Project Idea
LiRA-Align is a compact and cost-effective LIDAR-based system designed to detect structural misalignment in fixed radio telescopes. The system continuously monitors the distance between a fixed LIDAR sensor and a reflective target mounted on the telescope to identify mechanical shifts or deviations caused by environmental or structural factors. The system was simulated using MATLAB to evaluate its ability to detect misalignment under both ideal and noisy conditions.
01
The Beginning
The idea started from the need to maintain precise alignment of structural components in radio telescopes, such as the feed and horn, since factors such as wind, temperature, and mechanical wear can cause small misalignments that affect signal accuracy. Since many traditional alignment systems rely on manual inspection or low-precision sensors and do not provide real-time responses, the project proposed using LIDAR to provide continuous and real-time alignment monitoring in a simple and cost-effective way.
02
The Idea
The project provides a real-time structural alignment monitoring system using a LIDAR sensor to measure the distance between the sensor and a reflective target mounted on the telescope. When the measured distance exceeds a predefined threshold, the system generates an alert indicating misalignment. The system consists of a LIDAR sensor, a reflective target, a microcontroller, and an alert unit, providing a lightweight and low-cost approach for monitoring radio telescopes in educational observatories without requiring complex infrastructure.
03
The Experience
The concept developed from identifying the problem of structural misalignment in radio telescopes into a system based on continuous distance measurement using LIDAR. The system was then simulated in MATLAB by generating time-series data representing telescope movement with and without mechanical disturbances, adding Gaussian noise to represent realistic sensor conditions, and comparing the measurements against a baseline to identify when the misalignment threshold was exceeded. The results demonstrated that the system can detect deviations and maintain reliable sensitivity even under noisy conditions.
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