用于低维混沌时间序列预测的一种非线性自适应预测滤波器

在二阶Volterra滤波器基础上,提出了一种用于低维混沌时间自适应预测的非线性自适应预测器。基于最小均方误差准则导出了一种NLMS类型的自适应算法来实时调整这种非线性滤波预测器的系数,仿真实验结果表明：这种线性化的非线性自适应滤波预测器能够有效地预测低维混时间序列,且它的模块化特征更易于VLSI电路实现,具有广泛的工程应用价值。     

Distributed lifetime optimization in wireless sensor networks using alternating direction method of multipliers

Due to the limited energy of sensor nodes in wireless sensor networks, extending the network lifetime is a major challenge that can be formulated as an optimization problem. In this paper, we propose a distributed iterative algorithm based on alternating direction method of multipliers with the aim of maximizing sensor network lifetime. The features of this algorithm are the use of local information, low overhead of message passing, low computational complexity, fast convergence, and, consequently, reduced energy consumption. In this study, we present the convergence results and the number of iterations required to achieve the stopping criterion. Furthermore, the impact of problem size (number of sensor nodes) on the solution and constraints violation is studied, and, finally, the proposed algorithm is compared with one of the well‐known subgradient‐based algorithms.     

Linear and non‐linear space diversity combining algorithms over fading channels in TDMA systems

This paper investigates the performances of various adaptive algorithms for space diversity combining in time division multiple access (TDMA) digital cellular mobile radio systems. Two linear adaptive algorithms are investigated, the least mean square (LMS) and the square root Kalman (SRK) algorithm. These algorithms are based on the minimization of the mean‐square error. However, the optimal performance can only be obtained using algorithms satisfying the minimum bit error rate (BER) criterion. This criterion can be satisfied using non‐linear signal processing techniques such as artificial neural networks. An artificial neural network combiner model is developed, based on the recurrent neural network (RNN) structure, trained using the real‐time recurrent learning (RTRL) algorithm. It is shown that, for channels characterized by Rician fading, the artificial neural network combiners based on the RNN structure are able to provide significant improvements in the BER performance in comparison with the linear techniques. In particular, improvements are evident in time‐varying channels dominated by inter‐symbol interference. Copyright © 2001 John Wiley & Sons, Ltd.     

初值自适应的磁性目标跟踪方法

针对在初始先验信息缺失时磁性目标滤波跟踪方法发散问题进行研究,本文提出了一种多初值模型的解决框架,并以平方根形式的中心差分卡尔曼滤波器（Square-Root Central Difference Kalman Filter,SRCDKF）为例,结合多初值模型得到了SRCDKF自适应磁性目标跟踪算法.文章首先根据远距离磁偶极子的磁场等效性,建立了多初值滤波跟踪模型,然后基于最大似然选择理论推导了如何从多模型中选择最佳结果,即多初值模型的选择方法,最后以SRCDKF滤波器为滤波单元,得到了基于SRCDKF的自适应磁性目标跟踪算法.经过仿真试验表明：（1）多初值模型建立和选择方法的有效性;（2）基于SRCDKF的自适应磁性目标跟踪算法,在初始位置信息缺失的情况下,能够有效完成对磁性目标的跟踪;（3）以不同滤波器为滤波单元的自适应跟踪算法跟踪试验结果表明,多初值模型的解决框架可解决初值先验未知下的跟踪问题.     

Blind adaptive receiver for uplink multiuser massive MIMO systems

Herein, we consider uplink multiuser massive multiple‐input multiple‐output systems when multiple users transmit information symbols to a base station (BS) by applying simple space‐time block coding (STBC). At the BS receiver, two detection filters for each user are used to detect the STBC information symbols. One of these filters is for odd‐indexed symbols and the other for even‐indexed symbols. Using constrained output variance metric minimization, we first derive a special relation between the closed‐form optimal solutions for the two detection filters. Then, using the derived special relation, we propose a new blind adaptive algorithm for implementing the minimum output variance‐based optimal filters. In the proposed adaptive algorithm, filter weight vectors are updated only in the region satisfying the special relation. Through a theoretical analysis of the convergence speed and a computer simulation, we demonstrate that the proposed scheme exhibits faster convergence speed and lower steady‐state bit error rate than the conventional scheme.     

