The wavelet transform-domain LMS adaptive filter with partial subband-coefficient updating

In this paper, we propose and analyze the wavelet transform-domain LMS (WTDLMS) algorithm where only a subset of the adaptive filter coefficients are updated at each iteration. The use of wavelets or subband filterbanks can lead to several novel schemes whose combinations can lead to other sub-schemes as well. In the proposed scheme, the coefficients can be selected altogether as a block and not just individually as done earlier in the literature. This algorithm is then tested in the context of system identification and equalization.     

一种自适应码书模型背景更新算法

因背景更新过程中运动信息不足,造成在处理缓慢移动目标和只有局部运动目标时常常发生误判,为解决上述问题,通过提取运动目标的空间整体信息,提出了一种自适应的码书模型背景更新算法.该方法通过对运动目标空间信息变化进行分析,寻找前景中潜在背景,然后联合像素时域统计信息,得到真正的背景模型.实验结果表明,该算法可以快速适应背景变化,能明显减少对运动信息不足目标的误判,同时保证目标检测的完整性.     

Reduced-complexity decoding algorithm for low-density parity-checkcodes

A new reduced-complexity decoding algorithm for low-density parity-check codes that operates entirely in the log-likelihood domain is presented. The computationally expensive check-node updates of the sum-product algorithm are simplified by using a difference-metric approach on a two-state trellis and by employing the dual-max approximation. The dual-max approximation is further improved by using a correction factor that allows the performance to approach that of full sum-product decoding     

The use of the modified escalator algorithm to improve theperformance of transform-domain LMS adaptive filters

This paper describes a new algorithm that improves the convergence performance of the transform-domain least mean-square (TRLMS) algorithm. The algorithm exploits the sparse structure of the correlation matrix of the transformed input process to derive a data dependent Gram-Schmidt orthogonalization type transform of the process. We show its faster convergence compared with the time-domain least mean-square (LMS) algorithm and the DCT or the DWT-based TRLMS algorithm. The Gram-Schmidt orthogonalization is realized using a modified adaptive escalator algorithm. The modification significantly reduces the computational complexity of the adaptive escalator algorithm and determines the computational complexity of the proposed algorithm     

用于MIMO信号检测的降低复杂度V-BLAST算法

用于多输入多输出(MIMO)通信系统信号检测的V-BLAST算法其预处理具有较高的运算复杂度。对此作出改进,提出了一种降低复杂度V-BLAST算法。该算法在V-BLAST每一步的检测中不仅选取信噪比/信干噪比最大的一路信号、而且同时选取信噪比/信干噪比足够大的各路信号一起进行检测。仿真结果显示,该降低复杂度V-BLAST算法在取得与常规V-BLAST检测十分接近的误码性能的同时,达到了显著低于后者的预处理复杂度。     

An adaptive distance-based location update algorithm fornext-generation PCS networks

In this paper, we propose a stochastic model to compute the optimal update boundary for the distance-based location update algorithm. The proposed model is flexible and captures some of the real characteristics in the wireless cellular environment. The model can adapt to arbitrary cell topologies in which the number of neighboring base stations at different locations may vary. The cell residence time can follow general distributions which captures the fact that the mobile user may spend more time at certain locations than others. The model also incorporates the concept of a trip in which the mobile user may follow a particular path to a destination. For implementation, the decision of location update can be made by a simple table lookup. Numerical results indicate that the proposed model provides a more accurate update boundary in real environment than that derived from a hexagonal cell configuration with a random walk movement pattern. The proposed model allows the network to maintain a better balance between the processing incurred due to location update and the radio bandwidth utilized for paging between call arrivals     

Narrow-band interference excision in spread-spectrum systems usingself-orthogonalizing transform-domain adaptive filters

Among many transform-domain interference excision techniques, transform-domain adaptive filtering has many advantages. It is based on a true optimization of some particular performance parameters such as the bit-error rate (BER). Moreover, it is insensitive to jammer frequency. However, transform-domain adaptive filtering also has the drawback of being incapable of tracking a rapidly changing interference because most adaptive algorithms require time to converge to the optimal solution. In this paper, a self-orthogonalizing transform-domain least mean square (SO-TRLMS) algorithm is used to speed up the convergence. Compared to a traditional transform-domain least mean square (TRLMS) algorithm, the SO-TRLMS algorithm can significantly improve the convergence rate of the LMS algorithm, thus making the transform-domain adaptive filtering technique more suitable for real-time processing. In order to show how the system performance is affected by various factors such as interference power and the transform used, this paper presents an analytical result for the BER performance that is applicable for arbitrary orthogonal linear transforms. Simulation results are also presented to demonstrate the validity of the analysis     

Reduced-complexity delayed-decision algorithm for context-based image processing systems.

