Non-Supervised Learning for Spread Spectrum Signal Pseudo-Noise Sequence Acquisition

An idea of estimating the direct sequence spread spectrum（DSSS） signal pseudo-noise（PN） sequence is presented. Without the apriority knowledge about the DSSS signal in the non-cooperation condition, we propose a self-organizing feature map（SOFM） neural network algorithm to detect and identify the PN sequence. A non-supervised learning algorithm is proposed according the Kohonen rule in SOFM. The blind algorithm can also estimate the PN sequence in a low signal-to-noise（SNR） and computer simulation demonstrates that the algorithm is effective. Compared with the traditional correlation algorithm based on slip-correlation, the proposed algorithm＇s bit error rate（BER） and complexity are lower.

A Neural Network Approach to Blind Estimation of PN Spreading Sequence in DS/SS Signals

In this paper, a new approach is proposed to estimate pseudo noise(PN) sequence in the lower SNR DS/SS signals blindly. This method utilizes the characteristics of self-organization, principal components analysis and extraction of unsupervised neural networks adequately, in addition to its higher-speed operation ability, successfully solve the difficult problem about PN sequence blind estimation. The theoretic analysis and experimental results show that this approach can work very well on lower SNR input signals.

Fast method for spreading sequence estimation of DSSS signal based on maximum likelihood function

To estimate the spreading sequence of the direct sequence spread spectrum (DSSS) signal, a fast algorithm based on maximum likelihood function is proposed, and the theoretical derivation of the algorithm is provided. By simplifying the objective function of maximum likelihood estimation, the algorithm can realize sequence synchronization and sequence estimation via adaptive iteration and sliding window. Since it avoids the correlation matrix computation, the algorithm significantly reduces the storage requirement and the computation complexity. Simulations show that it is a fast convergent algorithm, and can perform well in low signal to noise ratio (SNR).

Neural network approach to blindestimation of PN spreading sequence in lower SNR DS/SS signals

An approach based on discrete Karhunen-Loeve transformation of the DS/SS signals is proposed to estimate PN sequence in lower S/N ratio DS/SS signals. Characteristics of self-organization and principle components extraction of unsupervised neural networks are exploited adequately. Theoretical analysis and experimental results are provided to show that this approach can work well on the lower S/N ratio input signals.

Narrow-band interference excision in spread-spectrum systems using new N-sigma algorithm based on fuzzy threshold

The key to narrow-band interference excision in frequency domain is to determine the excision thresh- old in direct-sequence spread-spectrum （DS-SS） systems. The excision threshold is a non-linear function related to the number and the power of interference, and attempting to get the exact relation of threshold related to the number and the power of interference is almost impossible. The N-sigma algorithm determines the excision threshold using subsection function; however, the excision threshold determined by this method is not exact. A new method to determine the threshold of N-sigma algorithm is proposed. The new method modifies the scale factor N by use of the membership function. The threshold determined by this method is consecutive and smooth, and it is closer to the fact than that of the initial N-sigma algorithm. The GPS signal and single-tone （CW） interference （that is, typical narrow-band interference） are implemented in the simulation, and the results are presented to demonstrate the validity of the new algorithm.

基于SVD-K-means算法的软扩频信号伪码序列盲估计

针对通信中软扩频信号伪码序列盲估计困难的问题,提出一种奇异值分解(singular value decomposition,SVD)和K-means聚类相结合的方法。该方法先对接收信号按照一倍伪码周期进行不重叠分段构造数据矩阵。其次对数据矩阵和相似性矩阵分别进行SVD完成对伪码序列集合规模数的估计、数据降噪、粗分类以及初始聚类中心的选取。最后通过K-means算法优化分类结果,得到伪码序列的估计值。该算法在聚类之前事先确定聚类数目,大大减少了迭代次数。同时实验结果表明,该算法在信息码元分组小于5 bit,信噪比大于-10 dB时可以准确估计出软扩频信号的伪码序列,性能较同类算法有所提升。

