电离层非相干散射谱和自相关函数估计算法

非相干散射谱对电离层各物理参数的研究具有至关重要的作用。现有非相干散射谱模型推导和计算较为复杂,且无法给出相应的自相关函数模型。该文重新推导了电离层非相干谱的简化模型,并提出相应的自相关函数模型。在此基础上,针对非相干散射信号处理中零时延处距离模糊大、分辨率较低,导致自相关函数在不同时延处均衡性较差,影响电离层散射谱估计性能的问题,提出使用自相关函数模型估计零时延自相关值的数据拟合方法。考虑到计算复杂度,提出利用多项式函数逼近自相关函数模型的快速实现方法。最后,通过对实测回波数据处理结果的对比分析,证明了该方法的可行性,对提高电离层探测精度具有重要意义。

     

Direct Determination of the Two-Dimensional Image Spectrum from Raw Synthetic Aperture Radar Data

This paper describes an algorithm which computes two-dimensional Fourier transform of a synthetic aperture radar (SAR) image. This method is intended to replace the whole SAR processing and the two-dimensional Fourier transformation by a specialized processing scheme which, in terms of computational speed and complexity, will be of the order of a single two-dimensional autocorrelation. This processing approach is applied to the estimation of the wave directional spectrum from the raw data of the SAR sensor of Seasat-1.     

一种同时基于多个数学难题的签名算法

为在同等计算复杂度的前提下提高算法的安全性,提出一个新的数字签名方案.在不扩大有限域zq*的基础上,通过增加本原根的维数,并且引入HAsh函数和二次剩余,基于多个数学难题对经典的Neberg-Rueppel签名体制进行改进.通过比较发现新算法的安全性大大增强,但是其复杂度却没有相应增加.     

Fast fractal image encoding based on adaptive search

This paper presents a new adaptive search approach to reduce the computational complexity of fractal encoding. A simple but very efficient adaptive necessary condition is introduced to exclude a large number of unqualified domain blocks so as to speed-up fractal image compression. Furthermore, we analyzed an unconventional affine parameter that has better properties than the conventional luminance offset. Specifically, we formulated an optimal bit allocation scheme for the simultaneous quantizations of the usual scaling and the aforementioned unconventional affine parameter. Experiments on standard images showed that our adaptive search method yields superior performance over conventional fractal encoding.     

ARMA modeling of fourth-order cumulants and phase estimation

A new procedure is proposed for ARMA modeling of fourth-order cumulants and trispectrum estimation of non-Gaussian stationary random processes. The new procedure is applied to the identification of nonminimum phase systems for both phase and magnitude response estimation. It is demonstrated by means of comprehensive simulation examples that the ARMA approach exhibits improved performance over conventional trispectrum methods. ARMA model order selection criteria based on fourth-order cumulants are presented and their performance evaluated. The computational complexity of the ARMA and conventional trispectrum methods is also examined. The new procedure does not require knowledge of the non-Gaussian distribution.This work was supported by the Office of Naval Research under Contract No. ONR-N00014-86-K-0219.     

A Nonlinear Acoustic Echo Canceller Using Sigmoid Transform in Conjunction With RLS Algorithm

Nonlinearity of amplifiers and/or loudspeakers gives rise to nonlinear echo in acoustic systems, which seriously degrades the performance of speech and audio communications. Many nonlinear acoustic echo cancellation (AEC) methods have been proposed. In this paper, a simple yet efficient nonlinear echo cancellation scheme is presented by using an adaptable sigmoid function in conjunction with a conventional transversal adaptive filter. The new scheme uses the least mean square (LMS) algorithm to update the parameters of sigmoid function and the recursive least square (RLS) algorithm to determine the coefficient vector of the transversal filter. The proposed AEC is proved to be convergent under some mild assumptions. Computer simulations show that the proposed scheme gives a superior echo cancellation performance over the well known Volterra filter approach when the echo path suffers from the saturation-type nonlinear distortion. More importantly, the new AEC has a much lower computational complexity than the Volterra-filter-based method.      

