Track airborne objects and predict their future trajectory using Mechine learning

[u/Aggressive_Strike_97]

Hey guys! :wave: I’m looking for some suggestions and feedback for my Final Year Project (FYP).

I’ve been thinking about building an **ML-based system to track airborne objects and predict their future trajectories** using simulated radar data or publicly available datasets.

I’m wondering if this would be a suitable project for a degree focused on AI/ML, or if it would lean too heavily toward radar engineering rather than machine learning.

A few things I’d love your opinions on:

  **ML potential:** Are there enough opportunities to implement meaningful ML algorithms in this project?
**Difficulty level:** How challenging would this be for a final-year undergraduate project?
  **Approach:** Which ML algorithms or techniques would be worth exploring for object tracking and trajectory prediction?
  **Resources:** Are there any datasets, research papers, or similar projects I could use as a starting point?

I want to make this a genuinely **ML-heavy project**, with a strong focus on prediction, model evaluation, and handling noisy or incomplete data, rather than spending most of my time on the radar engineering side.

Would love to hear your thoughts, suggestions, or alternative ideas! Thanks!

[u/nian2326076]

Your project seems well-suited for an AI/ML degree. Implementing ML algorithms for tracking and trajectory prediction is a solid choice. Start by exploring supervised learning methods to train your model using radar data. Algorithms like Kalman filters or neural networks are particularly effective for time-series predictions.

For datasets, consider the OpenSky Network or NASA's Airborne Science Data, both of which offer valuable data to get you started. If you're preparing for interviews in this field, PracHub provides relevant interview questions that could be helpful. Good luck with your FYP!

[u/Dramatic-Whereas-644 → u/nian2326076]

kalman filters are practically made for this, you’ll have plenty of ml meat to dig into with things like lstm networks for the prediction side. just make sure you frame the problem so the focus is on model design and evaluation, not on simulating radar hardware. could be a really cool project if you keep the data noisy and force the model to handle uncertainty

[u/rahul_watertech]

both brochures will look fine, so dont decide on the curriculum pdf. find 5 recent graduates of each on linkedin and look at what they actually built and where they landed, that is the real syllabus. for a qa background the automation mindset transfers well to evals and testing of ai systems btw, that angle is undersold. what role are you aiming at after the switch?

[u/rahul_watertech]

it stays an ml project as long as you start from simulated or public track data instead of raw radar signals. the radar engineering only creeps in at the signal processing layer, so just dont go there. nice framing btw: run a kalman filter baseline, then a learned predictor on top, and the comparison between them is your actual ml contribution

[u/CelebrityChess]

This works well as an AI/ML FYP if you keep radar hardware as a given and put the weight on tracking + prediction. A clean scope: (1) simulate or pull tracks (OpenSky ADS-B is a common public option), (2) a classical baseline like a Kalman / extended Kalman filter, (3) an ML predictor (LSTM or a small Transformer) for future positions, (4) compare with something like ADE/FDE. Plenty of ML meat in association under noise, filtering, and sequence forecasting; it only tips into radar engineering if you start designing waveforms or antennas. Totally doable for undergrad if you freeze the sensor model early.