NSST框架下结合局部自适应亮度与DCAPCNN的双波段图像融合

针对现有红外与可见光图像融合后,易出现边缘平滑严重、纹理细节恢复不足、对比度低、显著目标不突出、部分信息缺失等问题,提出一种基于非下采样剪切波变换(non-subsampled shearlet transform,NSST)的红外与可见光双波段图像融合算法。首先,采用基于自适应引导滤波(adaptive guided filter,AGF)的方法对源红外、可见光图像增强。其次,利用NSST正变换分别对源红外与可见光图像分解,得到红外、可见光图像的低、高频子带分量。然后,分别通过基于局部自适应亮度(local adaptive intensity,LAI)与双通道自适应脉冲耦合神经网络(dual channel adaptive pulse coupled neural network,DCAPCNN)规则融合低、高频子带分量。最后,通过NSST逆变换得到最终融合图像。实验结果表明,本文算法整体对比度更适宜,对红外热目标及可见光背景的边缘与纹理的细节恢复性更好,融合图像信噪比高,有效结合了红外及可见光图像的各自优势,与现有传统图像融合与深度学习融合算法相比,本文算法达到了更好的实验效果,在主观视觉感知和客观指标评价中均具有更好的融合性能。     

A new family of concurrent algorithms for adaptive Volterra andlinear filters

A novel idea for introducing concurrency in least squares (LS) adaptive algorithms by sacrificing optimality has been proposed. The resultant class of algorithms provides schemes to fill the wide gap in the convergence rates of LS and stochastic gradient (SG) algorithms. It will be particularly useful in the real time implementations of large-order linear and Volterra filters for which both the LS and SG algorithms are unsuited     

Sparse least mean fourth algorithm for adaptive channel estimation in low signal‐to‐noise ratio region

Both least mean square (LMS) and least mean fourth (LMF) are popular adaptive algorithms with application to adaptive channel estimation. Because the wireless channel vector is often sparse, sparse LMS‐based approaches have been proposed with different sparse penalties, for example, zero‐attracting LMS and L_p‐norm LMS. However, these proposed methods lead to suboptimal solutions in low signal‐to‐noise ratio (SNR) region, and the suboptimal solutions are caused by LMS‐based algorithms that are sensitive to the scaling of input signal and strong noise. Comparatively, LMF can achieve better solution in low SNR region. However, LMF cannot exploit the sparse information because the algorithm depends only on its adaptive updating error but neglects the inherent sparse channel structure. In this paper, we propose several sparse LMF algorithms with different sparse penalties to achieve better solution in low SNR region and take the advantage of channel sparsity at the same time. The contribution of this paper is briefly summarized as follows: (1) construct the cost functions of the LMF algorithm with different sparse penalties; (2) derive their lower bounds; and (3) provide experiment results to show the performance advantage of the propose method in low SNR region. Copyright © 2013 John Wiley & Sons, Ltd.     

一种变步长LMS自适应滤波算法及分析

本文对变步长自适应滤波算法进行了讨论,建立了步长因子μ与误差信号e(n)之间另一种新的非线性函数关系.该函数比已有的Sigmoid函数简单,且在误差e(n)接近零处具有缓慢变化的特性,克服了Sigmoid函数在自适应稳态阶段步长调整过程中的不足.由此函数本文得出了另一种新的变步长自适应滤波算法,并且分析了参数α,β的取值原则及对算法收敛性能的影响.该算法有较好的收敛性能且计算量少.计算机仿真结果与理论分析相一致,证实了该算法的收敛性能优于已有的算法.     

