10 articles in this category
You've tuned your Kalman filter for weeks. The noise matrices are polished, the model feels right. Then your robot drifts left by a meter, or your sen...
So your particle filter just collapsed to a single point. Not the kind of collapse that simplifies debugging—the kind where your filter forgets every ...
Adaptive stochastic control sounds like a cure-all. When a system changes—drift in a chemical reactor, turbulence on an aircraft wing—the controller t...
You've coded up an Ensemble Kalman Filter. It runs. But the estimates drift, or the filter blows up, or the analysis looks like a mess. Two knobs prom...
Noise models are the lens through which you see your system. Pick the wrong one, and your filter will track the noise instead of the signal. I've seen...
You've been here: running a Kalman filter, everything looks fine, then suddenly the covariance matrix throws a 'not positive definite' error. Your fil...
You are sitting in a control room. Data streams in—noisy, delayed, sometimes missing. The framework you are responsible for must act now, not after yo...
Stochastic control and filtering form the backbone of decision-making under uncertainty. Think of a self-driving car inferring the position of a pedes...
So your particle filter is dead. You threw 10,000 particles at a 20-dimensional state space, watched the weights collapse to one particle after three ...
Nonlinear filtered is a messy business. You inherit a model from the literature, throw in a particle filter, and watch it diverge after twenty steps. ...