It is well known that the performance of context-based image processing systems can be improved by allowing the processor (e.g., an encoder or a denoiser) a delay of several samples before making a processing decision. Often, however, for such systems, traditional delayed-decision algorithms can become computationally prohibitive due to the growth in the size of the space of possible solutions. In this paper, we propose a reduced-complexity, one-pass, delayed-decision algorithm that systematically reduces the size of the search space, while also preserving its structure. In particular, we apply the proposed algorithm to two examples of adaptive context-based image processing systems, an image coding system that employs a context-based entropy coder, and a spatially adaptive image-denoising system. For these two types of widely used systems, we show that the proposed delayed-decision search algorithm outperforms instantaneous-decision algorithms with only a small increase in complexity. We also show that the performance of the proposed algorithm is better than that of other, higher complexity, delayed-decision algorithms.     

Suppression of narrowband jammers in a spread-spectrum receiverusing transform-domain adaptive filtering

The author investigates the use of transform-domain adaptive filtering for the suppression of narrowband jammers in a direct-sequence (DS) spread-spectrum receiver. An input signal consisting of the DS signal, white Gaussian noise, and a narrowband jammer is transformed into the frequency domain where adaptive filtering acts to suppress the jammer. Weight leakage is used to allow jammer suppression while preserving the desired DS signal. Bit error rate (BER) results obtained by computer simulation are presented to illustrate performance for a single-tone jammer and for a jammer consisting of a second DS signal with a lower chip rate. These results are compared to those for a transform-domain exciser. Expressions for the converged tap weights are derived and the results are verified via the simulation. Finally, the convergence properties of the adaptive suppressor are illustrated     

Image restoration algorithm based on compensated transmission and adaptive haze concentration coefficient

Aiming at the drawbacks of traditional dark channel prior,which was prone to distortion and Halo effects in the bright areas,a haze image restoration algorithm based on compensated transmission and adaptive haze concentration coefficient was proposed.First of all,a Gaussian function was used to fit the attenuation relationship between the haze and haze-free image,and the compensation transmission was set to correct the initial transmission.Then the characteristics of haze was analyzed,the concept of brightness entropy was introduced and the bright channel operation was performed to acquire entropy value with pixel by pixel.Combined with the Gaussian pyramid to extract texture features,the haze distribution map was obtained.An adaptive transformation was established to seek the haze concentration coefficient and get the accurate transmission.Finally,the recovery results were restored by improved atmospheric light value and atmospheric scattering model.Experimental results show that the recovered image has better color and detail,the degree of dehazing is thorough,the brightness is appropriate,and the effect is clear and natural.     

自适应更新时空方向能量目标跟踪算法研究

为降低遮挡对目标跟踪性能的影响,提出了一种基于自适应更新时空方向能量的目标跟踪算法。首先依据目标外观模型进行初步跟踪,计算目标平均运动矢量;然后,求取运动目标的时空方向能量特征,构建运动模型;依据运动模型和状态机检测目标状态,生成遮挡掩膜;最后,对不同状态和遮挡情况的目标采用不同的参数自适应更新其外观和运动模型。实验采用国际通用的CAVIAR和York两个公共测试数据集,并用平均跟踪误差和多目标跟踪精确度两个指标评测了跟踪性能。实验结果表明该方法的目标跟踪性能好,尤其是对目标遮挡的鲁棒性强。     

