An Image Processing Pipeline for Autonomous Deep-Space Optical Navigation

A new era of space exploration and exploitation is fast approaching. A multitude of spacecraft will flow in the future decades under the propulsive momentum of the new space economy. Yet, the flourishing proliferation of deep-space assets will make it unsustainable to pilot them from ground with standard radiometric tracking. The adoption of autonomous navigation alternatives is crucial to overcoming these limitations. Among these, optical navigation is an affordable and fully ground-independent approach. Probes can triangulate their position by observing visible beacons, e.g., planets or asteroids, by acquiring their line-of-sight in deep space. To do so, developing efficient and robust image processing algorithms providing information to navigation filters is a necessary action. This paper proposes an innovative pipeline for unresolved beacon recognition and line-of-sight extraction from images for autonomous interplanetary navigation. The developed algorithm exploits the k-vector method for the non-stellar object identification and statistical likelihood to detect whether any beacon projection is visible in the image. Statistical results show that the accuracy in detecting the planet position projection is independent of the spacecraft position uncertainty. Whereas, the planet detection success rate is higher than 95% when the spacecraft position is known with a 3sigma accuracy up to 10^5 km.Comment: 26 pages, 7 figure

Star Centroiding Based on Fast Gaussian Fitting for Star Sensors

The most accurate star centroiding method for star sensors is the Gaussian fitting (GF) algorithm, because the intensity distribution of a star spot conforms to the Gaussian function, but the computational complexity of GF is too high for real-time applications. In this paper, we develop the fast Gaussian fitting method (FGF), which approximates the solution of the GF in a closed-form, thus significantly speeding up the GF algorithm. Based on the fast Gaussian fitting method, a novel star centroiding algorithm is proposed, which sequentially performs the FGF twice to calculate the star centroid: the first FGF step roughly calculates the Gaussian parameters of a star spot and the noise intensity of each pixel; subsequently the second FGF accurately calculates the star centroid utilizing the noise intensity provided in the first step. In this way, the proposed algorithm achieves both high accuracy and high efficiency. Both simulated star images and star sensor images are used to verify the performance of the algorithm. Experimental results show that the accuracy of the proposed algorithm is almost the same as the GF algorithm, higher than most existing centroiding algorithms, meanwhile, the proposed algorithm is about 15 times faster than the GF algorithm, making it suitable for real-time applications

Desenvolupament i caracterització d’un star tracker basat en Raspberry Pi

In astronomy and the aerospace world, determining the orientation or attitude of a system in space is of great importance, whether it's simply to determine observations or to successfully carry out space missions. One of the devices commonly used is the star tracker. These devices allow the determination of attitude based on observed stars, using a preloaded catalogue in the system that identifies which stars are captured. In this work, the catalogue preloaded in the star tracker has been specifically designed for the proposed requirements, a catalogue derived from the Hipparcos catalogue. The starting point of the design is a Lost In Space (LIS) situation, where the only reference available are the stars that this device can capture and identify correctly. Traditionally, they have been systems with limited accessibility, only for large organizations or major space projects, due to their high cost and complexity. With the rapid development and advancement of technologies in recent years, today, we can find high-performance technological elements at a reduced cost. In this case, a functional star tracker has been developed and implemented using a Raspberry Pi with the Raspberry Pi OS operating system, a camera, and an optical system. This setup allows capturing images of the celestial sphere. To capture the images, the MATLAB® package "MATLAB Support Package for Raspberry Pi Hardware" has been used, a package that allows direct connection between the Raspberry Pi operating system and MATLAB®, through a WiFi network or an ethernet cable. To determine the attitude, it's necessary to find the match between the stars of the preloaded catalogue and the captured stars. In this case, the three brightest stars captured after processing, correcting, and characterizing the image. To correct the light frame and eliminate random noise and outliers, the master frames of the dark, bias, and flat images have been obtained. Additionally, an average filter has been used to soften and blur the captured stars. To characterize the image, the optimal ISO value, exposure time, aperture, and focus were determined. Finally, with the corrected image, the centroid of the three brightest stars, and the match found in the catalogue, the rotation matrix was determined using the Kabsch algorithm, a matrix that has allowed determining the attitude of the star tracker. The attitude has been determined in two ways, with the quaternion and with the Euler angles, with a precision between 1 and 3 arc minutes and a 97,50% satisfactory detection rate