变换域自适应抗干扰方法及其FPGA实现

针对卫星扩频通信中的强窄带干扰,提出了一种基于频域处理的自适应窄带干扰抑制技术.该方法是将时域信号通过傅立叶变换形成一组近似正交的分量,在不相关的各频率点上分别进行LMS滤波.采用FPGA芯片EP2S60F48414完成自适应滤波器设计.结果表明:该频域算法运算量以及收敛性都优于时域滤波,具有很好的抗干扰性能.     

两种GPS天线阵列抗干扰算法研究及仿真

介绍了自适应天线阵列信号响应的数学描述,论述了在GPS抗干扰应用中广泛采用的基于最小均平方（LMS）的自适应算法的原理和实现过程,并对算法进行了仿真。提出了一种优于LMS算法的自适应数字波束形成（DBF）技术,推导出了其最优权系数矢量的计算方法,并对该算法进行了仿真。仿真结果表明：4阵元LMS的自适应调零算法对于单干扰的抑制和双干扰的抑制都达到了较好的效果,同时也体现了该算法零陷的优点。     

卫星导航信号窄带干扰抑制技术研究

介绍了基于预测的干扰抑制技术,简述了预测滤波器工作原理及最小均方误差算法（LMS算法）,针对LMS算法的不足,给出了LMS算法改进方案并将其应用于系统仿真模型。仿真结果表明：抑制滤波器有较好的窄带干扰抑制能力,改进的LMS算法能够有效地提高系统的抗干扰能力。     

大动态宽带干涉式雷电测向接收机新技术

提出了一种基于非线性盲辨识的自适应数字接收机技术。由于宽带雷电信号的时频特征未知或时变,在频域对宽带数字接收机输出信号中的谐波与互调分量进行识别和分选,并以其短时能量最小化作为其非线性行为模型参数的盲辨识准则,利用最速下降算法实现模型参数的自适应提取和更新,然后在线实时地对接收机输出信号进行非线性补偿。实验结果表明,该盲辨识数字接收技术可以将整机的无杂散失真动态范围（SFDR）提高近20dB,极有利于在强干扰存在时对微弱信号的接收与检测。     

一种抑制GPS射频干扰的空频自适应处理算法

为了提高窄带干扰与期望卫星信号同向时的抑制性能,提出了一种新的空频自适应滤波算法。该算法首先利用频域实系数自适应滤波对信号进行预处理抑制窄带干扰,然后在频域直接对处理后的信号进行空频自适应处理抑制宽带干扰。算法在预处理时采用自适应滤波可有效减少期望卫星信号的损失以及SFAP导向矢量误差。仿真实验表明,相对于普通的空时及空频自适应处理算法,新算法有效地提高了输出信干噪比,在干扰与信号同向的情况下也能表现出优良的处理性能。     

导航接收机线性阵列天线抗多径方法

本文提出一种导航接收机最小方差抗多径方法,针对均匀线阵空时二维滤波器结构,推导出权值求解公式,能使天线接收方向图在干扰信号的到达方向上形成窄的零陷,同时对地面反射的有用信号多径方向自适应地形成零陷。仿真结果验证了本文空时滤波结构抑制多径信号的有效性。算法可以应用在车载、机载、弹载抗干扰导航接收机设备,适应实际环境中存在复杂干扰以及多径信号等情况。     

GPS消除电子干扰和多路径效应的新方法

GPS信号是比较脆弱的，易受电子干扰和多路径效应的影响。就干扰和多路径效应对GPS的威胁进行了探讨，比较了传统与现代抗干扰技术的优缺点，重点介绍自适应DBF算法在GPS接收机部分的应用，依据是否使用导航辅助信号，将自适应DBF算法分为非盲和盲算法。自适应DBF算法并非万能，要根据接收机用途和具体使用环境，将该算法与时域滤波和频域滤波等技术有机结合起来，才能获得最佳抗干扰效果。     

基于DSP技术的GPS接收机天线自适应抗干扰模块的设计与实现

针对GPS接收机在接收信号的过程中经常存在由于干扰信号的影响而导致接收机无法跟踪锁定有用信号,从而失去精确导航定位的能力问题,提出了一种在天线部分增加一个自适应抗干扰模块的解决办法,并用TI公司的TMS320C6416芯片实现了该方案。     