Sparse LMS/F algorithms with application to adaptive system identification

Standard least mean square/fourth (LMS/F) is a classical adaptive algorithm that combined the advantages of both least mean square (LMS) and least mean fourth (LMF). The advantage of LMS is fast convergence speed while its shortcoming is suboptimal solution in low signal‐to‐noise ratio (SNR) environment. On the contrary, the advantage of LMF algorithm is robust in low SNR while its drawback is slow convergence speed in high SNR case. Many finite impulse response systems are modeled as sparse rather than traditionally dense. To take advantage of system sparsity, different sparse LMS algorithms with l_p‐LMS and l₀‐LMS have been proposed to improve adaptive identification performance. However, sparse LMS algorithms have the same drawback as standard LMS. Different from LMS filter, standard LMS/F filter can achieve better performance. Hence, the aim of this paper is to introduce sparse penalties to the LMS/F algorithm so that it can further improve identification performance. We propose two sparse LMS/F algorithms using two sparse constraints to improve adaptive identification performance. Two experiments are performed to show the effectiveness of the proposed algorithms by computer simulation. In the first experiment, the number of nonzero coefficients is changing, and the proposed algorithms can achieve better mean square deviation performance than sparse LMS algorithms. In the second experiment, the number of nonzero coefficient is fixed, and mean square deviation performance of sparse LMS/F algorithms is still better than that of sparse LMS algorithms. Copyright © 2013 John Wiley & Sons, Ltd.     

自适应滤波算法及应用研究

本文首先分析了自适应滤波器的种类和各种算法的原理,然后举例分析自适应滤波算法的应用,给出所编写的程序.最后比较了各种算法的优缺点.     

自适应免疫遗传算法在认知决策引擎中的应用

提出一种自适应免疫遗传算法,设计自适应免疫遗传算子。该算法利用交叉率和变异率自适应调整策略,既防止交叉变异中的个体退化,又保证种群的多样性,并能快速收敛到全局最优解。仿真分析表明,与遗传算法等其他算法相比,该算法具有收敛速度快、平均适应度高、稳定性好等优点,能满足认知引擎参数优化的需要。     

Fast adaptive algorithms for AR parameters estimation using higherorder statistics

Time-varying statistics in linear filtering and linear estimation problems necessitate the use of adaptive or time-varying filters in the solution. With the rapid availability of vast and inexpensive computation power, models which are non-Gaussian even nonstationary are being investigated at increasing intensity. Statistical tools used in such investigations usually involve higher order statistics (HOS). The classical instrumental variable (IV) principle has been widely used to develop adaptive algorithms for the estimation of ARMA processes. Despite, the great number of IV methods developed in the literature, the cumulant-based procedures for pure autoregressive (AR) processes are almost nonexistent, except lattice versions of IV algorithms. This paper deals with the derivation and the properties of fast transversal algorithms. Hence, by establishing a relationship between classical (IV) methods and cumulant-based AR estimation problems, new fast adaptive algorithms, (fast transversal recursive instrumental variable-FTRIV) and (generalized least mean squares-GLMS), are proposed for the estimation of AR processes. The algorithms are seen to have better performance in terms of convergence speed and misadjustment even in low SNR. The extra computational complexity is negligible. The performance of the algorithms, as well as some illustrative tracking comparisons with the existing adaptive ones in the literature, are verified via simulations. The conditions of convergence are investigated for the GLMS     

一种新的变步长自适应滤波算法及分析

现有的变步长自适应滤波算法可分为步长一致和步长非一致两种情况,本文着重对步长一致的情况进行分析讨论,给出了统一的表达式,并定性地提出了步长变化的准则.同时在分析了几种现有变步长自适应滤波算法的基础上,提出了一种新的变步长自适应滤波算法.理论分析和不同情况下的仿真实验结果都表明,新算法能够符合文中提出的变步长准则,在保持算法简单的同时能够满足收敛速度,跟踪能力和收敛精度的要求.     

