statespace 1.3.38

state-space distributions and decisions

reference problems from

Bayesian Signal Processing: Classical, Modern, and Particle Filtering Methods, James V. Candy

Kalman Filtering: Theory and Practice, Mohinder S. Grewal, Angus P. Andrews

Stochastic Processes and Filtering Theory, Jazwinski

build-test-deploy to pypi is mostly a placeholder, ubuntu clone-install-develop of gitlab repo is assumed for now.

sudo apt-get -qq update -qy
sudo apt-get -qq install -y python3.6 python3-venv python3-pip
git clone [e-mail]:noahhsmith/statespace.git statespace
cd statespace
python3 -m venv venv
. venv/bin/activate
python3 setup.py develop
pytest
python3 statespace --demo

210221

brought the documentation via readthedocs up to a minimal level. cleaned up the project and brought some focus to what's going on here. as the docs now make clear - this project focuses on reference problems from Bayesian Signal Processing: Classical, Modern, and Particle Filtering Methods, Kalman Filtering: Theory and Practice, and Stochastic Processes and Filtering Theory - in particular, using numpy for matrix and vector manipulation.

190418

brief lit-review posted on linkedin.

190331

concise motivation piece posted on linkedin.

190310

decision-function-based detector is go. simplest possible case - linear rc-circuit system-model and linear kalman-filter tracker. log-likelihood decision function for detection, ensembles of 100 runs each for signal case and noise case. output curves shown in the first plot - green signal, blue noise-only. roc curves in the second plot.

190223

kl-divergence for evaluating sequential monte-carlo - demonstrated below by three pf's in action during the first second of the jazwinksi problem - start-up and convergence. these are 100 hz dist-curves - each dist-curve is a kernel-density-estimate combining hundreds of monte-carlo samples, the fundamental-particles - green dist-curves for truth, blue dist-curves for pf. state-estimates are two red curves on the x,t-plane beneath the dist-curves.

190105

ukf adaptive jazwinksi switched to square-root filtering, qr-factorization, cholesky-factor update and downdate. improved numerical stability and scaled sampling is clear. still a question around scalar-obs and the obs cholesky-factor and gain. with an adhoc stabilizer on the obs cholesky-factor it's working well overall.

181230

pf adaptive jazwinksi. parameter-roughening.

181226

ukf adaptive jazwinski. sample-and-propagate tuning.

180910

ekf adaptive jazwinski. ud-factorized square-root filtering required for numerical stability.