CN104181552A - 一种动态gnss接收机的抗干扰正态零陷加宽的方法 - Google Patents
一种动态gnss接收机的抗干扰正态零陷加宽的方法 Download PDFInfo
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Abstract
本发明属于GNSS工程安全技术领域,涉及一种动态GNSS接收机的抗干扰正态零陷加宽的方法。本发明从正态分布的概率模型出发,提出了动态GNSS接收机的零陷加宽模型。考虑在小角度变化情况下将本模型线性化,采用Matlab仿真工具对比加宽前后的频谱增益。在功率倒置算法下,还分析了展宽前和展宽后输出SNR与输入INR的关系,最后通过FPGA+DSP平台验证提出算法的工程可实现性。结果表明加宽算法形成的加宽零陷可以有效地抵消掉一定范围内变化的干扰源,达到抗动态干扰的目的,提高了自适应算法的稳健性。
Description
技术领域
本发明属于GNSS工程安全技术领域,是一种干扰抑制技术,属于阵列天线抗干扰技术之列,具体涉及一种动态GNSS接收机的抗干扰正态零陷加宽的方法。加宽算法形成的加宽零陷可以有效地抵消掉一定范围内变化的干扰源,达到抗动态干扰的目的,提高了自适应算法的稳健性。
背景技术
目前导航接收机的抗干扰技术一般分为三类:自适应滤波技术,阵列天线抗干扰技术和接收机信号处理技术,本发明主要涉及到的是阵列天线抗干扰技术。
阵列天线抗干扰技术中的调零技术利用扩频系统将随机码信号深埋在热噪声中的特点,使进入接收机系统的总输入功率降至最低限度,可以有效地将强干扰电平降到低噪声水平,从而达到抗干扰的目的。波束形成技术将阵列天线的方向图最大增益对准GNSS卫星信号方向,进而提高信干比,达到抗干扰的目的。阵列天线抗干扰技术能使卫星导航接收机抗干扰能力提高至少50dB,是目前广泛应用的一种抗干扰手段。
阵列天线抗干扰技术的发展要追溯到上世纪70年代,最早使用的阵列天线都是基于模拟域的抗干扰系统,使用一系列模拟移相器,自适应地给各天线阵元信号进行加权。该移相器由于其模拟的本质,精度大打折扣。另一方面,权值一般须使用迭代算法计算出来,响应时间比较慢。这些因素导致模拟域中的抗干扰系统不能适应高强度的干扰环境。
随着现代数字技术DSP的高速发展,基于空域的接收机抗干扰技术也达到了前所未有的高潮。在数字域中的进行各阵元信号的加权(即相当于模拟域中的相移加权)可以非常精确。各天线阵元精确的相位控制可以使得自适应天线性能更佳,一方面,它可以使得调零自适应天线系统调零深度更深,另一方面,它可以使得自适应多波束系统的方向矢量增益更高。在数字域中的进行的加权均为复数权值加权,这使得数字抗干扰系统的响应速度得到很大的提高,信号处理速度比模拟系统快,能够适应高强度的干扰环境。
随着对飞行载体实时定位以及对导航的精度、灵敏度、可靠性等要求的不断提高,现代全球导航卫星系统(GNSS)接收机需具备在动态条件下进行定位、导航和抗干扰的能力,以达到较高的精度与灵敏度。干扰抑制技术一直是阵列信号处理领域的热点问题,在动态条件下,接收机的载体平台会发生的强烈震动和运动,GNSS接收机的姿态变化很快,使得接收机接收到的干扰来波的方向也迅速变化。但是由于自适应权值的更新速度相对较慢,算法的收敛速度跟不上干扰波达方向(DOA)变化的速度,使得干扰很容易移出天线零陷所指方向从而不能被抑制,导致一定的数据失配现象,不能保证GNSS接收机对卫星信号的正确捕获,使得常规的空域和空时域干扰抑制方法完全失效。
发明内容
本发明主要是解决现有技术所存在的技术问题;提供了一种可以有效地抵消掉一定范围内变化的干扰源,达到抗动态干扰的目的,提高了自适应算法的稳健性的一种动态GNSS接收机的抗干扰正态零陷加宽的方法。
本发明的上述技术问题主要是通过下述技术方案得以解决的:
一种动态GNSS接收机的抗干扰正态零陷加宽的方法,其特征是,基于一个动态GNSS接收机的零陷加宽模型,该模型是在常规静态输出功率最小约束模型的基础上,将干扰波的波达方向展宽为左右两个干扰信号同时入射,并定义失配角度服从正态分布,在小角度变化情况下,能够自适应计算出展宽权值;
