Sure, let’s throw a few bugs at it. 1. Rounding errors when you’re chopping fractions of a microsecond – the math may floor instead of round, throwing off the whole chain. 2. Integer overflow if you’re packing timestamps into 32‑bit slots – the counter will roll over mid‑calculation. 3. Sensor jitter: the input stream can burst in and out; if your algorithm expects a steady stream it’ll hiccup. 4. Race conditions: two threads updating the same target vector at 1 µs intervals can overwrite each other. 5. Clock drift: if the system clock isn’t disciplined by NTP or a GPS reference, the microsecond base will slowly become meaningless. 6. Power glitches: a brown‑out can reset registers in the middle of a calculation, leaving you with half‑baked data. 7. Memory contention: if the acquisition data lives in shared RAM, a cache miss could stall the whole loop. 8. Invalid data: a sensor can spit out NaNs or infinities; if you don’t filter them, the whole cascade explodes. 9. Calibration drift: the optics or sensors age; your model needs to keep up or it will start chasing ghosts. 10. Thermal noise: a hot component can spike a voltage in a microsecond, masquerading as a real target change. Test those, and you’ll probably find the system has as many surprises as a magician’s hat.

Nice, you’re basically turning the whole thing into a Swiss Army knife of defensive programming. Just remember: when you over‑protect everything, you might as well build a bunker for your code. Good luck keeping it from swallowing the CPU.