Table of Content

25 March 2013, Volume 31 Issue 2

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Communication Engineering

Cooperative Relay Transmission Scheme Based on Interference Cancelation

LI Guo-you, ZHOU Ya-jian, YUAN Quan, YANG Yi-xian

2013, 31(2):  111-115.  doi:10.3969/j.issn.0255-8297.2013.02.001

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In order to improve system achievable rate and throughput for wireless systems, this paper proposes a cooperative relay transmission scheme with interference cancelation. By overhearing interference signal as a priori information, the proposed scheme uses interference reconstruction and subtraction to cancel interference,reducing the time slot. Moreover, the paper derives the system achievable rate and outage probability expressions, and analyses the performance of different transmission schemes in diverse relay modes. Simulation results show that the proposed scheme can effectively improve the system achievable rate as the link signal-tonoise ratio (SNR) increases. By adjusting the transmission rate, optimal system throughput is obtained when
the link SNR at the receiver is unknown.

Cooperative Spectrum Sensing Algorithm with Soft Fusion Based on Weighting

LIU Xin, TAN Xue-zhi, ZHAO Ying

2013, 31(2):  116-122.  doi:10.3969/j.issn.0255-8297.2013.02.002

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To improve the spectrum sensing performance, this paper proposes cooperative spectrum sensing that fuses the sensing information from every cognitive radio with soft fusion based on weighting. To reduce interference to the primary user and improve throughput of cognitive radio, two kinds of weight selection
methods that minimize interference and maximize throughput are proposed. As large weighting is allocated to the cognitive radios with high SNR or low channel gain to the coordinator, proportion of the sensing information sent by the cognitive radio with high SNR to the fusion information at the coordinator is increased. Lost information due to channel fading is compensated. Simulation results show that performance of the algorithm is better than those based on SNR weighting and those without weighting. The proposed algorithm can also reduce influences of the fading channel on detection performance. The results show that, by selecting appropriate weights, interference can be reduced and throughput increased.

Multi-hop Relay Transmission Scheme Using Digital Fountain Code and Rotated Constellation

LEI Wei-jia, LIU Hui-feng, XIE Xian-zhong

2013, 31(2):  123-129.  doi:10.3969/j.issn.0255-8297.2013.02.003

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In multi-hop relay transmission, digital fountain codes can greatly improve transmission efficiency. A multi-hop relay transmission scheme based on fountain codes is proposed, which improves data relay performance in two ways: 1) Two relay nodes form a relay pair, and by using rotated constellation modulation,
signal space diversity gain is obtained without sacrificing bandwidth efficiency. 2) An improved encoded packets linear combination algorithm is used to rebuild the encoded packets by the relay to realize packet diversity. Simulation results show that transmission efficiency of the multi-hop relay transmission is improved considerably, and transmission time reduced.

Transform Domain Anti-jamming Scheme for Spacecraft Payload in Satellite Communications

BU Xiang-yuan, GUO Yu-kun, HAO Shi-guang

2013, 31(2):  130-134.  doi:10.3969/j.issn.0255-8297.2013.02.004

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With the development of modern satellite communication, integration of the functions of the satellite payload has significantly raised requirements of the hardware costs. This paper proposes an improved FFT algorithm that uses self-adapting truncation at each level of the FFT process to achieve a lager dynamic range of the signal’s spectrum as compared to the conventional FFT implementation. The improved algorithm has been applied in an inter-satellite link of a satellite navigation system with good anti-jamming performance, large dynamic range, and low complexity.

Minimum Distance-Based MIMO Precoding for Triple-Stream Transmission

GU Qiang1, XU Xiao-dong1, JIANG Yi2, DAI Xu-chu1

2013, 31(2):  135-141.  doi:10.3969/j.issn.0255-8297.2013.02.005

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A new MIMO precoding method is proposed based on maximizing the minimum Euclidean distance between points in the received signal space for triple-stream transmission. Under a real-valued rotation matrix constraint, the proposed algorithm solves the non-convex optimization problem with two steps from
the geometric viewpoint, named offline step and online step, respectively. In the offline step, all feasible surfaces are collected by expanding quadric surfaces within 3-dimensional space. In the online step, the precoder matrix is constructed with respect to the unique feasible surface chosen by particular channel implementation. Meanwhile, a closed-form expression of the minimum distance is also provided in this paper. Simulation results validate that the proposed method not only outperforms existing schemes with low computational complexity, but also has superior performance of more than 3 dB in terms of bit-error-rate (BER) with increase of the quadrature amplitude modulation (QAM) constellations.