Pricing and power control for energy‐efficient radio resource management in cognitive femtocell networks

Cognitive femtocell has been considered as a promising technique that can improve the capacity and the utilization of spectrum efficiency in wireless networks because of the short transmission distance and low transmit power. In this paper, we study the win–win solution of energy‐efficient radio resource management in cognitive femtocell networks, where the macrocell tries to maximize its revenue by adjusting spectrum utilization price while the femtocells try to maximize their revenues by dynamically adjusting the transmit power. When the spectrum utilization price is given by macrocell, we formulate the power control problem of standalone femtocells as an optimization problem and introduce a low‐complexity iteration algorithm based on gradient‐assisted binary search algorithm to solve it. Besides, non‐cooperative game is used to formulate the power control problem between collocated femtocells in a collocated femtocell set, and then low complexity and widely used gradient‐based iteration algorithm is applied to obtain the Nash‐equilibrium solution. Specially, asymptotic analysis is applied to find the approximate spectrum utilization price in macrocell, which can greatly reduce the computational complexity of the proposed energy‐efficient radio resource management scheme. Finally, extensive simulation results are presented to verify our theoretical analysis and demonstrate the performance of the proposed scheme. Copyright © 2013 John Wiley & Sons, Ltd.     

基于OFDM符号宽度的循环平稳频谱感知方法

该文提出一种新的基于OFDM符号宽度的感知方法。该方法首先对接收到的每个OFDM符号在其符号周期内进行循环自相关函数的估计,然后利用多元统计理论计算判决量和判决门限,最后将判决量和判决门限进行比较从而得到判决结果。该方法是非参数化的,因而能够在噪声不确定的情况下有效工作,并且该方法能够极大简化目前循环平稳感知类算法的复杂度而只有细微的性能损失。此外,该文接着又提出一个非参数化多天线线性加权合并感知方法。仿真结果表明,所提合并方法通过合理地非参数化优化加权系数,与传统循环平稳感知方法相比,在复杂度显著降低的同时,性能几乎与传统循环平稳感知方法一致。     

Adaptive Processing of Digital Broadband Seismic Data

The main purpose of this paper is to develop a step-wise approach to recursive parameter estimation for time-invariant autoregressive moving average (ARMA) models used to track slowly time variant-seismic noise. In these steps computational complexity is balanced against estimation accuracy. By updating with every new data sample, the recursions are well adapted to on-line implementation. They are designed to be insensitive to spurious additive glitches in the data. Assuming that the ARMA parameters vary slowly with time, the estimated parameters contain information about the long time behavior of the modeled process compared with the time duration of additive transient signals. In seismological applications these transients are thought to be earthquake signals. The estimated ARMA parameters are used a) for the design of robust prediction error filters with arbitrary prediction distance to reduce the microseismic noise while passing the earthquake signal widely undisturbed, and b) for automatic detection of earthquake signals. A three-step scheme for the detection of weak earthquake signals is developed: The first step is to clean the data from glitches (for example data transmission errors) by replacing these with predicted values. The second step involves conventional recursive bandpass filtering to focus upon relevant frequency bands. In the third step a detection variable is computed from the difference of time consecutive ARMA parameter vectors for the bandpass filtered traces.     

Low-complexity SIC-MMSE for joint multiple-input multiple-output detection

Iterative detection and decoding based on a soft interference cancellation–minimum mean squared error (SIC-MMSE) scheme provides efficient performance for coded MIMO systems. The critical computational burden for a SIC-MMSE detector in a MIMO system lies in the multiple inverse operations of the complex matrix. In this paper, we present a new method to reduce the complexity of the SIC-MMSE scheme based on a MIMO detection scheme that uses a single universal matrix with a non-layer-dependent inversion process. We apply the Taylor series expansion approach and derive a simple non-layer-dependent inverse matrix. The simulation results reveal that the utilization of the universal matrices presented in this paper produces almost the same performance as the conventional SIC-MMSE scheme but with low computational complexity.     

Adaptive clutter suppression based on iterative adaptive approach for airborne radar

To improve the performance of the recently developed weighted least-squares-based iterative adaptive approach (IAA) in space–time adaptive processing (STAP) for weak or slow targets detection, we propose a novel IAA scheme to adaptively suppress the ground clutter by using the secondary training data (STD). Especially, we use the IAA to estimate the clutter plus noise covariance matrix from a very small number of STD. The resulting clutter plus noise covariance matrix can be utilized to form the STAP filter and then suppress the clutter. To reduce the computational complexity of the IAA, we exploit the sparsity of large clutter components in the angle-Doppler image and develop a modified IAA algorithm employing a soft-thresholding to adaptively determine the entries of each iteration that should be updated. Simulation results show that our proposed scheme outperforms the conventional IAA scheme over weak or slow targets detection and the modified IAA algorithm exhibits a comparable or even a better performance than the IAA algorithm but a lower computational complexity.     

