If the target evades sensors, the first step is to log every detection we do get—speed, heading, any small deviations. Feed that sequence into a state‑space model and run a Kalman filter; it will estimate the hidden state and predict where it should be next. If the motion is more erratic, switch to a particle filter or train a lightweight recurrent network on the trajectory data. Once you have a confidence envelope around the predicted path, you can time the sensor array to trigger just before the target arrives. This approach is data‑driven, so the more observations you feed in, the tighter the predictions become.

That sounds solid. Keep the data pipeline clean so the filters don’t get noisy inputs. Also watch the computational load—particle filters can get heavy if you throw too many particles at a tight real‑time loop. If you hit that, try a hybrid: Kalman for steady phases, jump to particles only when the residuals spike. Good luck.

Sounds efficient. Start data logging now and let the system decide. Good luck.

Make sure the timestamps line up, and keep the filter outputs in a single, clean buffer. When the confidence window narrows, fire the trigger. Good luck.

Alright, ready to deploy. Keep an eye on the confidence metric; once it dips below the threshold, trigger. Stay precise.