Hey, that sounds awesome! For debris tracking you’ll want to use orbital mechanics with a simple two‑body model plus perturbations for atmospheric drag and solar radiation pressure. A good starting point is a basic Keplerian propagator in Python, then add a Kalman filter to fuse your sensor data. Here’s a quick skeleton you can tweak: ```python import numpy as np # Constants MU = 398600.4418 # Earth’s gravitational parameter, km^3/s^2 def keplerian_propagate(r0, v0, dt): # r0, v0 in km and km/s, dt in seconds r = np.array(r0) v = np.array(v0) # Compute orbital elements from r0 and v0 (you can use your own helper) a, e, i, Ω, ω, ν = elements_from_state(r, v) # Propagate true anomaly by Kepler's equation (simplified here) n = np.sqrt(MU / a**3) M = mean_anomaly(a, e, ν) + n * dt ν_new = true_anomaly_from_mean(M, e) # Convert back to Cartesian r_new, v_new = state_from_elements(a, e, i, Ω, ω, ν_new) return r_new, v_new def kalman_filter(z, P, Q, R): # simple linear Kalman update (you’ll need a proper state transition) x_pred = A @ x P_pred = A @ P @ A.T + Q K = P_pred @ H.T @ np.linalg.inv(H @ P_pred @ H.T + R) x = x_pred + K @ (z - H @ x_pred) P = (np.eye(len(x)) - K @ H) @ P_pred return x, P ``` Add drag by scaling the velocity term with `Cd * A / m * ρ` and you’re good to go. Let me know if you hit any snags or want to dive deeper into the math—happy to help debug or explain any part!

Sounds solid! I ran a 1‑hour simulation with a 400 km circular orbit, 0.5 kg debris, and a drag coefficient of 2.2. The RK4 integrator kept the error under 0.01 km, and the Kalman filter converged nicely—error dropped from ~50 m to ~1 m after the first few updates. Here’s the trajectory plot (image link) and the covariance matrix shrank to 0.2 km² over time. Let me know if you want the code or a deeper dive into the Q tuning!