SBIR-STTR Award

This program will develop an innovative Random Finite Set (RFS)-theory-based software tool for Multi-Target Tracking (MTT), using measurement filtering methods that include the Sequential Monte Carlo Generalized Labeled Multi-Bernoulli (SMC-GLMB) and the Student’s t-Mixture GMLB (STM-GLMB) filters. These MTT methods enable classification and tracking of objects within the field of view of spacecraft, including a target spacecraft for rendezvous, secondary spacecraft, orbital debris, or other planetary bodies. In this program, ASTER Labs’ team will develop RFS-based algorithms that will improve the reliability of sensor measurement gathering, object classification, and target tracking, even in the presence of high levels of non-Gaussian noise. The newly developed RFS-MTT Toolset will integrate RFS-based algorithms with Clohessy-Wiltshire-Hill, Tschauner-Hempel, and Karlgaard relative orbital dynamics equations, sensor and uncertainty models, and non-Gaussian noise-generation methods to form a full software package for simulation and analytical purposes. Orbital trajectory data from databases maintained by NORAD that feature multiple rendezvous maneuvers will be utilized along with noise models to create additional measurement uncertainty. This data will be processed via the developed RFS-MTT Toolset to confirm fidelity of the dynamics models, analyze the RFS-based algorithms, and verify the algorithms’ ability to accurately track targets in high-clutter and high sensor noise environments. Phase I will focus on developing the RFS-MTT Toolset and associated algorithms for simulations and performance assessment in orbital spacecraft rendezvous and proximity operations. The project will also evaluate these algorithms for eventual incorporation into NASA’s existing software tools, e.g. GEONS. Potential NASA Applications (Limit 1500 characters, approximately 150 words) This RFS-MTT Toolset will be directly applicable to NASA’s spacecraft rendezvous and proximity operations missions. The software will enhance spacecraft multi-target tracking capabilities, to detect other vehicles and objects in the presence of non-Gaussian noise and false positives. The software applies to cargo transport and delivery, satellite servicing, and orbital debris removal, which will improve modeling and performance in an increasingly cluttered space environment, while having broader applicability to aerial and ground vehicles. Potential Non-NASA Applications (Limit 1500 characters, approximately 150 words) The RFS Multi-Target Tracking algorithms apply to systems requiring data-driven solutions for target identification, classification, and tracking in high-noise environments. Non-NASA applications include military hostile satellite tracking, and covert operations. Commercial applications include UAS integration into civilian aerospace, integration onto UGV systems, and pedestrian flow monitoring.

This program will develop an innovative Random Finite Set (RFS)-theory-based software tool for Multi-Target Tracking (MTT), using measurement filtering methods that include the Sequential Monte Carlo Generalized Labeled Multi-Bernoulli (SMC-GLMB), Student's t-Mixture GMLB (STM-GLMB), and Joint-GLMB. These MTT methods enable classification and accurate tracking of objects within the field-of-view of spacecraft, including a target spacecraft for rendezvous, secondary spacecraft in vicinity, and orbital debris. In this program, ASTER Labs’ team will enhance RFS-based algorithms that will improve the reliability and efficiency of sensor measurement gathering, object classification, and target tracking, even in the presence of high levels of non-Gaussian noise. The newly developed RFS-MTT Toolset will integrate the RFS-based filters with Clohessy-Wiltshire-Hill, Tschauner-Hempel, and Karlgaard relative orbital dynamics equations, vehicle attitude, sensor and uncertainty models, and non-Gaussian noise-generation methods to form a full software package for simulation and analytical purposes. Orbital trajectory data featuring multiple rendezvous maneuvers will be utilized along with high-fidelity noise, disturbance, birth, and clutter to create additional measurement uncertainty. This data will be processed via the developed RFS-MTT Toolset to confirm fidelity of the processing techniques models, and verify the system’s ability to effectively track multiple targets in environments with high clutter and high sensor noise. Phase II will expand the RFS-MTT Toolset and associated algorithms for software simulations and performance assessment in orbital spacecraft rendezvous and proximity operations. The RFS-MTT Toolset will be incorporated into the full SWARM Toolset and evaluate this functionality for eventual incorporation into NASA’s software tools, e.g. GEONS, MONTE. Hardware demonstrations will with wheeled vehicles and UAVs be performed and presented to NASA. Potential NASA Applications (Limit 1500 characters, approximately 150 words): This RFS-MTT Toolset is directly applicable to NASA’s spacecraft rendezvous and proximity operations missions. The software will enhance spacecraft multi-target tracking capabilities to detect other vehicles and objects in the presence of clutter and non-Gaussian noise, and reduce false and missed detections. Applications include supply transport, satellite servicing, and orbital debris removal, which address current and future needs in an increasingly-complex space environment, with broader applicability to aerial and ground vehicles. Potential Non-NASA Applications (Limit 1500 characters, approximately 150 words): The RFS-MTT algorithms apply to commercial and defense systems requiring data-driven solutions for target identification, classification, and tracking in high-noise and high-traffic environments. Non-NASA applications include defensive hostile satellite tracking and covert operations. Commercial applications include UAS traffic in civilian aerospace, UGV operations, and pedestrian flow monitoring. Duration: 24