CHMMBOX, version 1.2, Iead Rezek, Oxford University, Feb 2001
Matlab toolbox for max. aposteriori Estimation of two chain
Coupled Hidden Markov Models.
這是寫給作為軟件工程項目經理的書，原書的書評是：
"If you re looking for solid, easy-to-follow advice on Estimation, requirements gathering, managing change, and more, you can stop now: this is the book for you."
--Scott Berkun, Author of The Art of Project Management
A one-dimensional calibration object consists of three or more collinear points with known relative positions.
It is generally believed that a camera can be calibrated only when a 1D calibration object is in planar motion or rotates
around a ¯ xed point. In this paper, it is proved that when a multi-camera is observing a 1D object undergoing general
rigid motions synchronously, the camera set can be linearly calibrated. A linear algorithm for the camera set calibration
is proposed，and then the linear Estimation is further re¯ ned using the maximum likelihood criteria. The simulated and
real image experiments show that the proposed algorithm is valid and robust.
《多傳感器數據融合手冊》
《Handbook of Multisensor Data Fusion》
作者: David L. Hall
定價: USD 199.95
出版社: CRC
出版年: 2001-06-20
簡介 · · · · · ·
Multisensor data fusion is an emerging technology with important applications in both the military and civilian sectors, such as target recognition, robotics, medical diagnostics, and "smart" buildings. It draws on techniques from wide-ranging disciplines, including artificial intelligence, pattern recognition, and statistical Estimation. This handbook is an up-to-date, comprehensive resource for data fusion system designers and researchers. Top experts in the field lead readers from a basic introduction and survey of data fusion technology to advanced mathematics and theory and to some very practical advice for systems implementers.
The subroutines glkern.f and lokern.f use an efficient and fast algorithm for
automatically adaptive nonparametric regression Estimation with a kernel method.
Roughly speaking, the method performs a local averaging of the observations when
estimating the regression function. Analogously, one can estimate derivatives of
small order of the regression function.
In recent years large scientific interest has been
devoted to joint data decoding and parameter Estimation
techniques. In this paper, iterative turbo decoding joint
to channel frequency and phase Estimation is proposed.
The phase and frequency estimator is embedded into the
structure of the turbo decoder itself, taking into consideration
both turbo interleaving and puncturing. Results
show that the proposed technique outperforms conventional
approaches both in terms of detection capabilities and
implementation complexity.
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.
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.
