Convergence Results for the Gaussian Mixture.
In this section, we provide the results from experiments on simulation data to evaluate the performance of the proposed DCPPHD filter. The DCPPHD filter is compared to two traditional particle PHD filters with different particle numbers. The number of particles in DCPPHD is the same as that of the first particle PHD filter (PHD1).
This compact, informal introduction for graduate students and advanced undergraduates presents the current state-of-the-art filtering and smoothing methods in a unified Bayesian framework. Readers learn what non-linear Kalman filters and particle filters are, how they are related, and their relative advantages and disadvantages.
Their combined citations are counted only for the first article.. A survey of convergence results on particle filtering methods for practitioners. D Crisan, A Doucet. IEEE Transactions on signal processing 50 (3), 736-746, 2002. 1123: 2002: Particle filters for state estimation of jump Markov linear systems. A Doucet, NJ Gordon, V.
Resampling is a critical procedure that is of both theoretical and practical significance for efficient implementation of the particle filter. To gain an insight of the resampling process and the.
Resampling methods for particle filtering: identical distribution, a new method, and comparable study. Author(s): Tian-cheng Li, Gabriel Villarrubia, Shu-dong Sun, Juan M. Corchado, Javier Bajo.
The analysis results show the lower complexity, more amenable for parallel implementation of the GSCPHD filter than the convolution PHD (CPHD) filter and the ability to deal with complex observation model, small observation noise of the proposed filter over the existing Gaussian Mixture Particle PHD (GMPPHD) filter.
The weak convergence results obtained in this thesis and in the research articles which are incorporated in this thesis, respectively, are -- to the best of our knowledge -- the first results in the scientific literature which establish essentially sharp weak convergence rates for numerical approximations of the continuous version of the PAM.