基于波束形成跳频系统抗干扰方法研究

本文研究利用波束形成技术与跳频技术相结合,在存在宽带阻塞干扰以及跟踪干扰时,将信号与干扰在空域进行分离,提高跳频系统的抗干扰性能。指出利用空间平滑不仅可以有效抑制相干跟踪干扰,而且提高了波束的收敛速度。仿真结果证明了该方法的有效性。     

数字波束形成与跳频相结合的卫星通信抗干扰新技术

空分技术和时分技术相互结合能弥补各自的缺点,产生最理想的抗干扰效果。自适应数字波束形成技术与跳频技术相结合的方式,是一种新的抗干扰技术。本文主要讨论这种新技术在卫星通信中的应用,阐述了该技术对快速跟踪式干扰的抑制能力。     

基于自适应天线的跳频系统干扰抑制方法

自适应天线与跳频技术相结合可进一步提高系统的抗干扰性能.对跳频系统中快速跟踪干扰的抑制,采用基于特征空间(ESB)自适应波束形成算法可提高波束的收敛速度,并与空间平滑技术相结合,可有效抑制与期望信号相关的快速跟踪干扰.计算机仿真结果证明了该方法的有效性.     

基于宽带波束形成的跳频通信跟踪干扰抑制方法

提出了一种针对跳频通信中跟踪干扰抑制方法,该方法能够在跳频同步前实现跟踪干扰的抑制,避免了传统跟踪干扰抑制方法在跳频同步被干扰后失效的缺点。采用空间插值的频域宽带波束形成技术实现了跳频信号与跟踪干扰信号的波束形成,在此基础上,给出了一种跟踪干扰抑制方法。对所提跟踪干扰抑制方法进行了计算机仿真,验证了该方法的正确性和有效性。     

基于自适应波束形成的抗干扰跳频通信方案

本文将自适应波束形成技术与跳频通信相结合,提出了一种新的基于空间平滑算法与相位补偿算法的抗相关干扰跳频通信方案.该方案通过采用相位补偿算法,消除了跳频与自适应波束形成相结合时存在的波束指向偏差问题,提高了波束形成的收敛速度.将空间平滑算法应用到波束形成中,有效实现了对跳频通信系统中相关干扰的快速抑制.在强相关干扰环境下...     

基于波束形成跳频系统抗干扰方法研究

本文研究利用波束形成技术与跳频技术相结合，在存在宽带阻塞干扰以及跟踪干扰时，将信号与干扰在空域进行分离，提高跳频系统的抗干扰性能。指出利用空间平滑不仅可以有效抑制相干跟踪干扰，而且提高了波束的收敛速度。仿真结果证明了该方法的有效性。     

自适应调零天线对快速跟踪干扰抑制的研究

为了有效地抑制来自跳频卫星通信系统上行链路的快速跟踪干扰,本文提出了一种数字波束形成与跳频相结合的新型干扰抑制技术(AJDBFFH).基于AJDBFFH技术,本文较好地解决了快速跟踪干扰与跳频有用信号相关所带来的干扰抑制问题.通过对快速跟踪干扰抑制的计算机仿真,其结果表明,AJDBFFH技术可以有效地抑制快速跟踪干扰,系统的误码率性能得到了明显的改善.     

宽带自适应跳频控制器

本文叙述宽带自适应跳频控制器的原理及方案，频率集的配置，自适应跳频拒收门限及频点更新，跳频码序及同步等。有效地组织宽带自适应跳频系统能避开大部分固定和半因定性质的单频连续波干扰，使得移动卫星通信系统中实现高可靠性的数据传输。     

自适应数字波束形成的应用研究

自适应数字波束形成技术是一种以数字方法来实现波束形成的技术．它采用先进的数字信号处理技术对天线阵列信号进行处理，以获得优良的波束性能。本文在介绍自适应数字波束形成系统的基本组成和工作原理的同时，对其工程应用方面也做了较为详细的分析说明。     

基于跳频技术的卫星通信干扰分析与比较

简要分析了跳频通信的基本特点和优点，通过计算和比较卫星通信常见的几种干扰方式（宽带噪声干扰、窄带噪声干扰和脉冲干扰）的处理增益和误码率，得出了脉冲干扰对跳频技术下的卫星通信具有较好的干扰效果这一结论，并给出了与理论分析相一致的仿真结果。     

大型线阵自适应数字波束形成超低副瓣技术

自适应数字波束形成技术是现代阵列天线系统必须采用的关键技术。为了对付强有源干扰,现代相控阵雷达都必须具有自适应的干扰抑制能力。除了对抗有源干扰外,大部分雷达还要求具有强杂波背景下检测目标的能力,这就需要雷达天线具有低或超低副瓣电平。本文针对大型线阵,结合数字波束形成,讨论了在保证自适应干扰置零的前提下,如何控制自适应波束的副瓣电平,从而实现阵列系统的超低副瓣性能。     

用于自适应数字波束形成的稳健子阵异步RLS算法

提出了一种修正的递归最小二乘自适应算法--稳健子阵异步递推最小二乘算法(MSARLS)--用于自适应数字波束形成.该算法综合运用稳健估计和子阵异步递推技术.改进后的算法,不但大大减少了运算量,而且增强了算法抗突发强干扰的性能.另还给出了计算的理论分析和计算机仿真结果.     

