4 ms·

I mayve read the presentation wrong but it says 3s/iteration on the MHE. That’s insane. I’ve run close to real time UKF for nav and control applications so it’s

• touisteur 6y ago
  Right now, yes. There was a time where doppler filtering was too expensive in programmable COTS HW, or digital beamforming, or SVD... What's missing here is the gain vs optimisation effort. If there is an order of magnitude of gain here, we'll reduce the latency one way or the other. I see impossible-20-years-ago heavy-duty solvers in RT sensors more and more. Also depending on your refresh rate 3s might still be worth it IF the filter gain is so much better.
• wenc 6y ago
  For real time mechanical and electrical applications, the MHE is generally not competitive (at present) However, there many types of dynamic systems that are much slower like chemical plants (minute to hour level control) that can accommodate MHE’s execution time scales. If you need to simultaneously handle nonlinearities and constraints, it may be a superior choice. Also there are potential ways to make MHE run faster. For certain forms of the optimization problem (like a linear or quadratic program), it’s possible to convert it to a lookup table (search for “explicit MPC”) which can then be implemented in an embedded system for fast lookups. That said, I agree with you in practice. An nonlinear optimization problem tends to be a numerical black box and can be hard to diagnose when things go wrong. The Kalman family of algorithms is much more straightforward (only involves function evaluations), faster and have failure modes that are much more easily understood. They just don’t do so well with constraints.