噪声的分布层次分析及其自适应滤波算法

基于对噪声模型的分析 ,提出了两种自适应中值滤波器算法 :(1)对于消除噪声密度较大的脉冲干扰 ,提出了基于剩余脉冲检测的自适应中值滤波器算法 (AMF1) ,通过对滤波器输出是否存在剩余噪声的检测来决定采用的滤波器窗口尺寸 ,从而有效滤除脉冲干扰 ;(2 )对于消除的一定宽度的脉冲干扰 ,则提出了基于脉冲宽度检测的自适应中值滤波器算法 (AMF2 ) ,通过决定干扰脉冲的宽度来确定滤波器窗口的大小 ,从而有效去除较宽的干扰脉冲。通过对实际图像的测试 ,表明提出的算法运算结果优于标准中值滤波器的输出结果     

GPS码跟踪环自适应多径消除算法

根据多径信号的产生机理,在对GPS接收机中的码跟踪环多径信号模型研究的基础上,提出了采用自适应滤波的来消除GPS多径效应的算法。自适应滤波的方法不需要估计模型的系统参数,而直接通过自适应滤波将多径信号滤除。在有噪声的情况下,自适应滤波的RLS算法是最小二乘意义下的最优估计,仿真的结果表明采用自适应滤波算法可以快速的消除多径的影响,修正鉴相函数的过零点偏差,提高码跟踪环的跟踪精度。由于自适应滤波算法是递推算法,易于软、硬件实现。     

A new implementation of narrowband interference detection,characterization, and mitigation technique for a software-defined multi-GNSS receiver

Due to the very low power of satellite signals when reaching the earth’s surface, global navigation satellite system receivers are vulnerable to various types of radio frequency interference, and, therefore, countermeasures are necessary. In the case of a narrowband interference (NBI), the adaptive notch filtering technique has been extensively investigated. However, the research on the topic has focused on the adaptation of the notch frequency, but not of the notch width. We present a fully adaptive solution to counter NBI. The technique is capable of detecting and characterizing any number of narrow interfered bands, and then optimizing the mitigation process based on such characterization, namely the estimates of both interference frequency and width. Its full adaptiveness makes it suitable to cope with the unpredictable and diverse nature of unintentional interfering events. In addition to a thorough performance evaluation of the proposed method, which shows its benefits in terms of signal quality improvement, an analysis of the impact of different NBI profiles on GPS L1 C/A and Galileo E1 is also conducted.     

GNSS接收机抗干扰自适应调零技术性能估计

GNSS抗干扰技术中常采用功率倒置算法(PI)来得到自适应波束形成的零陷。信干比为80dB时,PI算法能准确识别干扰的方向,抗干扰分辨率好,但当信干比降低到20dB左右时,在射频干扰信号方向谱周围会形成大量带状的零陷,干扰信号的分辨率恶化严重。空间谱估计中的多重信号分类(MUSIC)算法具备超分辨率特性,通过信号子空间和噪声子空间的正交功率最小化原理,采用空间二维谱峰搜索方位角和仰角,能够准确进行DOA估计,有效区分有用信号和干扰信号。在高信干比条件下,基于MUSIC算法的最小功率估计抑制深度明显好于传统的PI算法;在低信干比条件下,MUSIC-PI算法在干扰信号方向谱判别及零陷抑制方面依然有效,而传统的PI算法失效。计算机仿真结果验证了该方法在GNSS抗干扰领域的有效性和鲁棒性。     

一种改进的SAR干涉图综合滤波算法研究

SAR干涉图作为相位信息的载体,其质量直接影响对研究区域形变状况的进一步分析,采取有效的滤波算法能抑制干涉图相位噪声,提高干涉测量精度。在获得的干涉相位图中,由于矿区开采而造成的地表沉降会体现出近环状相位条纹的特征。针对这一特点,对传统的基于梯度的滤波算法做出了改进,并结合Goldstein频域滤波和改进的梯度自适应滤波,提出了一种适用于矿区沉降形成的SAR干涉相位模式滤波方法。选取河北峰峰煤矿的PALSAR干涉相位图作为实验数据,对该滤波方法做出了详细的性能评价和对比。结果表明,采用本文提出的综合滤波方法在显著降低实验区SAR干涉图相位噪声的同时,也很好地保持了相位分辨率,使由于矿区沉降而造成的形变相位环的边缘形态更加清晰。     

