Stuart Bartlett, HEO; Sam Ross, HEO; Colleen Cahill, HEO; James Allworth, HEO; Hiranya Jayakody, HEO
https://doi.org/10.64861/KGPP9961
Keywords: Non-earth imagery, data fusion, passive RF, electro-optical SSA
Abstract:
As space becomes increasingly accessible to both government and commercial entities, the number of artificial resident space objects (RSOs) in Earth orbit has dramatically expanded. This rapid increase has coincided with increasingly diverse mission payloads, configurations and capabilities. The influx of new objects significantly increases the workload for existing space situational awareness infrastructure, leading to prolonged delays in identification. Characterizing this growing and increasingly complex body of objects in a timely manner post launch is of critical importance to maintaining space domain awareness (SDA). In this paper, HEO introduces a novel approach to RSO characterization through multi-phenemonology data fusion, combining HEO’s flyby Non-Earth Imagery (NEI) with a range of other sensor phenomenologies.
Traditionally, space object characterization approaches have typically focused on clever use of a single sensor modality by experts in that modality. This has given rise to a broad range of techniques exploiting different phenomenologies. Each technique provides some specific range of spatial, temporal and spectral resolution and uses inference based on those observations to perform characterization. Each also has specific gaps in coverage and spatial/spectral fidelity, based on their individual limitations. By combining these techniques in a complimentary manner, we can close these gaps.
For instance, HEO’s primary approach for RSO characterization is through the utilization of flyby NEI. This approach allows us to capture resolved images of RSOs, providing insights about object shape, size, and orientation that are difficult to obtain through other methods. Through these observations, we can learn about a satellite’s design and capabilities, infer information about its mission, and observe changes in state and patterns of behavior. Other space-borne techniques include infrared and UV imaging for resolved and semi-resolved imaging, and the use of dedicated SDA sensors or repurposed star trackers to provide updated state information.
Similarly, ground-based sensors can provide a wide range of phenomenologies, including resolved and unresolved imaging for tracking purposes, spectral/light curve fingerprinting, hyperspectral speckle imaging, active methods such as radar and laser ranging, and passive RF observation. Each of these techniques can individually be used for object characterization, either through direct observation or by identifying spatial/temporal patterns, such as through light curve characterization or optical fingerprinting. However, they are even more effective in combination.
HEO’s approach has brought together multiple modalities under the control of a single tasking and orchestration system, enabling collections from multiple sensors to be cooperatively scheduled. This straightforwardly extends the advantages of each individual phenomenology: by combining approaches, we can access a broader range of spatial, temporal, and spectral resolutions.The resulting analysis takes three basic forms: confirmation, inference, and combination.
Confirmation allows us to verify the same piece of information from two distinct sources. For example, a combination of NEI and RF beacon frequency can verify public information about a satellite’s basic characteristics, or confirm that a rocket body has been correctly catalogued as such.
Inference involves making use of overlapping data sets at distinct times to draw conclusions. Attitude estimates from ground-based EO can show that an object is in a stable attitude across a wide range of geographically distributed observations. If we then use NEI to determine that the target satellite is consistently nadir-pointing, we can then use the light curve data to increase our confidence that this was still the case even when NEI was not available. Lower-resolution spaceborne thermal imagery can then confirm this assessment at times when the spacecraft is in eclipse, and the other two phenomenologies are not in play.
Finally, combination involves the use of multiple pieces of discrete information to build up a larger picture. In a responsive scenario, ground-based EO and radar can be used to rapidly acquire a newly launched object. This tracking information can in turn cue other sensors. By then bringing together shape information from NEI, spectral information from ground-based hyperspectral, and rotation/stability information from light curve data, we can build a detailed model of the optical response of a target RSO. This allows us to make inferences about the spectral and material properties of the object that could not be confidently drawn from a single sensor.
HEO has successfully validated the correlation of ground-based EO and passive RF with flyby NEI to provide high-fidelity insights into objects in LEO. In this paper, we will discuss the extension of this system to incorporate other phenomenologies, as discussed above. This provides a way to augment the usefulness of multiple existing SDA phenomenologies, leveraging the strengths of each in a complimentary way. By facilitating this type of cooperative collection and subsequent analysis, HEO’s platform is enabling fast, responsive characterization of new RSOs.
Date of Conference: September 15-18, 2026
Track: Space-Based Assets