18,304 research outputs found
Efficient and Accurate Frequency Estimation of Multiple Superimposed Exponentials in Noise
The estimation of the frequencies of multiple superimposed exponentials in
noise is an important research problem due to its various applications from
engineering to chemistry. In this paper, we propose an efficient and accurate
algorithm that estimates the frequency of each component iteratively and
consecutively by combining an estimator with a leakage subtraction scheme.
During the iterative process, the proposed method gradually reduces estimation
error and improves the frequency estimation accuracy. We give theoretical
analysis where we derive the theoretical bias and variance of the frequency
estimates and discuss the convergence behaviour of the estimator. We show that
the algorithm converges to the asymptotic fixed point where the estimation is
asymptotically unbiased and the variance is just slightly above the Cramer-Rao
lower bound. We then verify the theoretical results and estimation performance
using extensive simulation. The simulation results show that the proposed
algorithm is capable of obtaining more accurate estimates than state-of-art
methods with only a few iterations.Comment: 10 pages, 10 figure
Image interpolation using Shearlet based iterative refinement
This paper proposes an image interpolation algorithm exploiting sparse
representation for natural images. It involves three main steps: (a) obtaining
an initial estimate of the high resolution image using linear methods like FIR
filtering, (b) promoting sparsity in a selected dictionary through iterative
thresholding, and (c) extracting high frequency information from the
approximation to refine the initial estimate. For the sparse modeling, a
shearlet dictionary is chosen to yield a multiscale directional representation.
The proposed algorithm is compared to several state-of-the-art methods to
assess its objective as well as subjective performance. Compared to the cubic
spline interpolation method, an average PSNR gain of around 0.8 dB is observed
over a dataset of 200 images
A Novel Data-Aided Channel Estimation with Reduced Complexity for TDS-OFDM Systems
In contrast to the classical cyclic prefix (CP)-OFDM, the time domain
synchronous (TDS)-OFDM employs a known pseudo noise (PN) sequence as guard
interval (GI). Conventional channel estimation methods for TDS-OFDM are based
on the exploitation of the PN sequence and consequently suffer from intersymbol
interference (ISI). This paper proposes a novel dataaided channel estimation
method which combines the channel estimates obtained from the PN sequence and,
most importantly, additional channel estimates extracted from OFDM data
symbols. Data-aided channel estimation is carried out using the rebuilt OFDM
data symbols as virtual training sequences. In contrast to the classical turbo
channel estimation, interleaving and decoding functions are not included in the
feedback loop when rebuilding OFDM data symbols thereby reducing the
complexity. Several improved techniques are proposed to refine the data-aided
channel estimates, namely one-dimensional (1-D)/two-dimensional (2-D) moving
average and Wiener filtering. Finally, the MMSE criteria is used to obtain the
best combination results and an iterative process is proposed to progressively
refine the estimation. Both MSE and BER simulations using specifications of the
DTMB system are carried out to prove the effectiveness of the proposed
algorithm even in very harsh channel conditions such as in the single frequency
network (SFN) case
- …