Non‐convex model for sensor network localization based on connectivity

In this paper, a new model utilizing all the information derived from connectivity‐based sensor network localization is introduced. The connectivity information between any pair of nodes is modeled as convex and non‐convex constraints. The localization problem is solved by searching for a solution that would satisfy all the constraints established in the problem. A two‐objective evolutionary algorithm called Pareto Archived Evolution Strategy (PAES) is used to solve the localization problem. The solution can reach the most suitable configuration of the unknown nodes because the information on both convex and non‐convex constraints related to connectivity has been utilized. From simulation results, a relationship between the communication range and accuracy is obtained. Furthermore, a two‐level range connectivity‐based sensor network localization method is proposed to enrich the connectivity information. The two‐level range/indication of connectivity between each pair of nodes would indicate three levels of connectivity: strong, weak, or nil. A comparison on accuracy between the one‐level and two‐level ranges of connectivity is carried out by simulation using six different topological networks all containing 100 nodes. Simulation results have shown that better solution can be obtained by using two‐level range connectivity compared with the usual one‐level range connectivity‐based localization. Copyright © 2012 John Wiley & Sons, Ltd.     

Resource allocation in OFDMA networks based on interior point methods

This paper studies a joint optimization problem of sub‐carrier assignment and power allocation in orthogonal frequency division multiple access (OFDMA) wireless networks. A major challenge in solving the optimization problem is non‐convexity caused by the combinatorial nature of sub‐carrier assignment problem and/or non‐convex objective functions. To address the combinatorial complexity, we formulate the resource allocation problem as an optimization problem with continuous variables. We propose a novel approach based on a penalty function method and an interior point method (PM/IPM) to solve the problem. In specific, using a two‐step implementation, the penalty method is applied first to convert the non‐convex feasible region to a convex one. Then, the interior point method is deployed to solve the problem which is non‐convex only in the objective function. To evaluate the performance of PM/IPM, we apply a genetic algorithm (GA) that achieves near optimal solutions of the problem by iterative searching. Copyright © 2009 John Wiley & Sons, Ltd.     

Multi‐target localization in wireless sensor networks: a compressive sampling‐based approach

This paper considers the problem of localizing a group of targets whose number is unknown by wireless sensor networks. At each time slot, to save energy and bandwidth resources, only part of sensor nodes are scheduled to activate to remain continuous monitoring of all the targets. The localization problem is formulated as a sparse vector recovery problem by utilizing the spatial sparsity of targets’ location. Specifically, each activated sensor records the RSS values of the signals received from the targets and sends the measurements to the sink node where a compressive sampling‐based localization algorithm is conducted to recover the number and locations of targets. We decompose the problem into two sub‐problems, namely, which sensor nodes to activate, and how to utilize the measurements. For the first subproblem, to reduce the effect of measurement noise, we propose an iterative activation algorithm to re‐assign the activation probability of each sensor by exploiting the previous estimate. For the second subproblem, to further improve the localization accuracy, a sequential recovery algorithm is proposed, which conducts compressive sampling on the least squares residual of the previous estimate such that all the previous estimate can be utilized. Under some mild assumptions, we provide the analytical performance bound of our algorithm, and the running time of proposed algorithm is given subsequently. Simulation results demonstrate the effectiveness of our algorithms.Copyright © 2013 John Wiley & Sons, Ltd.     

基于自适应神经网络的二维线性相位FIR滤波器优化设计

提出一种基于自适应三角函数基神经网络的二维线性相位FIR滤波器优化设计方法.该方法根据二维线性相位FIR滤波器幅频响应特性,采用三角函数基神经网络优化算法计算滤波器系数,同时在神经网络训练过程引入自适应学习率算法,提高神经网络的学习效率和收敛速度.通过训练神经网络的权值,使二维线性相位FIR滤波器幅频响应与理想幅频响应...     

