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1.
针对现有网络化雷达功率资源利用率低的问题,该文提出一种基于目标容量的功率分配(TC-PA)方案以提升保精度跟踪目标个数.TC-PA方案首先将网络化雷达功率分配模型制定为非光滑非凸优化问题;而后引入Sigmoid函数将原问题松弛为光滑非凸优化问题;最后运用近端非精确增广拉格朗日乘子法(PI-ALMM)对松弛后的非凸问题进行求解.仿真结果表明,PI-ALMM对于求解线性约束非凸优化问题可以较快地收敛到一个稳态点.另外,相比传统功率均分方法和遗传算法,所提TC-PA方案可以最大限度地提升目标容量.  相似文献   

2.
针对现有网络化雷达功率资源利用率低的问题,该文提出一种基于目标容量的功率分配(TC-PA)方案以提升保精度跟踪目标个数。TC-PA方案首先将网络化雷达功率分配模型制定为非光滑非凸优化问题;而后引入Sigmoid函数将原问题松弛为光滑非凸优化问题;最后运用近端非精确增广拉格朗日乘子法(PI-ALMM)对松弛后的非凸问题进行求解。仿真结果表明,PI-ALMM对于求解线性约束非凸优化问题可以较快地收敛到一个稳态点。另外,相比传统功率均分方法和遗传算法,所提TC-PA方案可以最大限度地提升目标容量。  相似文献   

3.
陈凤华  马杰  戴静 《电视技术》2016,40(7):123-127
针对X光图像去噪时在抑制噪声的同时会模糊图像边缘的情况,提出采用图像卡通纹理分解和基于全变分的增广拉格朗日算法进行图像恢复.图像可以分解为卡通部分和纹理部分,噪声信息及图像的快变信息被分离到图像的纹理部分.通过基于增广拉格朗日算法的全变分去噪模型对纹理图像进行去噪处理,将卡通图像与处理后的纹理图像加权合成得到恢复图像.仿真实验结果表明,该方法不仅可以对图像进行快速处理,而且能够较好地保持图像的边缘信息,获得较高的输出信噪比.  相似文献   

4.
《信息技术》2018,(1):95-99
文中提出一种新的基于iPiano非凸优化算法的图像分割方法。该方法通过核函数将低维空间数据映射到高维特征空间。图像分割模型中的数据项涉及非光滑的L1范数形式,并且以Ginzburg-Landau泛函作为正则项。因此该分割模型是非凸非光滑的,采用iPiano非凸优化算法对其进行直接求解。该分割模型能够适应于多种不同的图像数据类型,不受初始条件的影响,能够获得同其他主流图像分割算法相类似的分割结果。同时该方法是iPiano非凸优化算法在图像分割领域的第一次应用,扩展了iPiano算法的应用领域。  相似文献   

5.
本文将压缩感知图像恢复问题作为低秩矩阵恢复问题来进行研究.为了构建这样的低秩矩阵,我们采样非局部相似度模型,将相似图像块作为列向量构建一个二维相似块矩阵.由于列向量间的强相关性,因此该矩阵具有低秩属性.然后以压缩感知测量作为约束条件对这样的二维相似块矩阵进行低秩矩阵恢复求解.在算法求解的过程中,使用增广拉格朗日方法将受限优化问题转换为非受限优化问题,同时为了减少计算复杂度,使用基于泰勒展开的线性化技术来加速算法求解.实验表明该算法的收敛率、图像恢复性能均优于目前主流压缩感知图像恢复算法.  相似文献   

6.
针对能效提升、宏用户干扰减小的问题,该文研究了基于干扰效率最大的异构无线网络顽健资源分配算法.首先,考虑宏用户干扰约束、微蜂窝用户速率需求约束和最大发射功率约束,将资源优化问题建模为多变量非线性规划问题.其次,考虑有界信道不确定性模型,利用Dinkelbach辅助变量方法和连续凸近似方法结合对数变换方法,将原分式规划顽健资源分配问题转换为等价的确定性凸优化问题,并利用拉格朗日对偶算法获得解析解.理论分析了计算复杂度和参数不确定性对性能的影响.仿真结果表明该算法具有较好的干扰效率和鲁棒性.  相似文献   

7.
针对多用户的OFDM认知无线电系统,提出了一种适合于混合业务的分布式资源分配新算法.该算法以最大化系统容量为目标,将资源分配问题建模为非凸优化问题,并通过拉格朗日对偶理论将原问题分解为若干个独立的子问题,通过对子问题的求解可以获得最优的子载波分配和功率分配.同时,根据认知用户业务分组中不同的业务类型授予其不同的权重因子,确保资源分配结果能够满足各认知用户的QoS.仿真结果表明,该算法不仅提高了系统容量,而且还保证了资源分配的公平性和用户的QoS,且算法复杂度不高.  相似文献   

8.
针对多用户MIMO-OFDMA/TDM认知无线电系统,提出一种基于用户效用和最大化的动态资源分配与调度方案,通过松弛约束条件将NP-hard的组合最优化问题转化为凸优化问题并通过拉格朗日对偶法进行分解,利用次梯度迭代算法求解对偶问题求得原始问题最优解.仿真结果表明,该方案可最大化用户效用和,获得主用户与认知用户的最优功率/速率分配并实现用户调度,且算法复杂度低收敛速度快.  相似文献   