Narrow-Band Interference Suppression for SAR Based on Complex Empirical Mode Decomposition

Narrow-band interference (NBI) is a common interference source in synthetic aperture radar (SAR) imaging. Its existence will degrade the imaging quality greatly. Based on detailed analysis on the characteristics of NBI, this letter proposes a new NBI suppression algorithm using the complex empirical mode decomposition (CEMD) method. In this algorithm, echoes that include NBI are recognized in the time domain first. Then, these echoes are decomposed into a number of intrinsic mode functions (IMFs) via the CEMD. After that, IMFs that correspond to NBI are subtracted from the echoes by thresholding. Finally, well-focused SAR imagery can be obtained from the separated target echoes using traditional SAR imaging algorithms. The effective data loss in this algorithm is smaller than other NBI suppression approaches. In addition, this algorithm is robust to time-varying NBI. Imaging results of measured data have proved the validity of this algorithm.      

Eigensubspace-Based Filtering With Application in Narrow-Band Interference Suppression for SAR

Synthetic aperture radar (SAR) has found wide applications in many areas, e.g., battlefield awareness. However, SAR is vulnerable to various kinds of interference, among which narrow-band interference (NBI) is commonly used. In this letter, an eigensubspace-based filtering approach is proposed for NBI suppression in SAR without using passive-sniff data as the reference signal. Moreover, the proposed method can deal with smart or interrupted NBI. Both simulation and experimental results are provided to illustrate the performance of the proposed approach     

Adaptive filtering of GOCE-derived gravity gradients of the disturbing potential in the context of the space-wise approach

Filtering and signal processing techniques have been widely used in the processing of satellite gravity observations to reduce measurement noise and correlation errors. The parameters and types of filters used depend on the statistical and spectral properties of the signal under investigation. Filtering is usually applied in a non-real-time environment. The present work focuses on the implementation of an adaptive filtering technique to process satellite gravity gradiometry data for gravity field modeling. Adaptive filtering algorithms are commonly used in communication systems, noise and echo cancellation, and biomedical applications. Two independent studies have been performed to introduce adaptive signal processing techniques and test the performance of the least mean-squared (LMS) adaptive algorithm for filtering satellite measurements obtained by the gravity field and steady-state ocean circulation explorer (GOCE) mission. In the first study, a Monte Carlo simulation is performed in order to gain insights about the implementation of the LMS algorithm on data with spectral behavior close to that of real GOCE data. In the second study, the LMS algorithm is implemented on real GOCE data. Experiments are also performed to determine suitable filtering parameters. Only the four accurate components of the full GOCE gravity gradient tensor of the disturbing potential are used. The characteristics of the filtered gravity gradients are examined in the time and spectral domain. The obtained filtered GOCE gravity gradients show an agreement of 63–84 mEötvös (depending on the gravity gradient component), in terms of RMS error, when compared to the gravity gradients derived from the EGM2008 geopotential model. Spectral-domain analysis of the filtered gradients shows that the adaptive filters slightly suppress frequencies in the bandwidth of approximately 10–30 mHz. The limitations of the adaptive LMS algorithm are also discussed. The tested filtering algorithm can be connected to and employed in the first computational steps of the space-wise approach, where a time-wise Wiener filter is applied at the first stage of GOCE gravity gradient filtering. The results of this work can be extended to using other adaptive filtering algorithms, such as the recursive least-squares and recursive least-squares lattice filters.     

融合小波变换和自适应Kalman滤波在GPS监测中的应用研究

针对自适应卡尔曼滤波只适用于滤除高斯分布的白噪声,本文提出了融合小波变换和自适应卡尔曼滤波的算法。该算法利用小波变换的多尺度分解,将GPS高频的监测时间序列进行多层分解,重构出新的GPS监测时间序列,将其作为新的自适应卡尔曼滤波初始值,进行滤波处理。将融合算法的滤波结果与单一的自适应卡尔曼滤波结果进行对比分析,结果表明融合算法的滤波效果较为显著。同时,对融合算法滤除的噪声信息进行统计分析,结果表明融合算法滤除的噪声符合正态分布,进一步说明了该融合算法的有效性,为GPS的高频率、高精度的监测提供了技术支持。