用于三维超宽带乳腺肿瘤成像的最佳天线系统

在超宽带（UWB）乳腺肿瘤检测系统中,采用Debye-Z．雏色散模型对乳房建模可以很好地近似人体组织的电导率及介电常数通过在乳腺模型中分别加入3个不同频带的超宽带天线阵列,带宽涵盖4～12GHz,采用共焦成像算法和波束形成算法对使用不同天线的色散模型进行成像对比,确定出最优性能的天线．2种成像算法均能准确获得肿瘤位置图像,使用频带为4．0～7．5GHz的天线对色散模型进行波束形成成像时,成像图分辨率较高,干扰抑制最好,可以清晰地分辨距离肋骨层仅10mm、直径为5mm的肿瘤．     

基于FPGA的高集成度便携式B超成像系统

提出了一种全数字化的B超成像系统解决方案,该系统具有16通道80阵元,采用高集成度的超声波发射与回波接收电路设计,基于FPGA和USB技术实现数字化超声成像.与传统模拟解决方案相比,本设计大大降低了系统的面积与功耗,有较高的分辨率与探测精度,具有更高的医学诊断价值,并且能够在社区医疗、大型赛事等方面得到推广.同时在此方案基础上进行了系统雏形的设计与实现,从系统的角度验证方案的可行性.      

波束域的信号盲分离方法

现有的源信号盲分离方法大都是针对阵元输出信号进行的,各种干扰信号和观测噪声的影响使盲分离算法性能退化,甚至失效.为了提高低信噪比情况下的信号盲分离能力,提出一种新的信号盲分离方法,即先对阵元观测信号进行盲波束形成,而后利用波束输出信号实行盲分离.盲波束形成阶段既提高了盲分离输入信号的信噪比,又可降低盲分离模型的阶次,信号盲分离阶段不仅能进一步净化信号,还能分离同一波束内两个以上的源信号.采用多种情况的人工仿真混合信号进行实验,以评价新算法的性能,仿真结果表明新的盲分离方法优于各阶段算法.     

Composite multiobjective optimization beamforming based on genetic algorithms

All the parameters of beamforming are usually optimized simultaneously in implementing the optimization of antenna array pattern with multiple objectives and parameters by genetic algorithms （GAs）. Firstly, this paper analyzes the performance of fitness functions of previous algorithms. It shows that original algorithms make the fitness functions too complex leading to large amount of calculation, and also the selection of the weight of parameters very sensitive due to many parameters optimized simultaneously. This paper proposes a kind of algorithm of composite beamforming, which detaches the antenna array into two parts corresponding to optimization of different objective parameters respectively. New algorithm substitutes the previous complex fitness function with two simpler functions. Both theoretical analysis and simulation results show that this method simplifies the selection of weighting parameters and reduces the complexity of calculation. Furthermore, the algorithm has better performance in lowering side lobe and interferences in comparison with conventional algorithms of beamforming in the case of slightly widening the main lobe.     

时域波束形成在早期乳腺癌微波进场成像中的应用

提出时域波束形成算法模型，将其应用于早期乳腺癌微波近场成像方法中，并采用计算机进行仿真，取得较好的效果。该模型为其后采用DSP或FPGA具体实现提供方案可行性验证。     

超声成像系统的动态孔径控制方法

动态孔径控制技术是超声成像系统的一个重要环节,良好的动态孔径控制技术能够有效地改善图像质量.在深入分析超声信号的合成波束分布规律的基础上,提出一种能够消除聚焦波束扫描死区的动态孔径控制方法.该方法根据探头阵元数对探测区域进行均匀的区域分割,计算得到完全覆盖相邻两条扫描线之间区域的聚焦波束在该深度下所需的波束宽度,再根据不同探测深度的波束宽度反推该深度下所需的孔径大小.该方法不受探头参数特性改变,以及探测对象变化的影响,在超声成像系统具有普遍适用性.仿真成像表明:采用该动态孔径控制方法能够有效提高超声图像的整体质量.     

基于导数约束的互谱降秩自适应波束形成

在导数约束基础上,提出了一种对阵列导向矢量失配稳健的互谱降秩自适应波束形成算法.通过在期望方向上设置多个线性导数约束,对阵列接收数据进行利用互谱能量的降秩处理,然后进行自适应波束形成.该算法不仅能够在导向矢量失配情况下获得较好的性能,并且弥补了传统稳健自适应波束形成算法运算量大的缺点.仿真分析表明,所提出的算法具有较好的稳健性能.     

Diagonally loaded SMI algorithm based on inverse matrix recursion

The derivation of a diagonally loaded sample-matrix inversion (LSMI) algorithm on the busis of inverse matrix recursion (i.e.LSMI-IMR algorithm) is conducted by reconstructing the recursive formulation of covariance matrix. For the new algorithm, diagonal loading is by setting initial inverse matrix without any addition of computation. In addition, a corresponding improved recursive algorithm is presented, which is low computational complexity. This eliminates the complex multiplications of the scalar coefficient and updating matrix, resulting in significant computational savings. Simulations show that the LSMI-IMR algorithm is valid.     

Adaptive monopulse beamforming with partial parallel structure

A new recursive algorithm with the partial parallel structure based on the linearly constrained minimum variance (LCMV) criterion for adaptive monopulse systems is proposed. The weight vector associated with the original whole antenna array is decomposed into several adaptive weight sub-vectors firstly. An adaptive algorithm based on the conventional LCMV principle is then deduced to update the weight sub-vectors for sum and difference beam, respectively. The optimal weight vector can be obtained after convergence. The required computational complexity is evaluated for the proposed technique, which is on the order of O(N) and less than that of the conventional LCMV method. The flow chart scheme with the partial parallel structure of the proposed algorithm is introduced. This scheme is easy to be implemented on a distributed computer/digital signal processor (DSP) system to solve the problems of the heavy computational burden and vast data transmission of the large-scale adaptive monopulse array. Then, the monopulse ratio and convergence rate of the proposed algorithm are evaluated by numerical simulations. Compared with some recent adaptive monopulse estimation methods, a better performance on computational complexity and monopulse ratio can be achieved with the proposed adaptive method.     

基于SVRGD的机载预警雷达自适应波束形成算法

自适应波束形成是机载预警雷达数字信号处理的一个关键环节。针对传统最小均方误差(least mean square, LMS)算法在短快拍数条件下的波束形成性能下降以及因迭代震荡易收敛于局部最优值的问题,提出了一种基于机器学习的随机方差减小梯度下降(stochastic variance reduction gradient descent, SVRGD)自适应波束形成方法。首先,建立面阵列接收信号数据模型。其次,基于随机梯度下降原理,引入方差缩减法通过内外循环迭代方式进行梯度修正,以减小随机梯度估计的方差,建立算法模型与实现流程。最后，通过设置平面阵列仿真场景,分析SVRGD自适应波束形成算法在波束形成、抗干扰、收敛速度等方面的性能,验证了该算法在低快拍数、强干扰和强噪声背景下具有的优良能力。