Periodic Reporting for period 1 - DeDUST (Debris Detection Using Star Trackers)
Reporting period: 2023-09-01 to 2024-08-31
Space based surveillance requires a monitoring of the space environment with high spatial and temporal resolution. Various dedicated space-based debris detection and tracking sensors show technological promise. The potential to scale the deployment of these sensor is however often limited from a financial and operational perspective. There is furthermore a painful irony in launching extra satellites with space debris detection systems.
Our proposed approach is to carry out space-based surveillance using star trackers. With the implementation of additional software functionality, arcsec’s Sagitta and Twinkle star tracker will be perfectly suited to detect and track space debris. As these sensors are already included with the purpose of determining the spacecraft attitude, the cost of this approach is extremely low. This facilitates the widescale adoption of the approach, leading to a sensor network that has sufficient spatial and temporal resolution.
Our proposed approach is to carry out space-based surveillance using star trackers. With the implementation of additional software functionality, arcsec’s Sagitta and Twinkle star tracker will be perfectly suited to detect and track space debris. As these sensors are already included with the purpose of determining the spacecraft attitude, the cost of this approach is extremely low. This facilitates the widescale adoption of the approach, leading to a sensor network that has sufficient spatial and temporal resolution.
The technical work performed in the context of DeDUST until now consists of three main parts.
The first part is the development of image processing algorithms that allow for the detection of space debris in star tracker images.
Second, the observations that are generated from part one are processed to perform characterization of the debris element.
Finally, a third part of the development is the set-up of a full simulation framework to function as a digital twin for the DeDUST project.
In the image processing part, an improved blob detection method has been developed, which is able to detect bright areas in the star tracker image that can correspond to space debris elements. These elements typically have a higher relative velocity and show up on the sensor as streaks. The detection of streak-like blobs was a novel development within arcsec. After blob detection, the debris elements are tracked over multiple consecutive images using a kalman filter to estimate position and velocity. This leads to increased precision, as well as more accurate error estimates. Finally, extra information is extracted from streak-like blobs. This information exists of location, direction, length and total brightness.
The debris characterization part consists of the development of an orbit determination pipeline to go from debris observation to debris orbit prediction. This is done by processing the raw observations to get angular measurements, then feeding the angular measurements into a batch least squares precise orbit determination. Using these algorithms, accurate predictions can be made about the observed debris.
Finally, a simulation framework was set up to predict and characterize results for the DeDUST network. A combination of orbital dynamics, photometric brightness simulation and observation simulations allow us to determine an expected number of observations for a satellite with a given orbit and a given attitude profile.
The first part is the development of image processing algorithms that allow for the detection of space debris in star tracker images.
Second, the observations that are generated from part one are processed to perform characterization of the debris element.
Finally, a third part of the development is the set-up of a full simulation framework to function as a digital twin for the DeDUST project.
In the image processing part, an improved blob detection method has been developed, which is able to detect bright areas in the star tracker image that can correspond to space debris elements. These elements typically have a higher relative velocity and show up on the sensor as streaks. The detection of streak-like blobs was a novel development within arcsec. After blob detection, the debris elements are tracked over multiple consecutive images using a kalman filter to estimate position and velocity. This leads to increased precision, as well as more accurate error estimates. Finally, extra information is extracted from streak-like blobs. This information exists of location, direction, length and total brightness.
The debris characterization part consists of the development of an orbit determination pipeline to go from debris observation to debris orbit prediction. This is done by processing the raw observations to get angular measurements, then feeding the angular measurements into a batch least squares precise orbit determination. Using these algorithms, accurate predictions can be made about the observed debris.
Finally, a simulation framework was set up to predict and characterize results for the DeDUST network. A combination of orbital dynamics, photometric brightness simulation and observation simulations allow us to determine an expected number of observations for a satellite with a given orbit and a given attitude profile.
The new blob detection algorithm that was developed for DeDUST correctly detects streaks 98% of the time and gives 0 false positives.
The simulation framework that is developed combines many different aspects relevant to SSA sensors and paints a complete picture of the SSA pipeline for space-based optical detections. It is easily adaptable to other scenarios or payloads.
The simulation framework that is developed combines many different aspects relevant to SSA sensors and paints a complete picture of the SSA pipeline for space-based optical detections. It is easily adaptable to other scenarios or payloads.