<![CDATA[ IET Signal Processing - new TOC ]]>
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TOC Alert for Publication# 4159607 2017May 25<![CDATA[LMMSE channel estimation in OFDM context: a review]]>1121231344596<![CDATA[Alternating projection for sparse recovery]]>s-sparse original signal provided the sensing matrix satisfies several assumptions when the noise is absent. They also prove that AP method is a noise-robust algorithm, i.e. a tolerable reconstruction can be obtained by AP if the noise is small. In numerical experiments, the authors compare AP with several existing algorithms when being applied to sparse signals recovery and images reconstruction. The results demonstrate the efficiency of the proposed algorithm.]]>1121351444556<![CDATA[Approximating the standard condition number for cognitive radio spectrum sensing with finite number of sensors]]>0 and ℋ_{1} hypotheses. Due to the complexity of these expressions, the authors approximate the distribution of the SCN by the generalised extreme value distribution using moment matching. They derive the exact form of the pth moment of the SCN for these cases. Consequently, the performance probabilities are approximated and a simple decision threshold formula is provided. In addition, a similar approximation for the detection probability is provided using non-central/central approximation. They show that the proposed analytical approximations provide high accuracy using Monte-Carlo simulations.]]>1121451541660<![CDATA[Compressed sensing-based ground MTI with clutter rejection scheme for synthetic aperture radar]]>1121551644578<![CDATA[Event-triggered state estimator for stochastic systems with unknown inputs]]>1121651701257<![CDATA[Non-convex block-sparse compressed sensing with redundant dictionaries]]>2k|τ <; 1, a sufficient condition for robust signal reconstruction with redundant dictionaries by mixed ℓ_{2}/ℓ_{p}(0 <; p <; 1) minimisation is established. Furthermore, the authors' theoretical results show that, under the assumption that (√2/2) ≤ δ_{2k|τ} <; 1, p ∈ (0,p̂], where p̂ = {1.6835(1-δ_{2k|τ}), δ_{2k|τ} ∈[[√2]/2,0.73) 0.45418, δ_{2k|τ} ∈(0.73,0.7983) 2.2522(1-δ_{2k|τ}), δ_{2k|τ} ∈[0.7983,1), then the block k-sparse signal can be stably reconstructed via non-convex ℓ_{2}/ℓ_{p} minimisation with redundant dictionaries in the presence of noise. Particularly, this improves the existed result when the block-sparse signal degenerate to the conventional signal case. Besides, the authors also obtain robust reconstruction condition and error upp-
r bound estimation when the block number is no more than four times the sparsity of the block signal (d ≤ 4k). Moreover, the numerical experiments to some extent testify the performance of non-convex ℓ_{2}/ℓ_{p}(0 <; p <; 1) minimisation with redundant dictionaries.]]>1121711802339<![CDATA[Optimal and accurate design of fractional-order digital differentiator – an evolutionary approach]]>11218119610965<![CDATA[Linear estimators for networked systems with one-step random delay and multiple packet dropouts based on prediction compensation]]>1121972042951<![CDATA[Implementation of wideband digital transmitting beamformer based on LFM waveforms]]>1122052122638<![CDATA[Time–frequency analysis method based on affine Fourier transform and Gabor transform]]>1122132202314<![CDATA[Depth of anaesthesia assessment using interval second-order difference plot and permutation entropy techniques]]>1122212273816<![CDATA[Parameter estimation algorithms for dynamical response signals based on the multi-innovation theory and the hierarchical principle<?show [AQ ID=Q1]?>]]>1122282371403