state-space distributions and decisions
Project description
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
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.
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.
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