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The rapid relative motion of satellites causes the burst spread-spectrum signals to produce a Doppler frequency shift. In low signal-to-noise ratio (SNR) environments, the traditional acquisition algorithms are limited in their abilities to capture large Doppler frequency shift when signals are seriously disturbed by noise. In view of the above problems, this paper designs a parallel burst spread spectrum signal acquisition algorithm based on pilot information and Partial Match Filter-Fast Fourier Transform (PMF-FFT). The algorithm utilizes pilot information and multi-path parallel processing method to achieve fast acquisition of Doppler frequency shift and PN code, which greatly reduces the acquisition time of burst signal. Firstly, the mathematical model and the acquisition loss of the algorithm are analyzed in detail. Then, the scallop loss of the acquisition system is reduced by zeroing the FFT. Finally, a method of the shortest synchronization segment based on a constant false-alarm rate and a specified detection probability is designed. The simulation results show that the acquisition probability of the proposed algorithm reaches 99 % for burst signals under the -31 dB signal-to-noise ratio when the false alarm probability is 10-6,which verifies the feasibility of the design method.

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卫星的快速相对运动会导致突发扩频信号产生多普勒频移。在低信噪比环境下,信号受噪声干扰严重,传统捕获算法对大范围多普勒频移的捕获能力受限。针对上述问题,本文设计了一种导频加部分匹配滤波和快速傅里叶变换(Partial Match Filter-Fast Fourier Transform,PMF-FFT)的并行突发扩频信号捕获算法。该算法利用导频信息以及多路并行处理实现对多普勒频移和码相位的快速捕获,大幅降低突发信号的捕获时间。通过对捕获算法的数学模型和捕获损耗进行详细分析,并对FFT进行补零以降低捕获系统的扇贝损失,本文最后设计了一种基于恒虚警率和指定检测概率的最短同步段的设计方案。仿真结果表明:虚警概率为10-6下,捕获算法对信噪比为-31 dB的突发信号捕获概率达到99%,验证了设计方案的可行性。

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魏鑫程 1999年生,硕士研究生。

李晓亮 1979年生,硕士,研究员。

马荣 1987年生,硕士,高级工程师。

任欢 1996年生,硕士,中级工程师。

平续斌 1982年生,博士,副教授。。

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任欢 1996年生,硕士,中级工程师。

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基于PMF-FFT的低信噪比突发扩频捕获方案设计
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魏鑫程 1 , 李晓亮 2 , 马荣 2 , 任欢 2 , 平续斌 1
遥测遥控 | 测控通信与导航 2025,46(2): 48-55
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遥测遥控 | 测控通信与导航 2025, 46(2): 48-55
基于PMF-FFT的低信噪比突发扩频捕获方案设计
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魏鑫程1, 李晓亮2, 马荣2, 任欢2, 平续斌1
作者信息
  • 1西安电子科技大学 西安 710071
  • 2北京遥测技术研究所 北京 100076
  • 魏鑫程 1999年生,硕士研究生。

    李晓亮 1979年生,硕士,研究员。

    马荣 1987年生,硕士,高级工程师。

    任欢 1996年生,硕士,中级工程师。

    平续斌 1982年生,博士,副教授。。

Design of Burst Spread Spectrum Acquisition Scheme Based on PMF-FFT With Low SNR
Xincheng WEI1, Xiaoliang LI2, Rong MA2, Huan REN2, Xubin PING1
Affiliations
  • 1.Xidian University, Xi'an 710071, China
  • 2.Beijing Research Institute of Telemetry, Beijing 100076, China
出版时间: 2025-03-15 doi: 10.12347/j.ycyk.20241118001
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卫星的快速相对运动会导致突发扩频信号产生多普勒频移。在低信噪比环境下,信号受噪声干扰严重,传统捕获算法对大范围多普勒频移的捕获能力受限。针对上述问题,本文设计了一种导频加部分匹配滤波和快速傅里叶变换(Partial Match Filter-Fast Fourier Transform,PMF-FFT)的并行突发扩频信号捕获算法。该算法利用导频信息以及多路并行处理实现对多普勒频移和码相位的快速捕获,大幅降低突发信号的捕获时间。通过对捕获算法的数学模型和捕获损耗进行详细分析,并对FFT进行补零以降低捕获系统的扇贝损失,本文最后设计了一种基于恒虚警率和指定检测概率的最短同步段的设计方案。仿真结果表明:虚警概率为10-6下,捕获算法对信噪比为-31 dB的突发信号捕获概率达到99%,验证了设计方案的可行性。

