Carrier-phase synchronization can be approached in a
general manner by estimating the multiplicative distortion (MD) to which
a baseband received signal in an RF or coherent optical transmission
system is subjected. This paper presents a unified modeling and
estimation of the MD in finite-alphabet digital communication systems. A
simple form of MD is the camer phase exp GO) which has to be estimated
and compensated for in a coherent receiver. A more general case with
fading must, however, allow for amplitude as well as phase variations of
the MD.
We assume a state-variable model for the MD and generally obtain a
nonlinear estimation problem with additional randomly-varying system
parameters such as received signal power, frequency offset, and Doppler
spread. An extended Kalman filter is then applied as a near-optimal
solution to the adaptive MD and channel parameter estimation problem.
Examples are given to show the use and some advantages of this scheme.
Aiming at the application of passive trackinn based on sensor array, a new passive trackinn usinn sensor array
based on particle filter was proposed. Firstly, the“fake points" could be almost entirely and exactly deleted with the aids of the
sensor array at the expense of an additional sensor. Secondly, considered the fact that the measurements notten from each array
were independent in passive trackinn system, a novel sequential particle filter usinn sensor array with improved distribution was proposed. At last, in a simulation study we compared this approach a壇orithm with traditional trackinn methods. The simulation re-sups show that the proposed method can nreatly improve the state estimation precision of sensor array passive trackinn system.
In this article, we present an overview of methods for sequential simulation from posterior distributions.
These methods are of particular interest in Bayesian filtering for discrete time dynamic models
that are typically nonlinear and non-Gaussian. A general importance sampling framework is developed
that unifies many of the methods which have been proposed over the last few decades in several
different scientific disciplines. Novel extensions to the existing methods are also proposed.We showin
particular how to incorporate local linearisation methods similar to those which have previously been
employed in the deterministic filtering literature these lead to very effective importance distributions.
Furthermore we describe a method which uses Rao-Blackwellisation in order to take advantage of
the analytic structure present in some important classes of state-space models. In a final section we
develop algorithms for prediction, smoothing and evaluation of the likelihood in dynamic models.
We propose a novel approach for head tracking, which combines particle filters with Isomap. The particle filter works on the low-dimensional embedding of training images. It indexes into the Isomap with its state variables to find the closest template for each particle. The most weighted particle approximates the location of head. We develop a synthetic video sequence to test our technique. The results we get show that the tracker tracks the head which changes position, poses and lighting conditions.
In this paper, we consider the problem of filtering in relational
hidden Markov models. We present a compact representation for
such models and an associated logical particle filtering algorithm. Each
particle contains a logical formula that describes a set of states. The
algorithm updates the formulae as new observations are received. Since
a single particle tracks many states, this filter can be more accurate
than a traditional particle filter in high dimensional state spaces, as we
demonstrate in experiments.
Rao-Blackwellised Particle Filters (RBPFs) are a class of Particle
Filters (PFs) that exploit conditional dependencies between
parts of the state to estimate. By doing so, RBPFs can
improve the estimation quality while also reducing the overall
computational load in comparison to original PFs. However,
the computational complexity is still too high for many
real-time applications. In this paper, we propose a modified
RBPF that requires a single Kalman Filter (KF) iteration per
input sample. Comparative experiments show that while good
convergence can still be obtained, computational efficiency is
always drastically increased, making this algorithm an option
to consider for real-time implementations.
The need for accurate monitoring and analysis of sequential data arises in many scientic, industrial
and nancial problems. Although the Kalman lter is effective in the linear-Gaussian
case, new methods of dealing with sequential data are required with non-standard models.
Recently, there has been renewed interest in simulation-based techniques. The basic idea behind
these techniques is that the current state of knowledge is encapsulated in a representative
sample from the appropriate posterior distribution. As time goes on, the sample evolves and
adapts recursively in accordance with newly acquired data. We give a critical review of recent
developments, by reference to oil well monitoring, ion channel monitoring and tracking
problems, and propose some alternative algorithms that avoid the weaknesses of the current
methods.
To estimate the input-output mapping with inputs x
％ and outputs y generated by the following nonlinear,
％ nonstationary state space model:
％ x(t+1) = 0.5x(t) + [25x(t)]/[(1+x(t))^(2)]
％ + 8cos(1.2t) + process noise
％ y(t) = x(t)^(2) / 20 + 6 squareWave(0.05(t-1)) + 3
％ + time varying measurement noise
％ using a multi-layer perceptron (MLP) and both the EKF and
％ the hybrid importance-samping resampling (SIR) algorithm.
State_space_reconstruction_parameters_in_the_analysis_of_chaotic_time_series_-_the_role_of_the_time_window_length.
It is used for reconstruction of state space in chaotic time series, and also how to determine time window.