其中,基于常规输出功率最小约束模型基于如下定义以及公式:定义模型由M个阵元组成,采用线性阵列信号模型,它所接收的信号波形对应M个输出信号向量X(t),其中包含接收信号向量中的干扰成分和噪声成分,离散化后为X(n);
定义输入信号的相关矩阵为 式一
式中,Rss为干扰信号的相关矩阵,为噪声功率
A=[a(θ0),a(θ1),a(θ2),…,a(θM-1)]表示为阵列流型,
a(θp)=[1,exp(jΦp),…,exp(j(M-1)Φp)]H,其中θp代表第p个干扰的波达方向,I为单位矩阵;
输出信号为Y=WX,W为输出权值;根据输出功率最小准则
得到常规输出功率最小约束模型自适应权值
然后,失配条件下,定义第p个干扰的波达方向为可展宽为左右两个干扰信号和同时
入射:
其中θp为实际干扰入射方向,Δθp为失配角度,假设Δθp服从均值为0,方差为的正态分布,即
构造零陷加宽后的自适应权值:
其中, 式五
其中
具体方法包括以下步骤:
步骤1,设置带喇叭天线的强干扰源,并设置仰角和方位角;采用七元的圆心阵,阵元间距为半个波长,将接收到的信号送入射频接收前端,信号通过射频前端调整后进入A/D转换模块;
步骤2,由A/D采样单元进行数据采样,采样数据送入数字混频模块;
步骤3,数字混频模块将带通采样后的数字中频信号变换为基带信号,然后送入低通滤波器,滤除高频分量,得到I/Q两路信号;
步骤4,针对步骤3得到信号基于零陷加宽模型进行如下子步骤的操作:
步骤4.1,将该信号送至DSP权值向量求解模块,令形成的加宽矩阵为 k取7;
步骤4.2,实验中采用FPGA求出信号的自相关值Rxx;自相关矩阵求逆放在型号为TMS320C6416的DSP模块中进行;实验中选择了计算量较小且占内存空间较小的Gauss-Jordan算法,在求逆之前先由式五求出矩阵并将中的元素赋给另一个矩阵H(i,j),整个N*N矩阵求逆完成后的值为R(i,j);为了验证求逆过程是否正确,实验中把原来的矩阵H(i,j)乘以逆矩阵R(i,j),如果H(i,j)*R(i,j)=I,I为单位矩阵,则说明求逆过程在DSP里实现正确;
步骤4.3,将Rxx代入式三求出展宽前的权值因子Wopt;再把求逆后的值R(i,j)代替代入式四中,求出展宽后的权值因子
步骤4.4,计算出零陷加宽最优权值向量并将此结果送回FPGA中的加权求和模块,将得到的加权系数与输入数字信号做相乘求和运算,从而达到高动态干扰抑制的目的;
步骤4.5,通过求出的权值对输入信号进行滤波,并相互比较;
步骤5:将干扰抑制后的运算结果输出到GPS接收系统进行导航信息的捕获跟踪、定位解算。
因此,本发明具有如下优点:1、当接收机处于运动状态时,可以有效地将抑制零陷对准一定角度范围内变化的干扰源,达到抗动态干扰的目的,提高了自适应算法的稳健性;2、具备工程可实现性,已在FPGA+DSP平台上硬件物理实现;3、主要运算量在矩阵求逆运算,通过DSP实现,保证了动态抗干扰算法的有效性和实时性,发明转化前景明确。
附图说明
图1为零陷展宽抑制算法效果图。
图2为零陷展宽前后干噪比与输出信噪比的关系。
图3为未加入展宽矩阵的捕获效果图。
图4为加入展宽矩阵后的捕获效果图。
图5为抗干扰接收系统结构图。
图6为抗干扰算法实现流程图。
图7为FPGA和DSP硬件计算展宽前零陷抑制效果图。
图8为FPGA和DSP硬件计算展宽后零陷抑制效果图。
图9为FPGA硬件平台的验证图。
图10为基于FPGA+DSP硬件平台的SignalTap实时效果图。
具体实施方式
下面通过实施例,并结合附图,对本发明的技术方案作进一步具体的说明。
实施例:
一、首先介绍一下本发明中涉及的两个模型,其中,
1.常规输出功率最小约束模型如下:
假设模型由M个阵元组成,采用线性阵列信号模型,它所接收的信号波形对应M个输出信号向量X(t),表示为
X(t)=S(t)+N(t),(0≤t≤T) (1)
S(t)记为接收信号向量中的干扰信号成分,N(t)记为噪声成分。式(1)离散化后,定义输入信号的相关矩阵为
