Information Processing Technique For LEO Space Object Surveillance Based On Radar System

Low earth orbit (LEO) space surveillance systems are playing a crucial andfundamental role to support important functionalities of the space situational awarenessand orbital collision avoidance, which provide the guarantees for efficient utilization ofthe space resources and safety of the movements of the on-orbit spacecrafts. Theconstruction of an effective LEO space surveillance system that undertakes thecataloging and tracking tasks for LEO space objects is very important and challenging.Moreover, the information processing approach is a core component in any surveillancesystem. The investigation on information processing techniques will improve not onlyon the performances of the existing systems to satisfy the future needs of theestablishments of space surveillance systems, but also get a better understanding ontheoretical developments of more effective surveillance systems.Serving as a major instrument of LEO space surveillance systems, theground-based radar systems are the dominating sources where the measurement data ofLEO space objects can be obtained. Therefore, the radar systems of a LEO spacesurveillance system, especially an emerging fence-type surveillance radar system, havebeen taken as the fundamental infrastructure in this thesis. Based on such aninfrastructure, we investigate the key techniques of information processing to constructa radar surveillance system and the general problems with frontiers to satisfy the futuredemands of space surveillance systems. The main results and contributions aresummarized as follows.Firstly, the LEO space object environments are theoretically analyzed thatfollowed by the introductions of the development status and tendency of thesurveillance technologies of the LEO space objects based on the cognition progress forspace objects’ characteristics. The main challenging issues in the informationprocessing for exploring the LEO space objects by using a radar system are alsoelaborated. In addition, the basic interdisciplinary knowledge including the orbitalmovements and radar exploring technologies are also refined.Secondly, the methodologies of signal detection and parameter estimation for thesmall space objects based on a fence-type space surveillance radar system areinvestigated. Moreover, the observation model of space objects based on the fence-typeradar system is also introduced. The sparse characteristics of space objects crossing thebeam fence are analyzed. Furthermore, the reconstruction problem in originalthree-dimensional space can be reduced into two-dimensional space by carefullyanalyzing the relations between the acceleration and the directions of arrival for thecorresponding LEO space debris based on a fence-type surveillance system, which canachieve a higher efficiency and improve the reconstruction accuracy. According to these, a sparse reconstruction method involving the orbital knowledge constraint is proposedfor the signal detection and parameter estimation of the small objects. Finally, thecorrect sparse reconstruction probability and the estimation accuracy affected by thevalue of selected parameters are evaluated by theoretical analysis and numeroussimulations. In addition, the resolution capability for multiple objects is also analyzed.The results demonstrate the robustness of the approach in scenarios with a lowSignal-to-Noise Ratio (SNR) and the super-resolution properties.Subsequently, the data association problem for LEO space debris surveillancebased on a double fence radar system is also investigated. The surveillance mechanismsof a double fence radar system are elaborated that followed by the descriptions of theobservation data by using the information sets. Based on these constructions, weanalyze the set of orbital constraints on the LEO space debris in which the informationsets have to be satisfied. Moreover, combining with the hypothesis test methods, a noveldata association scheme is implemented by analyzing the discrepancy of the associationvariables, i.e. radial velocities, which are calculated according to the differenthypothetical associated sets. Furthermore, we also derive a theoretical analysis of theresolution performance in three-dimensional space for our proposed schemes. Thesuperiority and the effectiveness of our novel data association scheme are demonstratedby experimental results. The data used in our experiments is the LEO space debriscatalog produced by the North American Air Defense Command (NORAD) up to2009,especially for scenarios with high densities of LEO space debris, which were primarilyproduced by the collisions between Iridium33and Cosmos2251, which highly supportthe demonstration of the double fence space surveillance radar system.In this thesis, we also explore the orbit correlation approach including themaneuver detection problems. We integrate these two problems mentioned above intoone interrelated problem, and consider them simultaneously under a scenario wherespace objects only perform a single in-track orbital maneuver during the time intervalsbetween observations. More precisely, we mathematically formulate such an integratedproblem as the Maximum A-Posteriori Probability (MAP) estimation. To solve theMAP estimation, the maneuvering parameters are firstly estimated by optimally solvingthe constrained non-linear least squares iterative process based on a Second-order ConeProgramming (SOCP) algorithm. Subsequently, the corresponding posterior probabilityof an orbital maneuver and a joint association event can be approximately calculated bythe Joint Probabilistic Data Association (JPDA) algorithm. The desired solution hasbeen derived based on the MAP criterions. The performances and advantages of theproposed approaches have been shown by both theoretical analysis and simulationresults. We have to address that the proposed algorithms can be adapted and extended tomany different situations.Finally, the group tracking methods of LEO space objects are studied as well. In this chapter, we firstly introduce the orbital movement model and the observation modelof group objects and propose the optimal Bayesian tracking filtering involving groupcenter. Subsequently, due to the Bayesian theorem, the Bayesian tracking procedure canbe broke down into some detailed modules including the state transition model of spaceobjects, the state transition model of the group centers, the interaction Markov RandomField (MRF) model between group centers and individual trajectories, and the posteriordensity model of observations. We mainly focus on how to use the interaction MRFmodel between group centers and individual trajectories. It has been shown that we canobtain not only a more robust estimation of object numbers and improve the accuracy ofthe estimated corresponding individual trajectory, but also depict the evolution of thegroups under scenarios with the low object detection probabilities. MCMC-Particlealgorithm has been utilized to calculate the Bayesian integral and fulfill group tracking.Furthermore, the mechanism for group configuration inference has been incorporatedinto our approach that makes the operations of merge and split for groups much moresmart and efficient during the tracking process. We also show that the proposedalgorithm has significant impacts for the practical applications. Finally, we evaluate theperformances of our algorithms by the simulations of tracking multiple closely spacedorbital objects. The results verified the effectiveness of our proposed schemes for thescenarios with a low detection probability in a high dense clutter.