小均方自适应滤波器集平均理论及实验仿真简

在自适应滤波器应用中的一个重要问题是确定可调节滤波器参数最优的标准,以及利用这种标准形成实际上可行的算法。最小均方算法是现今应用最为广泛的一种线性自适应滤波算法。在变步长最小均方算法中,变步长算法的选取十分关键,它对自适应滤波器的滤波效果有重大的影响。基于最小步长理论的最小均方自适应滤波器理论,简化均方误差的计算过程,设计合适的参数使实际值与理论性逼近,验证最小步长理论的实用性,仿真结果表明实验值与理论值十分吻合,具有较强的实用性。     

无线传感器网络自适应蚁群定位算法研究

针对蚁群定位算法可能出现局部最优解而导致定位不准确的问题,提出了无线传感器网络自适应蚁群定位算法。通过将节点估计坐标移动方向离散化,将传感器定位问题转换成离散组合最优问题。定位过程中通过聚度和信息权重对传感器节点估计坐标向各个方向移动的概率进行修正,解决了定位结果收敛于局部最优解的问题。仿真结果表明,自适应蚁群定位算法比传统蚁群定位算法具有更低的定位误差。     

位移估计自适应滤波算法

基于“当前”统计模型的基础上 ,利用位移预测估计与实时位移估计间的偏差进行自适应方差调整 ,提出了一种新的自适应滤波算法———位移估计自适应跟踪算法 (AdaptiveFilteringAlgorithmofDistanceEstimation简记为ADE)。大量仿真结果表明 ,采用ADE算法既保持了对机动目标的跟踪性能 ,又显著提高了对弱机动目标及非机动目标的跟踪精度     

Multi-target localization algorithm based on adaptive grid in wireless sensor network

The RSS-based multi-target localization has the natural property of the sparsity in wireless sensor networks.A multi-target localization algorithm based on adaptive grid in wireless sensor networks was proposed,which divided the multi-target localization problem into two phases:large-scale grid-based localization and adaptive grid-based localization.In the large-scale grid-based localization phase,the optimal number of measurements was determined due to the sequential compressed sensing theory,and then the locations of the initial candidate grids were reconstructed by applying l_p (0＆lt; p＆lt;1) optimization.In the adaptive grid-based localization phase,the initial candidate grids were adaptively partitioned according to the compressed sensing theory,and then the locations of the targets were precisely estimated by applying l_poptimization once again.Compared with the traditional multi-target localization algorithm based on compressed sensing,the simulation results show that the proposed algorithm has higher localization accuracy and lower localization delay without foreknowing the number of targets.Therefore,it is more appropriate for the multi-target localization problem in the large-scale wireless sensor networks.     

A fast algorithm for designing stack filters

Stack filters are a class of nonlinear filters with excellent properties for signal restoration. Unfortunately, present algorithms for designing stack filters can only be used for small window sizes because of either their computational overhead or their serial nature. This paper presents a new adaptive algorithm for determining a stack filter that minimizes the mean absolute error criterion. The new algorithm retains the iterative nature of many current adaptive stack filtering algorithms, but significantly reduces the number of iterations required to converge to an optimal filter. This algorithm is faster than all currently available stack filter design algorithms, is simple to implement, and is shown in this paper to always converge to an optimal stack filter. Extensive comparisons between this new algorithm and all existing algorithms are provided. The comparisons are based both on the performance of the resulting filters and upon the time and space complexity of the algorithms. They demonstrate that the new algorithm has three advantages: it is faster than all other available algorithms; it can be used on standard workstations (SPARC 5 with 48 MB) to design filters with windows containing 20 or more points; and, its highly parallel structure allows very fast implementations on parallel machines. This new algorithm allows cascades of stack filters to be designed; stack filters with windows containing 72 points have been designed in a matter of minutes under this new approach.     

Dual‐mode multiple‐target tracking in wireless sensor networks based on sensor grouping and maximum likelihood estimation

The problem of tracking multiple mobile targets, using a wireless sensor network, is investigated in this paper. We propose a new sensor grouping algorithm, based on the maximum sensor separation distances (G‐MSSD), for estimating the location of multiple indistinguishable targets, either jointly or individually, depending on the distances between the generated groups. The joint tracking algorithm is formulated as a maximum likelihood (ML) estimator and solved through a modified version of the well‐known Gauss‐Newton (MGN) iterative method. We propose two candidate initial guesses for MGN based on G‐MSSD in joint tracking mode, while for the individual mode, the information of each group is used to estimate the location of only the corresponding target. The Cramer‐Rao lower bound (CRLB) for the variance of the proposed ML estimator is derived, and the potential conditions for reducing the CRLB are presented. Since tracking efficiency is affected by poor estimates, we present two criteria to evaluate the quality of estimates and detect the poor ones. An approach is also proposed for correcting the poor estimates, based on additional initial guesses. We demonstrate the effectiveness and accuracy of our proposed dual‐mode algorithm via simulation results and compare our results with the Multi‐Resolution search algorithm.     