9.
本文针对多用户的OFDM认知无线电系统,提出了一种适合于混合业务的分布式资源分配新算法。该算法以最大化系统容量为目标,将资源分配问题建模为非凸优化问题,并通过拉格朗日对偶理论将原问题分解为若干个独立的子问题,通过对子问题的求解可以获得最优的子载波分配和功率分配。同时,根据认知用户业务分组中不同的业务类型授予其不同的权重因子,确保资源分配结果能够满足各认知用户的QoS。仿真结果表明,该算法不仅提高了系统容量,而且还保证了资源分配的公平性和用户的QoS,且算法复杂度不高  相似文献   

10.
针对计算机断层扫描(CT)重建过程中统计方法计算时间较长的问题,提出一种利用有序子集加速拆分算法的三维CT图像重建方法。该方法充分利用线性约束凸优化问题的增广拉格朗日(AL)方法在较弱条件下的收敛速度快的优势;同时针对内部最小二乘问题,使用AL方法的线性变形求解加权正则化最小二乘问题,该方法使用可分离二次型代理函数代替缩放增广拉格朗日中的二次型AL惩罚项,得到一种简单有序子集(OS)加速型拆分算法(OS-ASA),避免了繁琐的参数调整,可快速收敛。实验结果表明,该文算法显著加快了CT图像重建的收敛速度,当使用子集较多时,CT图像重建可以减少OS伪影。  相似文献   

11.
Consider a communication system whereby multiple users share a common frequency band and must choose their transmit power spectra jointly in response to physical channel conditions including the effects of interference. The goal of the users is to maximize a system-wide utility function (e.g., weighted sum-rate of all users), subject to individual power constraints. A popular approach to solve the discretized version of this nonconvex problem is by Lagrangian dual relaxation. Unfortunately the discretized spectrum management problem is NP-hard and its Lagrangian dual is in general not equivalent to the primal formulation due to a positive duality gap. In this paper, we use a convexity result of Lyapunov to estimate the size of duality gap for the discretized spectrum management problem and show that the duality gap vanishes asymptotically at the rate O (1/radicN), where N is the size of the uniform discretization of the shared spectrum. If the channels are frequency flat, the duality gap estimate improves to O (1/N) . Moreover, when restricted to the FDMA spectrum sharing strategies, we show that the Lagrangian dual relaxation, combined with a linear programming scheme, can generate an epsiv-optimal solution for the continuous formulation of the spectrum management problem in polynomial time for any epsiv > 0.  相似文献   

12.
We consider a problem of optimizing multi-cell downlink throughput in multiple-input single-output (MISO) beamforming with single user per sub-channel in the wireless communication system. Previous work based on the generalization of uplink-downlink duality has already reformulated the maximum achievable downlink throughput into dual uplink throughput maximization problem. Since the dual uplink problem is nonconvex, it is difficult to find its optimal solution. The main contribution of this paper is a novel practical algorithm based on heuristic to find the solution of beamforer design satisfying the necessary optimality conditions of the dual uplink problem. Meanwhile the converged beamforming vectors can in turn improve the system sum rate significantly. As the dual problem is a mixed optimization, we also provide algorithms for its two sub-optimal problems. Simulation results validate the convergence and the efficiency of proposed algorithms.  相似文献   

13.
晏万才  李方伟  王明月 《电讯技术》2023,63(12):1985-1994
针对多天线无线携能通信系统中能量收集节点作为潜在窃听者的信息安全问题,提出了一种智能反射面(Intelligent Reflecting Surface, IRS)和人工噪声辅助的物理层安全传输方案。首先考虑发射功率、能量收集门限以及IRS单位模约束,以最大化系统安全速率为优化目标,在合法用户直射链路不可用的情况下,联合设计发射端波束赋形矩阵、人工噪声协方差矩阵以及IRS相移矩阵,建模一非线性多变量耦合的非凸优化问题;接着利用均方误差准则等价转换非凸目标函数,并利用连续凸逼近方法(Successive Convex Approximation, SCA)处理非凸的能量收集约束;最后基于交替优化框架,分别用拉格朗日对偶方法和基于价格机制的优化最小化(Majorization-Minimization, MM)算法求解发射端变量和IRS端变量。仿真结果表明,与现有方案相比,所提算法能够在保障能量收集需求的同时大幅度提升系统的安全性能。  相似文献   

14.
针对单幅模糊图像复原的局限性和视频应用的广泛 性,提出了一种基于时空体和增广Lagrangian的快速视频复原方法。首先对视频复原与图像 复原的特征进行比较和分析,研究视频复原的三维解卷积操作, 并对目前存在的视频复原方法的实现过程与性能进行分析和总结;然后在时空体的思想下, 通过时空联合 的各向同性全变分来控制时间误差和空间误差,并引入一种增广的Lagrangian方法完成全变 分规整化的难 题;最后通过求解Lagrangian形式的f子问题和u子问题实现全变分最小化难题,并对规整化 过程中的参 数进行研究与讨论,最终实现了视频的快速鲁棒复原。基于仿真图像和实际视频的实验结果 表明,本文方法 的性能在运行时间和视觉质量评价方面都要优于当前的其它方法,能够有效地实现图像和视 频的快速复原。  相似文献   