A Novel NN-Predistorter Learning Method for Nonlinear HPA

The deficiency of predistortion performance exists in indirect NN (Neural Network)-predistorter learning methods for nonlinear high power amplifiers (HPAs), and direct NN-predistorter learning methods possess great computational complexity. To circumvent these problems, in this paper we propose a novel NN-predistorter learning method with its structure developed by using some properties of nonlinear operators and its corresponding algorithm derived by using an approximation formula. The proposed method is based on the identification of NN post-distorter of the HPA, and then directly implements the efficient Levenberg-Marquardt back propagation algorithm. Thus, compared with the direct NN-predistorter learning method, our proposed method reduces the computational complexity and still keeps slightly better predistortion performance. Theoretical analysis and simulation results also show our proposed method outperforms the indirect NN-predistorter learning method in the term of about 5 dB adjacent channel power ratio improvement.     

基于似然值概率密度演变的速率可变穿刺Turbo码的最优设计

该文提出了一种新的用似然值概率密度演变来为高斯信道下的速率可变穿刺Turbo码(RCPT)选择最优穿刺矩阵的方法。这种新方法通过比较不同穿刺矩阵对应的解码门限来衡量Turbo码在低信噪比区域的性能。这种分析方法与传统的通过距离谱来分析Turbo码性能的方法相比主要有两个优点：一是这种新的分析方法与交织器无关,门限值不会随着交织器的变化而变化;二是在码字比较长的情况下,这种新的分析方法计算复杂度不会增加太多。计算机仿真结果表明,当码字比较长时,在高斯信道下,这种分析方法选出的最优穿刺矩阵与传统的距离谱分析方法选出的最优的穿刺矩阵是完全一致的。该文还进一步给出了以3G中的Turbo码为母码的最优RCPT矩阵以及其门限,并且发现当3G协议中的速率匹配采用穿刺的时候,速率匹配可以等效为一个穿刺矩阵。对于一些可以进行比较的速率,这个穿刺矩阵跟用该文提出的方法给出的最优穿刺矩阵是完全一致的。     

A novel center of mass method for estimation of center frequency and spectral edges in CR using filter banks

In this paper, we propose a novel multistage DFT based polyphase filter bank technique using center of mass approach for estimating center frequency, detecting spectral edges and identifying spectral holes in wideband cognitive radio (CR) for efficient utilization of radio frequency spectrum. Spectral holes are identified by measuring energy at the output of individual subband of filter banks. Accuracy of spectral holes detection depends on frequency resolution of subbands and can be increased with an increase in number of DFT points, however, at the expense of computational complexity. In order to reduce complexity our algorithm starts with a coarser spectral resolution in the first stage. If a primary user appears over more than one subband, center frequency can be estimated in the first stage using proposed approach. However, if the primary user appears exclusively within a single subband, center frequency can be estimated at the second stage. For center frequency estimation, we propose a novel center of mass approach to achieve better precision, where mass is related to energy and distance is related to frequency. Exhaustive simulation results show that center frequency estimation using proposed multistage polyphase filter bank based on center of mass reduces computational complexity and has higher precision compared to conventional filter bank methods.     

Improved Method for the Macroblock‐Level Deblocking Scheme

This paper presents a deblocking method for video compression in which the blocking artifacts are effectively extracted and eliminated based on both spatial and frequency domain operations. Firstly, we use a probabilistic approach to analyze the performance of the conventional macroblock‐level deblocking scheme. Then, based on the results of the analysis, an algorithm to reduce the computational complexity is introduced. Experimental results show that the proposed algorithm outperforms the conventional video coding methods in terms of computation complexity while coding efficiency is maintained.     

