

The increasing number of objects in Earth orbit has encouraged the development of space surveillance and tracking (SST) applications. A critical aspect of SST is the identification and characterization of close encounters between pairs of space objects. The present work introduces a tool for the analysis of conjunctions, consisting of several modules. The first module, which has been shown to greatly speed up the process, employs a series of geometric and temporal filters to shorten the list of potential colliding pairs. The remaining objects are then propagated to compute important parameters such as time of closest approach (TCA), miss distance (MD), and probability of collision (PoC), the latter using three different methods. When a conjunction assessment returns an MD or a PoC that exceeds predefined alert thresholds, the algorithm enables the planning of an impulsive collision avoidance maneuver (CAM) at specific maneuver epochs. CAM candidates are determined using an analytical Keplerian approach, with the goal of achieving the desired PoC or MD. The user can then verify the performance of a specific candidate through perturbed propagation, and the MD and PoC are recalculated after the maneuver to ensure that they meet the desired thresholds. In conclusion, this paper evaluates the performance of the tool using synthetic and real data, providing valuable insights into its effectiveness. [ABSTRACT FROM AUTHOR]

In the last decades, the growing in-orbit population of resident space objects has become one of the main concerns for space agencies and institutions worldwide. In this context, fragmentations further contribute to increase the number of space debris and, operationally, it is fundamental to identify the event epoch as soon as possible, even when just a single fragment orbital state, resulting from an Initial Orbit Determination (IOD) process, is available. This work illustrates the Fragmentation Epoch Detector (FRED) algorithm, which deals with the problem through a stochastic approach, starting from a single fragment IOD result (expressed through mean state and covariance) and parent ephemeris (assumed as deterministic). The process populates the fragment ephemeris with a multivariate normal distribution and, for each couple sample-parent, the epochs of parent transit through the Minimum Orbital Intersection Distance (MOID) are first computed on a time window and then clustered in time. For each cluster, both the three-dimensional MOID and the three-dimensional relative distance distributions are derived, and their similarity is statistically assessed. Given that, at the actual fragmentation epoch, MOID and relative distance were equal, the cluster featuring the best matching between the two distributions is considered as the optimal candidate, and the related fragmentation epoch is returned from the time of parent transit through the MOID, in terms of mean and standard deviation. FRED algorithm performance is assessed through a numerical analysis. The algorithm robustness decreases when parent and fragment orbits share a similar geometry, and results get deteriorated if the perturbations and, moreover, the IOD errors are included in the process, but the correct fragmentation epoch is always present among candidates. Overall, FRED algorithm turns out to be a valid choice in operational scenarios, and a sensitivity analysis tests the algorithm out of the nominal conditions. [ABSTRACT FROM AUTHOR]