15.
Using digital orthonormal filters and Lagrangian duality theory, the envelope-constrained (EC) filtering problem has been formulated as a dual quadratic programming (QP) problem with simple constraints. Applying the barrier-gradient and barrier-Newton methods based on the space transformation and gradient flow technique, two efficient design algorithms are constructed for solving this QP problem. An adaptive algorithm, based on the barrier-gradient algorithm, is developed to solve the EC filtering problem in a stochastic environment. The convergence properties are established in the mean and mean square error senses. To demonstrate the effectiveness of the proposed algorithms, a practical example using the Laguerre networks is solved for both the deterministic and stochastic cases  相似文献   

16.
In this paper, a novel regularization method for image restoration and reconstruction is introduced which is accomplished by adopting a nonconvex nonsmooth penalty that depends on the eigenvalues of structure tensor of the underlying image. At first, an alternating minimization scheme is developed in which the problem can be decomposed into three subproblems, two of them are convex and the remaining one is smooth. Then, the convergence of the sequence which generate by the alternating minimization algorithm is proved. Finally, the efficient performance of the proposed method is demonstrated through experimental results for both grayscale and vector-value images.  相似文献   

17.
字符矫正是光学字符识别(OCR)系统预处理过程中 的重要步骤,针对传统的增广拉格朗日乘子法(ALM)求解字符矫正问题时收敛性和计算速度 的不足,本文研究了并行分离的增广拉格朗日乘子法,综合考虑字符矫正模型的建立过程, 提出并行分离方法与ALM相结合的思想解决字符 矫正问题。用并行方式将迭代问题分解成3个子问题,计算时能够同时求解分解后的这3个 子问题,然后进行凸组合,最 后收敛到问题的最优解。实验结果表明,本文算法能够快速准确地对变形的字符图像进 行矫正,并且具有良好的实时性和适 应性,可用于OCR系统的矫正预处理中,提高OCR系统的识别率。  相似文献   

18.
Image restoration is an ill-posed problem that requires regularization to solve. Many existing regularization terms in the literature are the convex function. However, nonconvex nonsmooth regularization has advantages over convex regularization for restoring images, but its practical interest used to be limited by the difficulty of the computational stage which requires a nonconvex nonsmooth minimization. In this paper, an adaptive nonconvex nonsmooth regularization is proposed for image restoration by using the spatial information indicator. Moreover, an efficient numerical algorithm for solving the resulting minimization problem is provided by applying the variable splitting and the penalty techniques. Finally, its advantages are shown in deblurring edges and restoring fines of image simultaneously in experiments.  相似文献   

19.
Dual methods for nonconvex spectrum optimization of multicarrier systems   总被引:6,自引:0,他引:6  
The design and optimization of multicarrier communications systems often involve a maximization of the total throughput subject to system resource constraints. The optimization problem is numerically difficult to solve when the problem does not have a convexity structure. This paper makes progress toward solving optimization problems of this type by showing that under a certain condition called the time-sharing condition, the duality gap of the optimization problem is always zero, regardless of the convexity of the objective function. Further, we show that the time-sharing condition is satisfied for practical multiuser spectrum optimization problems in multicarrier systems in the limit as the number of carriers goes to infinity. This result leads to efficient numerical algorithms that solve the nonconvex problem in the dual domain. We show that the recently proposed optimal spectrum balancing algorithm for digital subscriber lines can be interpreted as a dual algorithm. This new interpretation gives rise to more efficient dual update methods. It also suggests ways in which the dual objective may be evaluated approximately, further improving the numerical efficiency of the algorithm. We propose a low-complexity iterative spectrum balancing algorithm based on these ideas, and show that the new algorithm achieves near-optimal performance in many practical situations.  相似文献   

20.
This paper presents a new algorithm for optimal spectrum balancing in modern digital subscriber line (DSL) systems using particle swarm optimization (PSO). In DSL, crosstalk is one of the major performance bottlenecks, therefore various dynamic spectrum management algorithms have been proposed to reduce excess crosstalks among users by dynamically optimizing transmission power spectra. In fact, the objective function in the spectrum optimization problem is always nonconcave. PSO is a new evolution algorithm based on the movement and intelligence of swarms looking for the most fertile feeding location, which can solve discontinuous, nonconvex and nonlinear problems efficiently. The proposed algorithm optimizes the weighted rate sum. These weights allow the system operator to place differing qualities of service or importance levels on each user, which makes it possible for the system to avoid the selfish‐optimum. We can show that the proposed algorithm converges to the global optimal solutions. Simulation results demonstrate that our algorithm can guarantee fast convergence within a few iterations and solve the nonconvex optimization problems efficiently. Copyright © 2008 John Wiley & Sons, Ltd.  相似文献   

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