突发信号  /  信号捕获  /  捕获损耗  /  PMF-FFT

The rapid relative motion of satellites causes the burst spread-spectrum signals to produce a Doppler frequency shift. In low signal-to-noise ratio (SNR) environments, the traditional acquisition algorithms are limited in their abilities to capture large Doppler frequency shift when signals are seriously disturbed by noise. In view of the above problems, this paper designs a parallel burst spread spectrum signal acquisition algorithm based on pilot information and Partial Match Filter-Fast Fourier Transform (PMF-FFT). The algorithm utilizes pilot information and multi-path parallel processing method to achieve fast acquisition of Doppler frequency shift and PN code, which greatly reduces the acquisition time of burst signal. Firstly, the mathematical model and the acquisition loss of the algorithm are analyzed in detail. Then, the scallop loss of the acquisition system is reduced by zeroing the FFT. Finally, a method of the shortest synchronization segment based on a constant false-alarm rate and a specified detection probability is designed. The simulation results show that the acquisition probability of the proposed algorithm reaches 99 % for burst signals under the -31 dB signal-to-noise ratio when the false alarm probability is 10-6,which verifies the feasibility of the design method.

Burst signal  /  Signal capture  /  Capture loss  /  PMF-FFT
魏鑫程, 李晓亮, 马荣, 任欢, 平续斌. 基于PMF-FFT的低信噪比突发扩频捕获方案设计. 遥测遥控, 2025 , 46 (2) : 48 -55 . DOI: 10.12347/j.ycyk.20241118001
Xincheng WEI, Xiaoliang LI, Rong MA, Huan REN, Xubin PING. Design of Burst Spread Spectrum Acquisition Scheme Based on PMF-FFT With Low SNR[J]. Journal of Telemetry, Tracking and Command, 2025 , 46 (2) : 48 -55 . DOI: 10.12347/j.ycyk.20241118001