Optimization of Time-Frequency Synchronization in OFDM System

ZHAO Jun-hui1,2, Lü Chen1, TAN Guo-ping3

2013, 31(2):  142-146.  doi:10.3969/j.issn.0255-8297.2013.02.006

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This paper studies joint synchronization in OFDM systems under multipath fading, and analyzes the reason of timing error in the existing algorithms. An optimized joint synchronization algorithm using PN sequence is proposed. The algorithm achieves time synchronization by looking for the first falling edge
of the sliding correlation sum value, chooses a size of the search window for the correlation sum value. Modified correlation calculation is introduced in the frequency synchronization. Simulation results show stability and superiority of the new algorithm under multipath environment, especially when the first path is not the strongest.

Signal and Information Processing

Weighted Global Least Square Navigation Based on Measurable Image Sequence

ZHANG Xiao-dong1,2, YANG Yuan-xi2, HU Qing-wu3, WANG Yuan-ming2

2013, 31(2):  147-153.  doi:10.3969/j.issn.0255-8297.2013.02.007

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 A measurable sequential images positioning and attitude determination model which considered the coeffient matrix error of observing equation and its weight determination mothod is presented. The method can realize measurable image sequence aided DGPS/IMU navigation with reliable accuracy. Experiment results show that the proposed algorithm can overcome the effects of coefficient matrix errors with accuracy better than the traditional least square method. With the GPS outage in 2.5 km, the position accuracy is better than 13 m and the attitude error is better than 0.1m.

Error Calibration of Three-Axis Magnetic Heading Sensor

FENG Wen-guang, LIU Shi-bin, LI Ju-ping, GUO Bo, YANG Shang-lin

2013, 31(2):  154-158.  doi:10.3969/j.issn.0255-8297.2013.02.008

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An improved least-square ellipsoid fitting method for calibrating of magnetic heading sensor is proposed to overcome instability of the traditional ellipsoid fitting algorithm due to singularity of the constraint matrix. The source of error and the mathematical model are discussed. A method of matrix block
decomposition is used to solve the singularity problem of constraint matrix in the traditional algorithm. The ellipsoid parameter is calculated using the least-square algorithm. The proposed method overcomes instability of the traditional algorithm and reduces computation complexity. Simulation and experiments show that the proposed method is effective in magnetic heading sensor calibration. The precision of calibrated heading is better than 0.42m.

Khatri-Rao Subspace Wideband DOA Estimation for Nonlinear Array

PAN Jie, ZHOU Jian-jiang

2013, 31(2):  159-164.  doi:10.3969/j.issn.0255-8297.2013.02.009

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A Khatri-Rao subspace based wideband direction-of-arrive (DOA) estimation algorithm for nonlinear arrays without preliminary angle estimation is proposed. From steering vectors of the Khatri-Rao subspace virtual array, the wideband focusing matrix regardless of DOAs is constructed with a manifold separation technique.Benefited from the increased dimensions of the Khatri-Rao subspace virtual array, preliminary angle estimation can be avoided and the algorithm still performs well. On the other hand, by using Root-MUSIC, this method can avoid expensive spectrum searching used in conventional methods so as to reduce the computational burden. Simulations show that performance of the proposed method is close to the preliminary angle estimation needed Khatri-Rao subspace wideband DOA estimation algorithm, FKR-RSS. The proposed method performs better than FKR-RSS when the number of sources is more than the number of sensors.

Fast Maximum Likelihood Estimation of Class A Model

JIANG Yu-zhong, YING Wen-wei, LIU Yue-liang

2013, 31(2):  165-169.  doi:10.3969/j.issn.0255-8297.2013.02.010

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This paper investigates the Class A noise model, and proposes a method to determine parameters of the model based on maximum likelihood estimation. The method uses FFT to reduce computation complexity and enhance performance by calculating two data groups from the original observed data. A method for estimating initial values is also proposed. Simulation results show that the method has good performance with a small number of iterations, and therefore is suitable for practical applications.

No-reference Image Quality Assessment Based on Parameters of Wavelet Coefficients Distribution

LI Hong-jun

2013, 31(2):  170-176.  doi:10.3969/j.issn.0255-8297.2013.02.011

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We propose a no-reference method for image quality assessment to be used to various types of image distortions. We assess the image using statistic information of natural scenes, and use the generalized Gaussian density model to fit the marginal distribution of wavelet coefficients. Degree of image distortion is measured with the parameter values in the generalized Gaussian density model. Objective assessment of the image quality is obtained by quantifying the difference between the values of scale and shape parameters. Experimental results are consistent with subjective assessments, showing that the proposed method can be applied to most common types of image distortion to give good prediction.

Compressed Sensing Sparse Recognition for Target Classification from Multi-view Images

LIU Ji-xin, SUN Quan-sen, CAO Guo

2013, 31(2):  177-182.  doi:10.3969/j.issn.0255-8297.2013.02.012

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Multi-view image target classification is usually difficult. To deal with the problem, we propose a sparse recognition (SR) method with compressed sensing (CS) features. Sensing data of the original image are used as corresponding features. Both the test sample and the training sample set are integrated into an SR framework with their CS features. Classification results can be obtained by solving an l1-norm optimization problem. Experiments show that excellent performance of SR can be obtained by using CS features that retain information redundancy of the original sample. Meanwhile, multi-view image target classification is robust without preprocessing.