式中Rss为干扰信号的相关矩阵,为噪声功率。A=[a(θ0),a(θ1),a(θ2),…,a(θM-1)]表示为阵列流型,其中θp代表第p个干扰的波达方向,I为单位矩阵。
a(θp)=[1,exp(jΦp),…,exp(j(M-1)Φp)]H (3)
式中Φp=2πd sinθp/λ,d为相邻两阵元间的距离,λ为信号波长。输出信号为Y=WX,W为输出权值。根据输出功率最小准则
构建拉格朗日约束方程得:
L(W)=WHRxxW+λ(WHA-1) (5)
其中λ为拉格朗日算子。令可得:
2.基于常规输出功率最小约束模型进行动态正态零陷加宽后的动态正态零陷加宽模型如下:
失配条件下,设第p个干扰的波达方向为可展宽为左右两个干扰信号和同时
入射:
其中θp为实际干扰入射方向,Δθp为失配角度,假设Δθp服从均值为0,方差为的正态分布,即首先考虑的情况,构造下面的平均协方差矩阵:
其中f(Δθp)表示Δθp的概率密度,rp为第p个干扰信号的功率,为M维方阵,有
式(9)中的第(k,l)个元素为
其中 利用公式 可求得
同理可得
当Δθp=0时,由公式(10)得到a(θp)aH(θp)=1
所以容易看出具有以下形式:
其中符号ο表示Hadamard积,矩阵和分别表示为
由于以上两式是在信号分成两部分的基础上得出的,将两个矩阵进行算术平均,可得
其中
因此,式(6)的自适应权值变成
二、下面介绍一下采用上述模型,进行具体抗干扰的方法案例:
接收机干扰抑制模块是由FPGA部分和DSP部分共同完成的,FPGA主要负责A/D采样、数字混频、低通滤波、基带I/Q分解、捕获跟踪、位置解算和导航电文输出;而DSP主要完成矩阵求逆、零陷展宽求解、加权求和。
基带板接收射频前端输出的50MHz中心频率、带宽6MHz的中频模拟信号,通过14位模数转换器(AD9251)量化为数字信号送给FPGA。A/D采样率为40.96MHz,根据带通采样定理,采样后的数字中频信号中心频率为9.04MHz,此后,FPGA与DSP进行数据通信。基带硬件板上的FPGA芯片为Altera公司Stratix III系列的一款芯片EP3SE110F1152I3。同时,系统也需要强大的数字信号处理的功能。这里选择使用DSP芯片来完成部分数据处理功能。图6是基带板的硬件平台,选用的是TI公司的定点DSP芯片TMS320C6416。
步骤1:基于FPGA/DSP的GNSS动态干扰抑制算法实现流程图如图6所示,七阵元的天线阵列将接收到的信号送入射频接收前端,信号通过射频前端调整后进入A/D转换模块。
步骤2:由A/D采样单元进行数据采样,采样数据送入数字混频模块。
步骤3:数字混频模块将带通采样后的数字中频信号变换为基带信号,然后送入低通滤波器,滤除高频分量,得到I/Q两路信号。
步骤4:将该信号送至DSP权值向量求解模块,计算出零陷加宽最优权值向量并将此结果送回FPGA中的加权求和模块,将得到的加权系数与输入数字信号做相乘求和运算,从而达到高动态干扰抑制的目的,具体的方法如下:
1、设置两个带喇叭天线的强干扰源,并设置仰角和方位角;
2、仿真采用N*N圆心阵,阵元间距为半个波长,不考虑方位角,只考虑仰角θ的变化。假设强干扰为互不相关的远场窄带干扰,进行静态干扰抑制仿真,高动态条件下,远场干扰的θ会发生变化,仿真中令形成的N*N加宽矩阵为k取1到N;
3、实验中采用FPGA求出信号的自相关值Rxx;
4、将Rxx代入公式(6)求出展宽前的权值因子Wopt,进行抗干扰滤波,图1虚线为仿真效果,可以看到在干扰来向上有较深的零陷,该零陷对准干扰,可将干扰准确抵消,另外将FPGA和DSP硬件计算得到的权值数据导入Matlab中,观测零陷抑制效果如图7所示;
5、自相关矩阵求逆放在型号为TMS320C6416的DSP模块中进行。实验中选择了计算量较小且占内存空间较小的Gauss-Jordan算法,在求逆之前先由公式(18)求出矩阵并将中的元素赋给另一个矩阵H(i,j),整个N*N矩阵求逆完成后的值为R(i,j)。