突发扩频通信凭借其良好的隐蔽性、抗干扰能力、抗多径衰落和易于实现码分多址等特点,被广泛应用于军事通信、卫星导航定位和遥测遥控等领域。随着天地一体化信息网络[1]建设的不断推进,海、陆、空、天用户突发接入需求不断增多,突发扩频信号的捕获需要更多地满足高动态、低信噪比的需求。在低轨卫星通信系统中,卫星运行轨道高度低,其运动速度极快,可达7.6 km/s。在这种高动态环境下,Ka和Ku频段信号的多普勒频移可高达700 kHz,但卫星体积受限以及噪声干扰等因素直接导致接收信号质量较差。这对接收机的捕获提出了更高的要求,如何在高动态和低信噪比环境下实现信号的快速捕获成为了提高卫星通信质量的关键技术。
扩频码的捕获主要受多普勒频偏和码相位不确定两种因素的影响,捕获算法的本质是通过对扩频信号和本地扩频码进行相关计算,利用相关结果峰值来获取扩频信号的频率和码相位信息。串行捕获算法[2,3]通过滑动的方式,对码相位和频偏进行二维扫描式搜索,该算法实现简单,只需几个相关器即可,但搜索速度慢。基于FFT的并行捕获算法[4-7]主要有码相位并行捕获算法和频率并行捕获算法,通过快速傅里叶变换,两种算法都将码相位和频偏的二维搜索简化为一维搜索,加快搜索速度。其中码相位并行捕获算法需要进行两次傅里叶变换和一次傅里叶反变换,计算资源消耗大,适用于长码捕获;频率并行捕获算法通过一次傅里叶变换即可完成频率域搜索,适合大频偏捕获。基于差分检测的捕获算法[8,9]用差分去除频偏的影响,对频偏不敏感,但差分会引入噪声平方项,对噪声敏感,不适用于低信噪比下信号捕获。基于PMF-FFT[10-13]的捕获算法采用部分匹配滤波器对信号进行分段相关积分,利用短时积分来提高捕获算法对大频偏的容忍度,增大频偏捕获范围,同时利用FFT计算可以实现频率的并行搜索。
突发信号持续时间短,盲补700 kHz的频偏是不现实的。因此本文先利用星历信息对多普勒频偏进行预补偿,缩小多普勒捕获范围。结合导频信息,利用PMF-FFT算法来实现频率的快速估计,同时对码相位采用多路并行搜索,减少搜索次数,进一步缩短捕获时间。本文对捕获算法过程损耗进行了详细分析,利用补零法降低算法的扇贝损耗,获得了将近2 dB的增益。结合过程损耗和相干增益,设计了一种基于虚警率和检测概率的最短同步段的设计方案以及自适应判决门限。通过仿真验证,该方案能够在信噪比为-31 dB、积分时间为15 ms的条件下完成突发信号的快速捕获,捕获范围为±10 kHz。
突发扩频信号[14-17]具有短时突发的特点,其信号起始位置不固定,具有随机性,且持续时间短,信号捕获难度大,采用导频的方式可以实现突发扩频信号的快速捕获。突发扩频信号的数据帧[18]结构如图1所示,主要由导频、同步头、数据段三部分组成:其中导频为单音序列,一般由全“1”或全“0”序列组成,其符号不会发生翻转,便于扩频码解扩,可以直接对频偏和码相位进行参数估计;同步头为一段独特序列,主要用于实现帧同步,通过检测同步头可以确定数据帧起始位置;信息段为不同调制方式的有用信息。
本文采用PMF-FFT算法对接收信号进行捕获,算法原理结构如图2所示。中频信号经过本地数字控制振荡器(Numerically Controlled Oscillator,NCO)进行正交下变频得到I、Q两路正交信号。再将I、Q两路信号平均分成M段,每段信号长度为X,分别送入部分匹配滤波器与本地码进行分段相关积分。随后对M个匹配滤波器输出的相关值做N (NM) 点FFT变换,对FFT输出结果进行平方检波并记录最大值。最后通过检测器进行判决,若最大值超过判决门限则说明扩频码已基本对齐,通过最大值出现的位置可以获取码相位和频偏的估计值;若最大值小于判决门限,则改变初始码相位重复上述过程。
接收机收到的中频信号由卫星信号和噪声两部分组成。假设d(k)为需要传输的数据码,c(k)为扩频码,fD为多普勒频偏,τ为码片时延,接收信号的初始相位为ϕ,噪声n(k)是均值为0、方差为σ2的高斯白噪声,采样时间为Ts,经过采样下变频得到的基带信号s(k)的表达式为:
由于导频的存在,数据相位不会发生翻转,即d(k)=1,当伪码完全对齐时,相关解扩结果c(k-τ)c(k)=1。忽略噪声影响时,信号经过部分匹配滤波器进行解扩,其归一化输出结果x(n)为:
对部分匹配滤波器的M点相关结果x(n)做N点FFT操作,其中N为2的正整数次幂,则第k点FFT输出结果的归一化幅频响应为:
式(3)中,L为序列长度(L=MX),ϕ′=exp(j(πfDTs(2X+1)-πk(M+2)/N)),为信号通过部分匹配滤波和FFT变换后的相位。PMF-FFT归一化增益可以拆分为由部分匹配滤波带来的增益GPMF(如图3所示)和FFT运算带来的增益GFFT(如图4所示)。
