Maneuver Detection via a Confidence Dominance Maneuver Indicator

TL;DR

This paper introduces a novel maneuver detection method for spacecraft based on confidence levels, using a confidence-dominance maneuver indicator (CDMI) and recursive polynomial optimization, achieving high accuracy and efficiency.

Contribution

The paper proposes a new confidence-based maneuver detection approach with an integrated CDMI that eliminates manual parameter tuning and improves detection accuracy and computational efficiency.

Findings

Achieves up to 99.33% detection accuracy.

Outperforms existing methods by at least 10%.

Reduces computational costs significantly.

Abstract

Accurate and efficient maneuver detection is critical for ensuring the safety and predictability of spacecraft trajectories. This paper presents a novel maneuver detection approach based on comparing the confidence levels associated with the orbital state estimation and the observation likelihood. First, a confidence-dominance maneuver indicator (CDMI) is proposed by setting a confidence level for the state estimation and computing the maximum likelihood of the observation and its confidence level. The CDMI then flag a maneuver when the observation's confidence level exceeds that of the state estimation, indicating that the observation is unlikely under the no-maneuver hypothesis while maintaining consistency with the prior state estimation confidence. To efficiently compute the maximum likelihood of the observation and obtain the CDMI, a recursive polynomial optimization method is developed, taking advantage of convex optimization and polynomial approximation. In addition, an integrated CDMI approach is developed to eliminate the need to manually select the state confidence level. The integrated CDMI approach maintains high detection accuracy while simultaneously providing an indication of maneuver likelihood, thereby enhancing robustness and practical applicability. The performance of the proposed CDMI-based maneuver detection approaches is evaluated against an optimal control distance metric and two mixture-based approaches. The simulation results demonstrate that the proposed integrated CDMI approach can achieve up to 99.33\% detection accuracy, at least 10% higher than the competing methods, while substantially reducing computational costs.