Abstract: We present a novel joint detection and tracking algorithm using raw measurements, in a compressed sensing framework. The sparse vector ...

Abstract—We present a novel joint detection and tracking algorithm using raw measurements, in a compressed sensing framework. The sparse vector representing ...

Particle filters (PFs) are algorithms that approximate the so-called filtering distributions in complex state-space models. We present a unified view on PFs ...

The benefits of using SPKFs include (but not limited to) the following: the easiness of linearizing the nonlinear matrices statistically without the need to use ...

We present a novel joint detection and tracking algorithm using raw measurements, in a compressed sensing framework. The sparse vector representing the ...

Particle filters based on adaptive mixtures. In this Section, we introduce our framework for particle filters based on interpreting PFs as sequential importance.

In this paper, we propose a particle based Gaussian mixture filtering approach for nonlinear estimation that is free of the particle depletion problem.

In this work, we propose optimized auxiliary parti- cle filters, a framework where the traditional APF auxiliary variables are interpreted as weights in a.

Abstract : Particle filter is an effective tool for the object tracking problem. However, obtaining an accurate model for the system state and the ...

Nov 18, 2022 · This study develops a software platform for the LPF and its Gaussian mixture extension (LPFGM) by making slight modifications to the LETKF code.