为便于理解算法增益,这里以符号速率为1 kb/s,伪码速率为1.024 Mb/s的信号为例进行说明。图3中,算法所能捕获的频偏范围与分段数和符号速率有关,随着分段数和符号速率增大,其捕获范围越来越大,捕获带宽为符号速率的M倍。当分段数不变,即积分时间相同时,随着频偏的不断增大,PMF模块的输出结果受相关损耗的影响不断减小,并且当频偏超过一定范围时,信号无法被检测。随着分段数的增加,单段积分时间变短,算法捕获带宽不断变大,适用于大频偏的捕获。但分段数的增加也意味着FFT点数增多,因此需要消耗更多的计算资源,在设计时需要合理选取分段数。
根据实际项目需求,需要针对信号速率为6 kb/s,伪码速率为3.072 Mb/s的信号展开设计,待捕获的多普勒范围为±10 kHz。同时需要在信噪比为-31 dB条件下完成捕获,虚警概率为10-6,检测概率需要达到99%。本方案对伪码进行四倍采样,采样时钟为12.288 Mb/s,采用半码片搜索,码相位搜索步进为0.5 chip。PMF-FFT算法的捕获损耗[19]包括:多普勒频偏导致的相关积分损耗、伪码同步损耗、FFT栅栏效应引起的扇贝损耗等。
下面对各种损耗进行分析。
① 相关积分损耗:即相关积分损耗为L1,如图5所示,其主要是由积分时间T和多普勒频偏fD共同决定(如式(5)所示)。为了确保信号能被成功捕获,接收机需要将相关损耗控制在3 dB范围之内,即fDT≤0.443。本方案将一个周期的伪码分为4段进行部分匹配滤波,积分时间T=1/240 00,其能捕获的多普勒范围为±10.632 kHz,因此满足方案要求,其相关积分损耗L1最大为:
② 伪码同步损耗:记伪码同步损耗为L2,如图6所示,扩频伪码具有良好的自相关性,当码相位完全对齐(即τ=0),同步损耗最小几乎为0。随着码片时延τ的不断增大,同步损耗逐渐增大。在传统捕获算法中,每个码片对应一个采样点,当采样点并非最佳采样点时,最大会产生0.5 chip的能量损失。本方案采用半码片搜索,将单个码片拆成两部分,分别与接收码进行相关解扩,此时码相位的搜索步长为0.5 chip,接收信号与本地伪码相位最大偏差为0.25 chip,和传统算法相比,能获得3.5 dB的增益,此时码相位不齐所引入的最大损耗为:
③ FFT扇贝损耗:即扇贝损耗为Lfft。FFT归一化增益见图4,当多普勒频偏为频点的整数倍,即频偏处于波峰时,几乎没有损耗。当多普勒频偏刚好处于波谷时,扇贝损耗达到最大,损耗过大会导致信号无法被检测,降低检测概率。当匹配滤波器输出点数和FFT输入点数相同(即M=N),且频偏位于波谷时,其扇贝损耗最大为:
扇贝损耗可以通过加窗法和补零法进行改善。这里采用工程上可简单实现的补零法来改进扇贝损耗。如图7所示,随着补零点数的不断增加,相邻两点FFT输出峰之间的间距不断减小,系统对频偏的分辨率不断增加,扇贝损耗不断减小。图8为补零后的最大扇贝损耗,当二倍补零后,系统的扇贝损耗从-3.92 dB减小到-0.9 dB,获得了将近3 dB的增益。补零法的缺点是会进一步增加FFT点数,增加计算资源的消耗。
本文采取平方律检波的方式对FFT输出结果的最大值进行判决。当没有卫星信号时,IQ两路只有高斯白噪声,检测值V服从自由度为2的中心卡方(χ2)分布;当有卫星信号时,IQ两路由卫星信号和噪声两部分组成,期望值不为0,此时检测值V服从自由度为2的非中心卡方(χ2)分布。其单次虚警概率Pfa和单次检测概率Pd[20]表达式如下:
式(8)中,σ2n为噪声功率,Vt为捕获门限,a为信号幅度,I0(•)为零阶修正的Bessel函数,Q1为1阶Marcum Q函数。由于FFT输出结果的统计变量属于独立同分布,且采用Max准则对FFT输出最大值进行判决,此时系统的虚警概率PFA和检测概率PD,其表达式如下:
公式(8)给出了输入信噪比(SNR)、捕获门限和检测概率的关系,其中捕获门限Vt可由虚警概率和噪声功率按照公式(10)计算得出,由捕获门限和检测概率可以确定捕获算法输入平方律检波所需的最小信噪比。不同虚警概率下信噪比和检测概率的关系如图9所示,在实际工程应用中,可以通过查表法进行方案设计,当虚警概率为10-6,检测概率为99%时,所需要的最低信噪比R1为14.5 dB。