为了验证求逆过程是否正确,实验中把原来的矩阵H(i,j)乘以逆矩阵R(i,j),如果H(i,j)*R(i,j)=I,I为单位矩阵,则说明求逆过程在DSP里实现正确;
6、再把求逆后的值R(i,j)代替代入公式(20)中,求出展宽后的权值因子
7、通过求出的权值对输入信号进行滤波,并相互比较。图1中对比了展宽前后的零陷抑制情况,其中实线代表展宽后零陷抑制增益曲线。可以看出零陷对准干扰,展宽明显,但零陷变浅,另外将FPGA和DSP硬件计算得到的权重数据导入Matlab中观测零陷抑制效果如图8所示,图8相对于图7,零陷加宽效果明显。
对于阵列处理的干扰抑制,往往采用输出SNR和输入INR的对比进行系统性能测评。本文统一考虑抗干扰算法对卫星信号、干扰信号和白噪声的影响,输出信号为
Y=WX=W(S(n)+I(n)+N(n))=WS(n)+WI(n)+WN(n)
其中S(n)为接收信号向量中的卫星信号成分,N(n)为噪声成分,I(n)为干扰信号。
匹配空间的输出信噪比可计算为
图2为强窄带干扰下,经抗干扰抑制算法处理后,输出信噪比随干噪比的变化曲线,其中窄带干扰为远场干扰,星号曲线是展宽前的曲线图,圆圈曲线是展宽后的曲线图。当干噪比小于20dB时,不属于强信号干扰,当卫星信号受到弱信号干扰时,功率倒置抑制算法失灵,因此随着干扰信号的增强,输出信噪比急剧下降。当干噪比从20dB增长到55dB时,功率倒置算法抑制效果增强,输出信噪比提高显著。图2显示干噪比大于55dB后,展宽前后的输出信噪比有了区别。当干噪比在55dB到70dB之间时,展宽后比展宽前得到更优的信噪比;干噪比大于70dB后,展宽前算法保持较高的输出信噪比,但展宽后算法的输出信噪比恶化明显,这说明在高干噪比条件下,展宽算法是通过牺牲信噪比来达到零陷展宽的效果。
通过仿真分析干扰位置发生变化时,功率倒置算法对第22号卫星捕获的影响。图3为不加入零陷展宽矩阵时的效果图,图4为加入零陷展宽矩阵时的效果图。从两图对比可见,当干扰的DOA发生变化时,图3没有捕获卫星,而图4正常捕获卫星信号。
步骤5:将干扰抑制后的运算结果输出到GPS接收系统进行导航信息的捕获跟踪、定位解算。
测试现场以信号发生器作为干扰源产生干扰信号,通过一个喇叭天线对阵列天线进行照射,天线阵列采用七元带圆心的圆型阵列,其半径为半个波长。
实验中通过转动喇叭天线照射天线阵,相当于产生动态的窄带干扰,射频前端的输出中频信号进入基带板进行干扰抑制处理,实验平台采用ALTERA的EP3SE110F1152I3N芯片和TI的TMS320C6416GLZ芯片实现功率倒置抗干扰算法和零陷加宽算法。
通过FPGA的Modelsim仿真平台进一步验证了抗干扰算法的有效性。图9中显示的是数字中频信号送给基带信号处理器中的7个通道中的第一个通道,进行IQ分解和低通滤波后,进行抗干扰抑制算法处理。第一行和第二行是第一路混合输入信号经IQ分解后的信号波形,第三行和第四行为经过抗干扰算法后的IQ路卫星信号,第五行和第六行是输入混合信号减去卫星信号后剩余的强干扰信号。第三行和第四行刚开始没有卫星信号,第五行和第六行相应过一段时间才出现抗干扰的效果,这现象是由于开机上电阶段干扰抑制算法有延迟造成的。
将程序下载到实验板上,用SignalTap在线逻辑分析仪观察抗干扰结果。图10中AD1_a、AD1_b、AD2_a、AD2_b、AD3_a、AD3_b、AD4_a分别表示干扰和卫星的混合信号经七路天线、射频前端,并A/D采样后的输入信号波形。图中第八行和第九行分别代表经过数字混频、低通滤波和零陷展宽干扰抑制算法处理后输出的干扰信号和卫星信号。从图中可以看出干扰抑制前后的观测结果,本文采用的方法成功识别并分离了强干扰和卫星信号。由于卫星信号的功率远小于干扰的功率,所以第八行的干扰比第九行的卫星信号明显,硬件实验证明了本文提出的零陷展宽干扰抑制算法是有效的。