A new approach to subband adaptive filtering

Subband adaptive filtering has attracted much attention lately. In this paper, we propose a new structure and a new formulation for adapting the filter coefficients. This structure is based on polyphase decomposition of the filter to be adapted and is independent of the type of filter banks used in the subband decomposition. The new formulation yields improved convergence rate when the LMS algorithm is used for coefficient adaptation. As we increase the number of bands in the filter, the convergence rate increases and approaches the rate that can be obtained with a flat input spectrum. The computational complexity of the proposed scheme is nearly the same as that of the fullband approach. Simulation results are included to demonstrate the efficacy of the new approach     

基于箕舌线函数的变步长归一化最小均方算法

对变步长归一化最小均方(VS-NLMS)自适应算法进行了讨论,针对其在自适应过程渐进稳态时对噪声干扰过于敏感的不足做了改进。同时,为了协调其低稳态误差与快速跟踪性能间的矛盾,引入基于相关误差项的变步长调整方案,同时采取了替代Sigmoid函数的箕舌线函数作为步长迭代公式,大大降低了计算复杂度。仿真结果表明,改进后的算法不仅具备优于归一化最小均方算法的收敛性能,同时具备了更小的稳态失调和快速灵敏的时变跟踪能力。     

An efficient search strategy for block motion estimation usingimage features

Block motion estimation using the exhaustive full search is computationally intensive. Fast search algorithms offered in the past tend to reduce the amount of computation by limiting the number of locations to be searched. Nearly all of these algorithms rely on this assumption: the mean absolute difference (MAD) distortion function increases monotonically as the search location moves away from the global minimum. Essentially, this assumption requires that the MAD error surface be unimodal over the search window. Unfortunately, this is usually not true in real-world video signals. However, we can reasonably assume that it is monotonic in a small neighborhood around the global minimum. Consequently, one simple strategy, but perhaps the most efficient and reliable, is to place the checking point as close as possible to the global minimum. In this paper, some image features are suggested to locate the initial search points. Such a guided scheme is based on the location of certain feature points. After applying a feature detecting process to each frame to extract a set of feature points as matching primitives, we have extensively studied the statistical behavior of these matching primitives, and found that they are highly correlated with the MAD error surface of real-world motion vectors. These correlation characteristics are extremely useful for fast search algorithms. The results are robust and the implementation could be very efficient. A beautiful point of our approach is that the proposed search algorithm can work together with other block motion estimation algorithms. Results of our experiment on applying the present approach to the block-based gradient descent search algorithm (BBGDS), the diamond search algorithm (DS) and our previously proposed edge-oriented block motion estimation show that the proposed search strategy is able to strengthen these searching algorithms. As compared to the conventional approach, the new algorithm, through the extraction of image features, is more robust, produces smaller motion compensation errors, and has a simple computational complexity.     

Multichannel fast QRD-LS adaptive filtering: new technique andalgorithms

A direct, unified approach for deriving fast multichannel QR decomposition (QRD) least squares (LS) adaptive algorithms is introduced. The starting point of the new methodology is the efficient update of the Cholesky factor of the input data correlation matrix. Using the new technique, two novel fast multichannel algorithms are developed. Both algorithms comprise scalar operations only and are based exclusively oh numerically robust orthogonal Givens rotations. The first algorithm assumes channels of equal orders and processes them all simultaneously. It is highly modular and provides enhanced pipelinability, with no increase in computational complexity, when compared with other algorithms of the same category. The second multichannel algorithm deals with the general case of channels with different number of delay elements and processes each channel separately. A modification of the algorithm leads to a scheme that can be implemented on a very regular systolic architecture. Moreover, both schemes offer substantially reduced computational complexity compared not only with the first algorithm but also with previously derived multichannel fast QRD schemes. Experimental results in two specific application setups as well as simulations in a finite precision environment are also included     

A novel surface registration algorithm with biomedical modeling applications.

In this paper, we propose a novel surface matching algorithm for arbitrarily shaped but simply connected 3-D objects. The spherical harmonic (SPHARM) method is used to describe these 3-D objects, and a novel surface registration approach is presented. The proposed technique is applied to various applications of medical image analysis. The results are compared with those using the traditional method, in which the first-order ellipsoid is used for establishing surface correspondence and aligning objects. In these applications, our surface alignment method is demonstrated to be more accurate and flexible than the traditional approach. This is due in large part to the fact that a new surface parameterization is generated by a shortcut that employs a useful rotational property of spherical harmonic basis functions for a fast implementation. In order to achieve a suitable computational speed for practical applications, we propose a fast alignment algorithm that improves computational complexity of the new surface registration method from O(n3) to O(n2).