Elucidating the Auxiliary Particle Filter via Multiple Importance Sampling [Lecture Notes]

Enhanced Importance Sampling: Unscented Auxiliary Particle Filtering for Visual Tracking

The particle filter has attracted considerable attention in visual tracking due to its relaxation of the linear and Gaussian restrictions in the state space model. It is thus more flexible than the Kalman filter. However, the conventional particle filter uses system transition as the proposal distribution, leading to poor sampling efficiency and poor performance in visual tracking. It is not a ...

Efficient Aging-Aware SRAM Failure Probability Calculation via Particle Filter-Based Importance Sampling

Unscented Auxiliary Particle Filter Implementation of the Cardinalized Probability Hypothesis Density Filters

The probability hypothesis density (PHD) filter suffers from lack of precise estimation of the expected number of targets. The Cardinalized PHD (CPHD) recursion, as a generalization of the PHD recursion, remedies this flaw and simultaneously propagates the intensity function and the posterior cardinality distribution. While there are a few new approaches to enhance the Sequential Monte Carlo (S...

Lecture Notes on Elliptic Filter Design

These notes and related MATLAB functions are available from the web page:

Lecture Notes on Testing by Implicit Sampling

Building on the junta tester, we present a general methodology for constructing testers for properties of Boolean functions (of the form f : {0, 1} → {0, 1}) that can be approximated by small juntas. This methodology yields testers of low query complexity for many natural properties, which contain functions that depend on relatively few relevant variables; specifically, the query complexity is ...