12,278 research outputs found
Nonlinear Compressive Particle Filtering
Many systems for which compressive sensing is used today are dynamical. The
common approach is to neglect the dynamics and see the problem as a sequence of
independent problems. This approach has two disadvantages. Firstly, the
temporal dependency in the state could be used to improve the accuracy of the
state estimates. Secondly, having an estimate for the state and its support
could be used to reduce the computational load of the subsequent step. In the
linear Gaussian setting, compressive sensing was recently combined with the
Kalman filter to mitigate above disadvantages. In the nonlinear dynamical case,
compressive sensing can not be used and, if the state dimension is high, the
particle filter would perform poorly. In this paper we combine one of the most
novel developments in compressive sensing, nonlinear compressive sensing, with
the particle filter. We show that the marriage of the two is essential and that
neither the particle filter or nonlinear compressive sensing alone gives a
satisfying solution.Comment: Accepted to CDC 201
Efficient delay-tolerant particle filtering
This paper proposes a novel framework for delay-tolerant particle filtering
that is computationally efficient and has limited memory requirements. Within
this framework the informativeness of a delayed (out-of-sequence) measurement
(OOSM) is estimated using a lightweight procedure and uninformative
measurements are immediately discarded. The framework requires the
identification of a threshold that separates informative from uninformative;
this threshold selection task is formulated as a constrained optimization
problem, where the goal is to minimize tracking error whilst controlling the
computational requirements. We develop an algorithm that provides an
approximate solution for the optimization problem. Simulation experiments
provide an example where the proposed framework processes less than 40% of all
OOSMs with only a small reduction in tracking accuracy
Moderate deviations for particle filtering
Consider the state space model (X_t,Y_t), where (X_t) is a Markov chain, and
(Y_t) are the observations. In order to solve the so-called filtering problem,
one has to compute L(X_t|Y_1,...,Y_t), the law of X_t given the observations
(Y_1,...,Y_t). The particle filtering method gives an approximation of the law
L(X_t|Y_1,...,Y_t) by an empirical measure \frac{1}{n}\sum_1^n\delta_{x_{i,t}}.
In this paper we establish the moderate deviation principle for the empirical
mean \frac{1}{n}\sum_1^n\psi(x_{i,t}) (centered and properly rescaled) when the
number of particles grows to infinity, enhancing the central limit theorem.
Several extensions and examples are also studied.Comment: Published at http://dx.doi.org/10.1214/105051604000000657 in the
Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute
of Mathematical Statistics (http://www.imstat.org
ILAPF: Incremental Learning Assisted Particle Filtering
This paper is concerned with dynamic system state estimation based on a
series of noisy measurement with the presence of outliers. An incremental
learning assisted particle filtering (ILAPF) method is presented, which can
learn the value range of outliers incrementally during the process of particle
filtering. The learned range of outliers is then used to improve subsequent
filtering of the future state. Convergence of the outlier range estimation
procedure is indicated by extensive empirical simulations using a set of
differing outlier distribution models. The validity of the ILAPF algorithm is
evaluated by illustrative simulations, and the result shows that ILAPF is more
accurate and faster than a recently published state-ofthe-art robust particle
filter. It also shows that the incremental learning property of the ILAPF
algorithm provides an efficient way to implement transfer learning among
related state filtering tasks.Comment: 5 pages, 4 figures, conferenc
PPF - A Parallel Particle Filtering Library
We present the parallel particle filtering (PPF) software library, which
enables hybrid shared-memory/distributed-memory parallelization of particle
filtering (PF) algorithms combining the Message Passing Interface (MPI) with
multithreading for multi-level parallelism. The library is implemented in Java
and relies on OpenMPI's Java bindings for inter-process communication. It
includes dynamic load balancing, multi-thread balancing, and several
algorithmic improvements for PF, such as input-space domain decomposition. The
PPF library hides the difficulties of efficient parallel programming of PF
algorithms and provides application developers with the necessary tools for
parallel implementation of PF methods. We demonstrate the capabilities of the
PPF library using two distributed PF algorithms in two scenarios with different
numbers of particles. The PPF library runs a 38 million particle problem,
corresponding to more than 1.86 GB of particle data, on 192 cores with 67%
parallel efficiency. To the best of our knowledge, the PPF library is the first
open-source software that offers a parallel framework for PF applications.Comment: 8 pages, 8 figures; will appear in the proceedings of the IET Data
Fusion & Target Tracking Conference 201
Semi-independent resampling for particle filtering
Among Sequential Monte Carlo (SMC) methods,Sampling Importance Resampling
(SIR) algorithms are based on Importance Sampling (IS) and on some
resampling-based)rejuvenation algorithm which aims at fighting against weight
degeneracy. However %whichever the resampling technique used this mechanism
tends to be insufficient when applied to informative or high-dimensional
models. In this paper we revisit the rejuvenation mechanism and propose a class
of parameterized SIR-based solutions which enable to adjust the tradeoff
between computational cost and statistical performances
- …