2,043 research outputs found
Latent parameter estimation in fusion networks using separable likelihoods
Multi-sensor state space models underpin fusion applications in networks of
sensors. Estimation of latent parameters in these models has the potential to
provide highly desirable capabilities such as network self-calibration.
Conventional solutions to the problem pose difficulties in scaling with the
number of sensors due to the joint multi-sensor filtering involved when
evaluating the parameter likelihood. In this article, we propose a separable
pseudo-likelihood which is a more accurate approximation compared to a
previously proposed alternative under typical operating conditions. In
addition, we consider using separable likelihoods in the presence of many
objects and ambiguity in associating measurements with objects that originated
them. To this end, we use a state space model with a hypothesis based
parameterisation, and, develop an empirical Bayesian perspective in order to
evaluate separable likelihoods on this model using local filtering. Bayesian
inference with this likelihood is carried out using belief propagation on the
associated pairwise Markov random field. We specify a particle algorithm for
latent parameter estimation in a linear Gaussian state space model and
demonstrate its efficacy for network self-calibration using measurements from
non-cooperative targets in comparison with alternatives.Comment: accepted with minor revisions, IEEE Transactions on Signal and
Information Processing Over Network
Sequential Quasi-Monte Carlo
We derive and study SQMC (Sequential Quasi-Monte Carlo), a class of
algorithms obtained by introducing QMC point sets in particle filtering. SQMC
is related to, and may be seen as an extension of, the array-RQMC algorithm of
L'Ecuyer et al. (2006). The complexity of SQMC is , where is
the number of simulations at each iteration, and its error rate is smaller than
the Monte Carlo rate . The only requirement to implement SQMC is
the ability to write the simulation of particle given as a
deterministic function of and a fixed number of uniform variates.
We show that SQMC is amenable to the same extensions as standard SMC, such as
forward smoothing, backward smoothing, unbiased likelihood evaluation, and so
on. In particular, SQMC may replace SMC within a PMCMC (particle Markov chain
Monte Carlo) algorithm. We establish several convergence results. We provide
numerical evidence that SQMC may significantly outperform SMC in practical
scenarios.Comment: 55 pages, 10 figures (final version
2D articulated tracking with dynamic Bayesian networks
©2004 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.We present a novel method for tracking the motion of an articulated structure in a video sequence. The analysis of articulated motion is challenging because of the potentially large number of degrees of freedom (DOFs) of an articulated body. For particle filter based algorithms, the number of samples required with high dimensional problems can be computationally prohibitive. To alleviate this problem, we represent the articulated object as an undirected graphical model (or Markov Random Field, MRF) in which soft constraints between adjacent subparts are captured by conditional probability distributions. The graphical model is extended across time frames to implement a tracker. The tracking algorithm can be interpreted as a belief inference procedure on a dynamic Bayesian network. The discretisation of the state vectors makes it possible to utilise the efficient belief propagation (BP) and mean field (MF) algorithms to reason in this network. Experiments on real video sequences demonstrate that the proposed method is computationally efficient and performs well in tracking the human body.Chunhua Shen, Anton van den Hengel, Anthony Dick, Michael J. Brook
Evolutionary computing and particle filtering: a hardware-based motion estimation system
Particle filters constitute themselves a highly powerful estimation tool, especially when dealing with non-linear non-Gaussian systems. However, traditional approaches present several limitations, which reduce significantly their performance. Evolutionary algorithms, and more specifically their optimization capabilities, may be used in order to overcome particle-filtering weaknesses. In this paper, a novel FPGA-based particle filter that takes advantage of evolutionary computation in order to estimate motion patterns is presented. The evolutionary algorithm, which has been included inside the resampling stage, mitigates the known sample impoverishment phenomenon, very common in particle-filtering systems. In addition, a hybrid mutation technique using two different mutation operators, each of them with a specific purpose, is proposed in order to enhance estimation results and make a more robust system. Moreover, implementing the proposed Evolutionary Particle Filter as a hardware accelerator has led to faster processing times than different software implementations of the same algorithm
Markov and Semi-markov Chains, Processes, Systems and Emerging Related Fields
This book covers a broad range of research results in the field of Markov and Semi-Markov chains, processes, systems and related emerging fields. The authors of the included research papers are well-known researchers in their field. The book presents the state-of-the-art and ideas for further research for theorists in the fields. Nonetheless, it also provides straightforwardly applicable results for diverse areas of practitioners
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