自适应数字预失真放大器的算法研究

由于功率放大器特性随温度,供电电压等因素的变化而改变,为了保证预失真功率放大器稳定工作,预失真系统的自适应性能就显得非常重要。基于查找表的预失真放大器广泛采用最小均方（least-mean．square,LMS）自适应算法。介绍了基于查找表的预失真放大器的基本结构,并根据步长参数和误差之间的非线性关系提出了一个新的变步长LMS算法。最后用MATLAB搭建了一个自适应预失真器的仿真系统。仿真表明,在迭代500次时,该算法对预失真放大器失真效果的改进明显优于以前的算法。     

Robust adaptive estimator for filtering noise in images

Provides three new methods for storing images corrupted by additive noise. One is the adaptive mean median filter for preserving the details of images when restored from additive Gaussian noise. Another is the minimum-maximum method for moving outlier noise. The third method, the robust adaptive mean p-median filter, is based on a combination of the previous two methods. In the past, proposed restoration methods have generally proven to be inadequate for both detail preservation and noise suppression, but the new adaptive mean p-median filter is shown to be good at both of these tasks, while the robust adaptive mean p-median filter can give good performance even in the presence of outliers. Degraded images are processed by the proposed algorithms, with the results compared with a selection of other median-based algorithms that have been proposed in the literature.     

A variable step‐size diffusion affine projection algorithm

This paper presents a new variable step‐size diffusion affine projection algorithm (VSS‐DAPA) to advance the filter performance of the diffusion affine projection algorithm (DAPA). The proposed VSS strategy is developed for the DAPA, which can solve the distributed estimation problem over diffusion networks well. To obtain the optimal step size reasonably, we seek the update recursion of mean‐square deviation (MSD) that is suitable for the DAPA. The step size is optimally given through the minimization for the MSD of the DAPA at each iteration. The derived step size through the MSD minimization improves the filter performance with respect to the convergence and the estimation error in steady state. The results based on simulations demonstrate that the proposed VSS‐DAPA performs better than the existing algorithms with a system‐identification scenario in diffusion network. Copyright © 2015 John Wiley & Sons, Ltd.     

Partial update and sparse adaptive filters

There has been increasing research interest in developing adaptive filters with partial update (PU) and adaptive filters for sparse impulse responses. On the basis of maximum a posteriori (MAP) estimation, new adaptive filters are developed by determining the update when a new set of training data is received. The MAP estimation formulation permits the study of a number of different prior distributions which naturally incorporate the sparse property of the filter coefficients. First, the Gaussian prior is studied, and a new adaptive filter with PU is proposed. A theoretical basis for an existing PU adaptive filter is also studied. Then new adaptive filters that directly exploit the sparsity of the filter are developed by using the scale mixture Gaussian distribution as the prior. Two new algorithms based on the Student's-t and power-exponential distributions are presented. The minorisation-maximisation algorithm is employed as an optimisation tool. Simulation results show that the learning performance of the proposed algorithms is better than or similar to that of some recently published algorithms     

OFDM系统中子信道的自适应比特、功率分配

研究了一种改进的OFDM系统中子信道的自适应比特、功率分配算法。该算法可适用于多径频率选择性衰落信道,它根据信道的瞬时特性,动态地为OFDM系统的各子信道分配传输比特数和发送功率。依据不同的分配准则,采用了MA和RA两种优化算法。仿真结果表明,采用自适应技术可以大大提高OFDM系统的性能。     

基于变步长等变化自适应盲源分离算法

对于基于梯度自适应的盲源分离算法，认真选择步长参数以达到好的分离性能是非常必要的。如果为加快收敛速度而增大步长因子，将会导致大的稳态误差，甚至引起算法发散，因此固定步长因子无法解决收敛速度和稳态误差之间的矛盾。本文为EASI算法提出了一种变步长的解决方案。通过建立步长因子与分离矩阵相互差异之间的非线性关系，加快了收敛速度，减小了失调误差。计算机仿真结果与理论分析相一致，证实了该算法明显优于传统的EASI算法。     

