This paper examines the asymptotic (large sample) performance
of a family of non-data aided feedforward (NDA FF) nonlinear
least-squares (NLS) type carrier frequency estimators for burst-mode
phase shift keying (PSK) modulations transmitted through AWGN and
flat Ricean-fading channels. The asymptotic performance of these estimators
is established in closed-form expression and compared with the
modified Cram`er-Rao bound (MCRB). A best linear unbiased estimator
(BLUE), which exhibits the lowest asymptotic variance within the family
of NDA FF NLS-type estimators, is also proposed.
We present a particle filter construction for a system that exhibits
time-scale separation. The separation of time-scales allows two simplifications
that we exploit: i) The use of the averaging principle for the
dimensional reduction of the system needed to solve for each particle
and ii) the factorization of the transition probability which allows the
Rao-Blackwellization of the filtering step. Both simplifications can be
implemented using the coarse projective integration framework. The
resulting particle filter is faster and has smaller variance than the particle
filter based on the original system. The convergence of the new
particle filter to the analytical filter for the original system is proved
and some numerical results are provided.
matlab 實現(xiàn)系統(tǒng)的參數(shù)計算，系統(tǒng)單位階躍響應的相關(guān)參數(shù)計算。Matlab real system parameters, the system unit step response of the relevant parameters.
Example - 3-D Stem Plot of an FFTFor example, fast Fourier transforms are calculated at points around the unit circle on the complex plane. So, it is interesting to visualize the plot around the unit circle. Calculating the unit circle.
The result is an IS-95CDMA forward link software simulation package ,which mimics real-time data communication from a basestation to a cellular unit. The package simulates an
IS-95CDMA forward link cellular system consisting of 3 major components:Transmitter,
Communication Channel and Receiver.
PlotSphereIntensity(azimuth, elevation)
PlotSphereIntensity(azimuth, elevation, intensity)
h = PlotSphereIntensity(...)
Plots the intensity (as color) of a number of points on a unit sphere.
Input:
azimuth (phi), in degrees
elevation (theta), in degrees
intensity (optional, if not provided, a green sphere is produced)
All inputs must be vectors or matrices of the same size. Data does not have to be evenly spaced. When there aren t enough points to draw a smooth sphere, additional points (with color) are interpolated.
Output:
h - a handle to the patch object
The axes are also plotted:
positive x axis is red
positive y axis is green
positive z axis is blue
Probabilistic Principal Components Analysis. [VAR, U, LAMBDA] = PPCA(X, PPCA_DIM) computes the principal
％ component subspace U of dimension PPCA_DIM using a centred covariance
matrix X. The variable VAR contains the off-subspace variance (which
is assumed to be spherical), while the vector LAMBDA contains the
variances of each of the principal components. This is computed
using the eigenvalue and eigenvector decomposition of X.
The MDP toolbox proposes functions related to the resolution of discrete-time Markov Decision Process : finite horizon, value iteration, policy iteration, linear programming algorithms with some variants.
The functions (m-functions) were developped with MATLAB v6.0 (one of the functions requires the Mathworks Optimization Toolbox) by the decision team of the Biometry and Artificial Intelligence Unit of INRA Toulouse (France).
The version 2.0 (February 2005) handles sparse matrices and contains an example