Combined adaptive beamforming schemes for nonstationary interfering noise reduction

This paper introduces new adaptive beamforming methods for nonstationary noise reduction, designed to be robust against broadband interfering signals. In particular, we propose combined beamforming schemes within a standard adaptive beamforming system, such as the generalized sidelobe canceller (GSC). The novelty of such combined adaptive beamformers relies on the use of different adaptive sidelobe cancelling structures which allow the system to achieve robustness in nonstationary noisy environments. The combined structures are based on the convex combination of two multiple-input single-output (MISO) adaptive systems with complementary capabilities. The whole beamformer benefits from such combination and results to be able to preserve the best properties of each system. We introduce two different adaptive schemes, whose difference lies in the way of combining the MISO systems. Moreover, we present a further adaptive beamforming scheme which generalizes the previous techniques, thus improving the robustness against nonstationary interfering signals in multisource environments. The effectiveness of the proposed systems is also assessed in a nonstationary dense multipath environment. The experiments show that the proposed combined beamforming schemes are capable of enhancing the desired signal even in the presence of nonstationary interfering signals.     

Principles of minimum variance robust adaptive beamforming design

Robustness is typically understood as an ability of adaptive beamforming algorithm to achieve high performance in the situations with imperfect, incomplete, or erroneous knowledge about the source, propagation media, and antenna array. It is also desired to achieve high performance with as little as possible prior information. In the last decade, several fruitful principles to minimum variance distortionless response (MVDR) robust adaptive beamforming (RAB) design have been developed and successfully applied to solve a number of problems in a wide range of applications. Such principles of MVDR RAB design are summarized here in a single paper. Prof. Gershman has actively participated in the development and applications of a number of such MVDR RAB design principles.     

基于压缩感知的圆阵自适应数字波束形成算法

根据目标在空间的稀疏性,在圆形面阵的接收端,提出了一种基于压缩感知的自适应数字波束形成算法.该算法在不改变波束性能与天线口径的前提下,可以大大减少实际的阵元数目,是一种新的稀布阵方法.在阵元稀布的情况下,根据压缩感知的压缩采样理论,先用重构算法恢复缺失通道的回波信息,然后利用恢复得到的信号计算自适应权系数,得到理想的自适应数字波束方向图.不同信噪比和干噪比情况下的仿真结果验证了所提算法的正确性和有效性.     

基于投影对消处理技术的稳健自适应波束形成

针对传统自适应波束形成算法中目标波达方向(Direction of Arrival,DOA)估计不准确引起的波束形成性能下降问题,提出了一种采用投影对消矩阵的稳健自适应波束形成算法.首先,寻找与估计波达方向有最大相关性的特征矢量作为目标信号特征矢量,然后构建对消矩阵消除协方差矩阵中的信号分量,最后通过增加零点约束实现干...     

Robust adaptive beamforming using multi-snapshot direct data domain approach

For the realistic case where there is no secondary snapshot that does not contain the desired signal and exhibits the statistical characteristics similar to the snapshot under test, direct data domain (D³) beamforming approaches have been proposed to estimate a desired signal in the presence of interference. However, the basic idea of the D³ methods is realized by making significant sacrifices with respect to the number of degrees of freedom (DoFs). In this paper, we present a multi-snapshot approach for D³ beamforming. Using the least-squares method with multiple snapshots, we can eliminate the interference without causing a severe reduction in the number of DoFs. In addition, to consider a mismatch between nominal and actual target steering vectors, we propose a D³ approach combined with a probability constraint to prevent the self-nulling effect, and the relationship between the probability constraint and norm constraint is discovered. The simulations verify that the proposed method provides better performance and robustness than the conventional D³ approaches.     

相干信号源自适应波束形成

介绍了常规自适应波束形成的一般理论,并在此基础上分析了期望信号与干扰信号相干时常规自适应波束形成失效原理,引入了空间平滑技术,给出了空间平滑技术去相干的原理和在自适应波束形成上的应用,同时给出了计算机仿真结果。     

Robust adaptive beamforming for large-scale arrays

For a large-scale adaptive array, heavy computational load and high-rate data transmission are two challenges in the implementation of an adaptive digital beamforming system. Moreover, the large-scale array becomes extremely sensitive to array imperfections. First, based on a restructured recursive linearly constrained minimum variance algorithm and a gradient-based optimization method, a new robust recursive linearly constrained minimum variance (RRLCMV) algorithm is proposed in this paper. The computational load of the RRLCMV algorithm is on the order of o(N), which is less than that of the conventional gradient-based robust adaptive algorithm. Then, a new efficient parallel robust recursive linearly constrained minimum variance (PRRLCMV) adaptive algorithm is proposed by appropriately partitioning the RRLCMV algorithm into a number of operational modules. It can be easily executed in a distributed-parallel-processing fashion, sequentially and in parallel. As a result, the PRRLCMV algorithm provides an effective solution that can alleviate the bottleneck of high-rate data transmission and reduce the computational cost. Finally, an implementation scheme of the PRRLCMV algorithm based on a distributed-parallel-processing system is also proposed. The simulation results demonstrate that the new PRRLCMV algorithm can significantly reduce the degradation due to various array errors.     

基于深度神经网络的自适应波束形成算法

阵列天线接收到的期望信号和干扰信号,其入射的到达角度(Angle of Arrival,AOA)总是快速变化的,而传统波束形成算法计算量大,无法实时计算。针对这一问题,提出了一种基于深度神经网络的自适应波束形成(Deep Neural Network Adaptive Beamforming,DNNABF)算法,用入射信号AOA组成的向量作为网络输入,网络输出逼近最小方差无失真响应(Minimum Variance Distortionless Response,MVDR)算法求得的权矢量。仿真结果表明,卷积神经网络(Convolutional Neural Network,CNN)与DNNABF方法都能准确拟合MVDR算法权矢量,可在入射信号AOA快速变化时自适应地形成波束和零陷,但DNN计算速度相对MVDR有将近6.5倍的提升,训练模型时间也远低于CNN。