Designing an adaptive approach for segmenting non-stationary signals

Signal analysis tools such as Fourier transform are often applicable on data with a limited length. Segmentation is an important pre-processing step in many signal processing applications. Statistical characteristics of the signal in a segment are often preferred to be similar. This characteristic, stationarity, improves performance of signal analysis technique. This article develops an adaptive segmentation method based on wavelet transform and fractal dimension from two aspects. One is to use discrete stationary wavelet transform in pre-processing step instead of using classical wavelet transform. The other is to choose the optimal parameters. Two parameters are needed to calculate the fractal dimension of a decomposed signal, window length and percentage overlapping of the successive windows, which affect the performance of the proposed approach. These parameters are optimally set using the particle swarm optimisation algorithm. Performance of the proposed method is compared with three other existing segmentation methods using both synthetic signal and real data. The results indicate the superiority of the proposed technique in terms of accuracy compared to existing methods.     

基于小波变换变步长LMS自适应信道均衡的新方法

结合变换域最小均方(LMS)和变步长LMS算法的优势,提出了一种基于小波变换的变步长LMS自适应均衡方法。该方法中步长调整函数采用了改进的Sigmoid函数,该函数具有简单且误差信号接近零时变化缓慢的特点。并且,在训练模式、判决引导模式以及混合模式下,将提出方法和传统均衡方法进行了仿真比较。结果表明,所提出的方法比传统的线性LMS算法、变步长LMS以及小波变换LMS收敛更快、性能更优。     

Acoustic feedback cancellation based on weighted adaptive projection subgradient method in hearing aids

Acoustic feedback is an important factor that degrades the overall performance of hearing aids, and acoustic feedback cancellation has always been the research focus in the field of signal processing in hearing aids. The newly suggested adaptive projection subgradient method (APSM) for adaptive signal processing solves the problem of difficulty in finding the exact projection operator in the realization of affine projection by taking the subgradient projection hyperplane as the searching region for relaxed projection. This work applies APSM in the acoustic feedback cancellation system of hearing aids for the first time, and proposes a weighted adaptive projection subgradient method (WAPSM), which takes into consideration the exponential decay weight factor to incorporate the prior information of estimation system. The new method is compared with the traditional NLMS algorithm and APSM algorithm in simulation experiments. Incorporating the prior information of estimation system by setting the proper weighting matrix, WAPSM achieved notable improvements on the speed, stability and accuracy of the misalignment convergence. Numerical experiments demonstrate that the proposed algorithm is more robust for low SNR and real speech segment input than the traditional algorithms.     

Stable and efficient lattice algorithms for adaptive IIR filtering

Previous attempts at applying lattice structures to adaptive infinite-impulse-response (IIR) filtering have met with gradient computations of O(N²) complexity. To overcome this computational burden, two new lattice-based algorithms are proposed for adaptive IIR filtering and system identification, with both algorithms of O(N) complexity. The first algorithm is a reinterpretation of the Steiglitz-McBride method (1965), while the second is a variation on the output error method. State space models are employed to make the derivations transparent, and the methods can be extended to other parameterizations if desired. The set of possible stationary points of the algorithms is shown to be consistent with the convergent points obtained from the direct-form versions of the Steiglitz-McBride and output error methods, whose properties are well studied. The derived algorithms are as computationally efficient as existing direct-form based algorithms, while overcoming the stability problems associated with time-varying direct-form filters     

CDMA系统中一种反向链路自适应波束成形算法

在已有波束成形算法基础上,针对码分多址(CDMA)系统反向链路,给出相应智能天线信号模型及性能分析;并基于最大功率输出准则,采用幂法迭代计算,提出一种适合于高速环境的自适应波束成形算法.与原有一些算法相比,该算法仅计算解扩后信号的最大特征矢量,有效地降低了计算量,且性能稳定;可很好地应用于高速车载多径环境.计算机仿真结果表明在相同衰落情况下所提算法性能可接近于性能较好的码滤波法,但复杂度远低于后者,从而实现了以低的计算复杂度获得较好的系统性能.