综上所述,动态条件下,强干扰信号的DOA变化明显,传统的功率倒置算法的抑制方向发生了错配,达不到抑制的效果。本发明提出的零陷展宽抑制算法,采用Matlab仿真工具对比加宽前后的频谱增益,结果表明零陷展宽效果明显,但深度变浅。通过分析输入INR和输出SNR的相互关系,可以看出INR低于20dB时,输出信噪比急剧下降,这是由于卫星信号受到弱信号干扰时,功率倒置抑制算法失效,当干噪比从20dB增长到55dB时,输出信噪比提高显著,功率倒置算法抑制效果增强;当干噪比从55dB增加到70dB时,展宽算法比未展宽算法得到更优的信噪比;当干噪比大于70dB后,未展宽算法保持较高的输出信噪比,但展宽算法的输出信噪比恶化明显,这说明在高干噪比条件下,展宽算法是通过牺牲信噪比来达到零陷展宽的效果。以上实验结果证明该加宽算法形成的加宽零陷可以有效地抵消掉一定范围内变化的干扰源,达到抗动态干扰的目的,提高了自适应算法的稳健性。
本文中所描述的具体实施例仅仅是对本发明精神作举例说明。本发明所属技术领域的技术人员可以对所描述的具体实施例做各种各样的修改或补充或采用类似的方式替代,但并不会偏离本发明的精神或者超越所附权利要求书所定义的范围。
Claims (1)
1.一种动态GNSS接收机的零陷加宽方法,其特征是,基于一个动态GNSS接收机的零陷加宽模型,该模型是在常规静态输出功率最小约束模型的基础上,将干扰波的波达方向展宽为左右两个干扰信号同时入射,并定义失配角度服从正态分布,在小角度变化情况下,能够自适应计算出展宽权值;
其中,基于常规输出功率最小约束模型基于如下定义以及公式:定义模型由M个阵元组成,采用线性阵列信号模型,它所接收的信号波形对应M个输出信号向量X(t),其中包含接收信号向量中的干扰成分和噪声成分,离散化后为X(n);
定义输入信号的相关矩阵为 式一
式中,Rss为干扰信号的相关矩阵,为噪声功率
A=[a(θ0),a(θ1),a(θ2),…,a(θM-1)]表示为阵列流型,
a(θp)=[1,exp(jΦp),…,exp(j(M-1)Φp)]H,其中θp代表第p个干扰的波达方向,I为单位矩阵;
输出信号为Y=WX,W为输出权值;根据输出功率最小准则
得到常规输出功率最小约束模型自适应权值
然后,失配条件下,定义第p个干扰的波达方向为可展宽为左右两个干扰信号和同时
入射:
其中θp为实际干扰入射方向,Δθp为失配角度,假设Δθp服从均值为0,方差为的正态分布,即
构造零陷加宽后的自适应权值:
其中, 式五
其中
具体方法包括以下步骤:
步骤1,设置带喇叭天线的强干扰源,并设置仰角和方位角;采用七元的圆心阵,阵元间距为半个波长,将接收到的信号送入射频接收前端,信号通过射频前端调整后进入A/D转换模块;
步骤2,由A/D采样单元进行数据采样,采样数据送入数字混频模块;
步骤3,数字混频模块将带通采样后的数字中频信号变换为基带信号,然后送入低通滤波器,滤除高频分量,得到I/Q两路信号;
步骤4,针对步骤3得到信号基于零陷加宽模型进行如下子步骤的操作:
步骤4.1,将该信号送至DSP权值向量求解模块,令形成的加宽矩阵为 k取7;
步骤4.2,实验中采用FPGA求出信号的自相关值Rxx;自相关矩阵求逆放在型号为TMS320C6416的DSP模块中进行;实验中选择了计算量较小且占内存空间较小的Gauss-Jordan算法,在求逆之前先由式五求出矩阵并将中的元素赋给另一个矩阵H(i,j),整个N*N矩阵求逆完成后的值为R(i,j);为了验证求逆过程是否正确,实验中把原来的矩阵H(i,j)乘以逆矩阵R(i,j),如果H(i,j)*R(i,j)=I,I为单位矩阵,则说明求逆过程在DSP里实现正确;
步骤4.3,将Rxx代入式三求出展宽前的权值因子Wopt;再把求逆后的值R(i,j)代替代入式四中,求出展宽后的权值因子
步骤4.4,计算出零陷加宽最优权值向量并将此结果送回FPGA中的加权求和模块,将得到的加权系数与输入数字信号做相乘求和运算,从而达到高动态干扰抑制的目的;
步骤4.5,通过求出的权值对输入信号进行滤波,并相互比较;
步骤5:将干扰抑制后的运算结果输出到GPS接收系统进行导航信息的捕获跟踪、定位解算。
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