自适应增益的SPGD算法

SPGD算法是一种应用广泛的无波前探测自适应光学控制算法。传统SPGD算法中增益系数保持某一固定值不变,随着变形镜单元数的增加,这将导致算法收敛速度变慢及陷入局部极值的概率增大。Adam优化器是深度学习常用的一种优化随机梯度下降算法,它具有增益系数自适应性调整的特点。将Adam优化器自适应调整增益系数的优势与SPGD算法结合起来用于自适应光学系统控制。分别以32、61、97、127单元变形镜作为波前校正器件,不同湍流强度的波前像差作为校正对象,建立了无波前探测自适应光学系统模型。结果表明,优化后的算法收敛速度更快,而且陷入局部极值的概率降低,并且随着变形镜单元数的增加与湍流强度的增大,算法的优势更加明显。以上研究结果为基于Adam优化的SPGD算法的实际应用提供了理论基础。     

Equivariant adaptive selective transmission

In this paper, we consider the problem of selective transmission-the dual of the blind source separation task-in which a set of independent source signals are adaptively premixed prior to a nondispersive physical mixing process so that each source can be independently monitored in the far field. Following similar procedures for information-theoretic blind source separation, we derive a stochastic gradient algorithm for iteratively estimating the premixing matrix in the selective transmission problem, and through a simple modification, we obtain a second algorithm whose performance is equivariant with respect to the channel's mixing characteristics. The local stability conditions for the algorithms about any selective transmission solution are shown to be the same as those for similar source separation algorithms. Practical implementation issues are discussed, including the estimation of the combined system matrix and the reordering and scaling of the received signals within the algorithm. Mean square error-based selective transmission algorithms are also derived for performance comparison purposes. Simulations indicate the useful behavior of the premixing algorithms for selective transmission     

Gradient-driven update lifting for adaptive wavelets

Over the past few years, wavelets have become extremely popular in signal and image processing applications. The classical linear wavelet transform, however, performs a homogeneous smoothing of the signal contents which, in some cases, is not desirable. This has led to a growing interest in (nonlinear) wavelet representations that can preserve discontinuities, such as transitions and edges.

In this paper, we present the construction of adaptive wavelets by means of an extension of the lifting scheme. The basic idea is to choose the update filters according to some decision criterion which depends on the local characteristics of the input signal. We show that these adaptive schemes yield lower entropies than schemes with fixed update filters, a property that is highly relevant in the context of compression. Moreover, we analyze the effect of a scalar uniform quantization and the stability in such adaptive wavelet decompositions.     

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     

Complex fuzzy adaptive filter with LMS algorithm

A fuzzy adaptive filter is constructed from a set of fuzzy IF-THEN rules that change adaptively to minimize some criterion function as new information becomes available. This paper generalizes the fuzzy adaptive filter based on least mean squares (LMS) to include complex parameters and complex signals. The fuzzy filter as adaptive equalizer is applied to quadrature amplitude modulation (QAM) digital communication with linear complex channel characteristics     

Phase-only adaptive nulling with a genetic algorithm

This paper describes a new approach to adaptive phase-only nulling with phased arrays. A genetic algorithm adjusts some of the least significant bits of the beam steering phase shifters to minimize the total output power. Using small adaptive phase values results in minor deviations in the beam steering direction and small perturbations in the sidelobe level in addition to constraining the search space of the genetic algorithm. Various results are presented to show the advantages and limitations of this approach, in general, the genetic algorithm proves to be better than previous phase-only adaptive algorithms     

一种改进的稳健自适应波束形成算法

当快拍数较小时,自适应波束形成算法的性能将会降低,而对角加载算法是提高这类自适应波束形成算法稳健性的简单而有效的方法,但是至今没有一种比较有效的方法来确定对角加载值。本文提出了一种确定加载值的方法,这种方法在加载值和协方差矩阵的估计误差之间建立联系,它能够根据阵列的输出数据动态的调整加载值。计算机仿真证实了该算法的有效性。     

An in-place levinson update algorithm

An in-place Levinson update algorithm is presented which requires less storage than update algorithms in some published programs. The algorithm may be easily implemented in a program which employs a Levinson recursion.     

Constrained LMS adaptive algorithm

A new least-mean-squares (LMS) adaptive algorithm is developed in the letter. This new algorithm solves a specific variance problem that occurs in LMS algorithms in the presence of high noise levels and when the input signal is bandlimited. Quantitative results in terms of an accuracy measure of a finite impulse response (FIR) system identification are presented.