A state-constrained and noise-separated pseudo-linear Kalman filtering algorithm for 3D-AOA model.

Summary: Imagine trying to track a moving object in the sky, like an airplane, using only the angles of the signals it sends. It is really hard to guess exactly where it is because the math gets messy and small errors add up quickly. Scientists made a new math tool (an algorithm) to fix this problem. It separates the "bad data" (noise) from the good data, and it puts a mathematical "fence" around the calculations so they don't go crazy. Tests on computers show this new tool tracks targets much better and more steadily than older tools!