在极低信噪比条件下,单码元积累能量有限,难以满足捕获要求,因此可以通过对多码元进行相干积累来提高信号的捕获信噪比。首先对单码元进行分段积分,随后将多个码元的分段积分结果进行FFT计算,可以对所有分段积分结果相位旋转后进行相干累加。参与相干积累的码元个数越多,相干增益越强,但会进一步降低传输效率,因此在保证相干增益的同时,需要采用最少数量的码元。
在确定同步头码元相干累加长度时,首先依据项目要求的虚警概率和检测概率,确定平方律检波所需的最小输入信噪比,随后考虑相关积分损耗、码片时延损耗及扇贝损耗,以此计算扩频增益,进而确定导频的最小长度。假设R0为接收信号的信噪比,fcode为伪码速率,fsym为符号速率,Nc为所需要的导频长度。综合考虑所有损耗,积分增益应满足以下条件:
由式(11)可知,为满足项目需求,至少需要90个码元的导频,即积分时间为15 ms的数据参与相干累加,本章节利用Matlab对第2章捕获设计方案进行仿真验证。算法对连续512个采样点(即1/4个符号的数据)进行相关积分,将15 ms积分时间的360个积分结果补零到512点进行FFT计算。多路并行码相位搜索,相邻两路码相位步进0.5 chip,1 024路FFT结果组成1 024×512的矩阵,寻找矩阵输出最大值与判决门限进行比较。单位噪声功率下判决门限Vt=40.107,去除FFT输出最大值后,对其他输出结果取平均获得噪声功率。实验结果如图10所示。
图10(a)图10(b)是无卫星信号时算法捕获结果,此时输出结果无明显峰值,且由图10(b)可以看出捕获门限要远大于只有噪声时的捕获最大值,算法不会将噪声信号误判为卫星信号。图10(c)图10(d)是有卫星信号时的算法捕获结果,此时频偏和码相位分别为4 687.5和120.5,均为分辨率的整数倍,该情况下扇贝损耗和码相位损耗最小,几乎为零,此时输出结果峰值明显,并且检测值要远高于捕获门限,算法捕获效果极佳。图10(e)图10(f)是所有损耗最大时的捕获结果,此时频偏和码相位分别为9 960.9和120.25,该情况下三种损耗均达到最大,此时检测值依旧高于捕获门限。在最恶劣的情况下,对补零改进后的算法进行1 000次蒙特卡罗仿真,其中有992次正确检测到信号,检测概率高于99%,在实际捕获中,损耗要小于该情况,捕获概率要优于指标要求。
图11给出了本文改进后算法、传统PMF-FFT算法以及并行码相位捕获三种算法的性能比较。
三种算法均在-31 dB下,对10 kHz的频偏进行捕获,图11(a)图11(b)分别为不同码元长度下的输出信噪比和检测概率。随着码元数目的增多,三种算法输出信噪比和检测概率均有所增加,相较于其他两种算法,本文改进后的算法具有更小的捕获损失。在相同码元数目下,本文算法具有更高的输出信噪比,同时算法检测概率的增长趋势更快,达到指定检测概率所需要的码元数目更少。
本文提出了一种基于PMF-FFT的突发扩频信号捕获算法设计方法,主要分析了信号捕获过程中的相关积分损耗、码片时延损耗和扇贝损耗。利用补零法对PMF-FFT捕获算法进行改进,降低了扇贝损耗。采用半码片搜索的方式,进一步提高了码片捕获精度。通过对多码元进行能量累加,完成了低信噪比突发信号的捕获,并给出了一种最短同步段的设计方法。通过仿真验证,在信噪比为-31 dB的条件下,得到了一套满足捕获指标的设计方案,捕获范围为±10 kHz,为后续突发捕获算法的方案设计提供了理论依据。
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doi: 10.12347/j.ycyk.20241118001
  • 接收时间:2024-11-18
  • 首发时间:2026-03-13
  • 出版时间:2025-03-15
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  • 收稿日期:2024-11-18
  • 修回日期:2025-01-01
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    1西安电子科技大学 西安 710071
    2北京遥测技术研究所 北京 100076
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2种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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