Scale aware remote sensing image enhancement using rolling guidance

Enhancement of remotely sensed images is a challenging problem, since the enhanced image has to have an improved contrast and edge information while preserving the original radiance values as much as possible. In this paper, a scale aware enhancement method based on rolling guidance is proposed for remotely sensed images. For each scale, a guidance image is defined and the approximation image is provided by an iterative joint filtering of the approximation and guidance images. Then the extracted details are amplified through an adaptive scheme and added to the final level approximation layer to provide the resulting enhanced image. A comparative study between the proposed methods with classical edge preserving filters and traditional methods have been carried out by using several criteria. The proposed methods have an average of 12% improvement for contrast gain (CG) metric and 81% improvement for enhancement measurement (EME) metric compared to the closest comparison method.     

Adaptive joint beamforming and B‐MMSE detection for CDMA signal reception under multipath interference

The combination of antenna array beamforming with multiuser detection can effectively improve the detection efficiency of a wireless system under multipath interference, especially in a fast‐fading channel. This paper studies the performance of an adaptive beamformer incorporated with a block‐wise minimum mean square error(B‐MMSE) detector, which works on a unique signal frame characterized by training sequence preamble and data blocks segmented by zero‐bits. Both beam‐former weights updating and B‐MMSE detection are carried out by either least mean square (LMS) or recursive least square (RLS) algorithm. The comparison of the two adaptive algorithms applied to both beamformer and B‐MMSE detector will be made in terms of convergence behaviour and estimation mean square error. Various multipath patterns are considered to test the receiver's responding rapidity to changing multipath interference. The performance of the adaptive B‐MMSE detector is also compared with that of non‐adaptive version (i.e. through direct matrix inversion). The final performance in error probability simulation reveals that the RLS/B‐MMSE scheme outperforms non‐adaptive B‐MMSE by 1–5 dB, depending on the multipath channel delay profiles of concern. The obtained results also suggest that adaptive beamformer should use RLS algorithm for its fast and robust convergence property; while the B‐MMSE filter can choose either LMS or RLS algorithm depending on antenna array size, multipath severity and implementation complexity. Copyright © 2004 John Wiley & Sons, Ltd.     

用于非线性机动目标跟踪的新型IMM算法

针对在非线性机动目标跟踪中存在的滤波器易发散、机动检测有延迟等问题,把Unscented Kalman Filter(UKF)引进到交互多模型算法(IMM)中,设计了交互多模型UKF滤波器。并利用目标运动模型集概率的相对变化率设计了自适应交互多模型UKF滤波器,最后进行了计算机仿真。蒙特卡罗仿真结果表明,两种滤波算法都具备UKF滤波器精度高、稳定性好、不易发散的优点,同时不需了解目标机动的先验信息,适合于实际应用;并且自适应交互多模型UKF滤波器具有更好的跟踪效果。     

一种基于自适应滤波的雷达目标跟踪算法

针对在复杂环境下基于卡尔曼滤波的雷达目标跟踪中存在的鲁棒性和自适应性较差的问题,研究了一种新的雷达目标自适应鲁棒跟踪算法;通过引入自适应渐消因子,对估计误差协方差和滤波增益矩阵进行在线自适应调整,从而使得滤波算法具备良好的鲁棒性和自适应性,提高雷达目标跟踪的精度。最后,通过仿真对所研究的方法进行了验证。     

Adaptive polynomial predistorters for M‐QAM transmission using non‐linear power amplifiers

In this paper, adaptive baseband polynomial predistortion techniques are introduced to counter‐balance the AM/AM and AM/PM non‐linear effects of the transmit power amplifier. The proposed polynomial predistortion scheme is based on polar coordinate representation. Both LMS and RLS concepts are used to derive the adaptive algorithms. An enhanced LMS‐based algorithm with fast convergence and low complexity is proposed. For very fast convergence, a cascaded RLS‐based adaptive polynomial predistorter structure is introduced. The performance of the proposed schemes in terms of intermodulation distortion, spectral regrowth, and convergence rate are examined. The obtained results show that the polynomial predistortion schemes can be used in M‐QAM transmitters with power amplifiers operating near saturation to achieve a highest power efficiency. Copyright © 2006 John Wiley & Sons, Ltd.