Soccer Detection in Images Based on Quaternion and Pulse Coupled Neural Network

ZHENG Tian-yu, GU Xiao-dong

2013, 31(2):  183-189.  doi:10.3969/j.issn.0255-8297.2013.02.013

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 This paper proposes a soccer detection method that combines the attention selection model of phase spectrum of quaternion Fourier transform (PQFT) and pulse coupled neural network (PCNN). In the preprocessing, the region outside the field is removed, and the region of interest extracted using PQFT. The
target is detected according to the physical characteristics such as color, shape and size. If no candidate or more than one are detected, a Kalman filter is used to make prediction. Simulation shows that the identification rate is improved by 9.6% and 14.9% respectively as compared to the dynamic Kalman filtering with velocity control and the real time ball detection framework introduced in the literature.

Multi-class Steganalyzer with Recompression Detection for JPEG Images

LI Xing1,2, ZHANG Tao1, HE Zan-yuan2, LI Wen-xiang1, LI Kai-da1, PING Xi-jian1

2013, 31(2):  190-196.  doi:10.3969/j.issn.0255-8297.2013.02.014

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A novel multi-class steganalyzer with recompression detection for JPEG images is proposed to identify steganographic methods from singly and doubly compressed stego images. Based on the statistical distribution of the first digits of DCT coefficients, a JPEG recompression detection method is proposed. Features for blind detection are extracted from the histogram, intrablock correlation, interblock correlation and spatial blockiness. Finally, a multi-class detector against current steganographic methods is constructed with support vector machine. The experimental results show that the proposed recompression detection scheme outperforms the existing methods significantly, and is robust to embedding changes. The low dimensional
steganalytic features have better performance, and the multi-class steganalyzer can identify the current JPEG steganographic methods reliably.

Computer Science and Applications

Text Categorization Based on Concept Knowledge

DING Ze-ya1,2, ZHANG Quan1

2013, 31(2):  197-203.  doi:10.3969/j.issn.0255-8297.2013.02.015

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 To achieve semantic understanding, this paper proposes a method for text categorization based on concept-knowledge in the hierarchical network of concepts (HNC). The method includes two parts: feature selection using concepts and text categorization according to category relatedness degree. In this paper, category key concepts are explored by computing discrimination degree of concepts, and used to further reduce dimensionality of the feature space. Based on the category semantic information consisting of category key concepts and relatedness weights, the method of computing relatedness degrees between documents and categories is proposed. The category relatedness degree of document is used as a measure for text categorization. Experiments show that the proposed method can effectively reduce dimensionality of feature space, increase efficiency and ensure effectiveness of text categorization. Compared with SVM, KNN and Bayes, this method is the best in terms of F1 values at higher feature reduction levels. In terms of overall performance, the method is almost equivalent to SVM, and better than KNN and Bayes.

Prediction of IFTS Based on Deterministic Transition

ZHENG Kou-quan1,2, LEI Ying-jie1, WANG Rui1, WANG Yi1

2013, 31(2):  204-211.  doi:10.3969/j.issn.0255-8297.2013.02.016

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To break the limitation in the description of the fuzzy trend of uncertain data sets and the partitioning intervals, the definition of intuitionistic fuzzy time series is regulated. A forecasting method of intuitionistic fuzzy time series is proposed, which optimize the domain-dividing interval with an intuitionistic fuzzy C-means clustering algorithm. Deterministic transition intuitionistic fuzzy rules are established by adding a back-tracking scheme. The proposed method can better reflect the characteristic distribution of the uncertain system and improve the prediction accuracy of time series in complicated environments. Validity and superiority of the method are checked with a classical instance.

Duplicate Field Matching for Data Cleaning of Chinese Placenames

YE Ou1, ZHANG Jing1,2, LI Jun-huai1

2013, 31(2):  212-220.  doi:10.3969/j.issn.0255-8297.2013.02.017

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To improve accuracy of field matching of Chinese placenames, an approximate duplicate detection and cleaning strategy and a matrix approximate duplicate matching method are proposed. In the strategy, a matrix approximate duplicate matching method is used. Frequencies of the same Chinese characters or word between two Chinese placenames can first be calculated with a matrix operation. Semantic similarity and structure similarity can be calculated using the frequencies. By combining semantic and structure similarities, they are considered as the basis of duplicate detection and data cleaning. Simulation experiments are conducted to prove feasibility and validity of the method, showing that the matrix approximate duplicate matching method is better than other existing methods in terms of precision and recall ratio.