Possibilistic multi-sensor tasking for geosynchronous space object

The growing number of Geosynchronous Earth Orbit (GEO) objects necessitates efficient multi-sensor tasking methods to maintain a space object catalog (SOC) for accurate orbital state estimation and collision risk assessment. While sensor tasking methods based on probabilistic Random Finite Set (RFS) theory have recently gained attention, effectively capturing epistemic uncertainty arising from incomplete system knowledge remains a significant challenge. To address this issue, this paper proposes a possibilistic multi-sensor tasking method using the Outer Probability Measure (OPM) theory and the possibility Labeled Multi-Bernoulli (LMB) filter. A possibilistic information gain (IG) metric for LMB uncertain finite sets (UFSs) is derived in a Gaussian Max-Mixture (GMM) form, enabling efficient reward function design. The multi-sensor tasking problem is formulated as a stepwise optimization process with computationally efficient implementations. The proposed method integrates multi-sensor tasking, tracking, and information fusion in a centralized network, offering superior robustness and efficiency compared to RFS-based approaches, particularly in scenarios characterized by incomplete information. Simulation results, including sensor tasking for 1000 GEO objects and a comprehensive sensitivity analysis, validated the enhanced performance of the developed methods.