This analysis demonstrates enhanced localization of rocket debris using sonic boom signals and advanced algorithms.
The precise localization and retrieval of rocket debris have become critical challenges due to advancements in rocket technology and the increasing frequency of space exploration missions. This study addresses these challenges by developing robust mathematical models and utilizing the Artificial Bee Colony (ABC) algorithm to analyze sonic boom signals for accurate debris positioning. By applying the Time Difference of Arrival (TDOA) technique, a comprehensive localization framework is established, enabling the determination of three-dimensional coordinates and the occurrence time of sonic boom events. To mitigate the impact of random errors inherent in monitoring equipment, Bayesian inference methods are integrated, thereby enhancing the model’s predictive accuracy and reliability under uncertain conditions. Simulation results demonstrate the efficacy of the proposed model, showcasing high precision in debris localization and robustness against measurement noise. This research innovatively combines global optimization algorithms with statistical inference, offering a superior approach compared to traditional triangulation and trilateration methods. The findings significantly contribute to the fields of aerospace engineering and debris management, providing a foundational framework for more effective and reliable rocket debris recovery operations.
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Jikun et al. (2025) studied this question.
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