基于圆周相关扩频信号的快速捕获改进方法

针对大多普勒频偏、低信噪比条件下长周期伪码的扩频信号捕获问题,提出了一种基于圆周相关扩频信号的快速捕获改进方法。该算法对圆周相关后的结果利用快速傅里叶变换(FFT)进行频率估计,扩大了传统圆周相关法的扫频步进,使扫频步进不再受跟踪环路带宽的限制。理论分析及仿真结果表明,在信噪比为-34 dB、多普勒频率为500 kHz条件下算法可以正确地估计出伪码相位和多普勒频偏。在达到相同的灵敏度条件下,所提算法的平均捕获时间与传统的圆周相关法相比缩短了约47%,与基于FFT的并行频率搜索法相比缩短了约88%。另外,用于频率估计的FFT点数仅占圆周相关点数的1%,故额外增加的硬件资源很少。算法逻辑控制简单,尤其适用于大多普勒频偏、低信噪比及长周期伪码的卫星通信系统。

低载噪比突发扩频信号的快速捕获设计

突发扩频信号是卫星无线电测定业务的一种扩频调制信号,针对传统突发扩频信号,即入站信号,对快速捕获灵敏度要求越来越高的需求,提出了一种低载噪比突发扩频信号的快速捕获硬件设计实现方法。利用同步头加Walsh码基于快速傅里叶变换(Fast Fourier Transform,FFT)的突发扩频信号全并行处理的方法,入站信号快捕接收的积分时长能够达到15 ms,相较于传统的捕获算法,性能实现了约2 dB的提升。仿真验证及工程实现结果表明,该方案载噪比可达到37.5 dBHz,为未来全球化小型化通用化接收终端提供了改进方向。

一种非相干扩频信号跟踪中HPK-P位同步捕获法

星载非相干扩频应答机在接收非相干扩频信号时,特别是在低信噪比条件下,信号跟踪环路中积分结果会因比特翻转而衰减严重,从而影响跟踪环路性能。针对上述问题,提出了一种HPK-P(Half-period Kokkonen-Pietila)位同步捕获法,将位同步时钟捕获精度缩短至半个伪码周期长,降低积分值衰减量,提高了跟踪前期的环路性能。仿真结果表明,与K-P法、半周期直方图法相比,HPK-P法更适合-24 dB低信噪比下的环路跟踪。在现场可编程门阵列(Field Programmable Gate Array,FPGA)上对HPK-P法进行测试,在信噪比为-24 dB,多普勒频偏范围为±1.5 kHz,多普勒加速度为+2.5 kHz/s条件下验证了该设计的可行性。

高动态短猝发扩频自适应信号快捕系统设计

在高动态环境下,短猝发信号传输过程中往往会产生普勒频偏,增加扩频信号的捕获时间,甚至降低信号捕获信噪比,为实现对扩频自适应信号的快速、有效捕获,设计高动态短猝发扩频自适应信号快捕系统;系统硬件采用模块化设计方式,改装信号接收模块和滤波模块,从信号缓存和锁相环两个方面调整系统电路;在硬件系统支持下,通过扩频过程的模拟确定高动态短猝发扩频信号特征,通过特征匹配实现对高动态环境中短猝发扩频信号的自适应搜索;计算捕获长度、频率、门限等参数,实现系统的短猝发扩频自适应信号快速捕获功能;实验结果表明,在无噪声条件下,优化设计系统输出扩频信号信噪比的平均值为141.6,捕获时间平均为8 s,捕获面积平均为90 km^(2);在有噪声条件下,系统输出扩频信号信噪比的平均值为121.8,捕获时间为10 s,捕获面积为130 km^(2)。

基于扩展频域时域反射法的在线光伏阵列故障检测信号特性研究

基于扩展频域时域反射法(Spread Spectral Time Domain Reflectometry,SSTDR)的光伏阵列故障诊断方法存在检测盲区和衰减特性,有必要研究检测信号的性质以提高故障检测性能。首先,对检测信号在光伏阵列中的传输行为进行研究,探究不同信号参数对检测范围和精度的影响;其次,根据光伏电池的动态模型和排布规律,搭建光伏阵列故障检测仿真平台,通过断路故障仿真实验对结果进行验证,结果表明,改善信号能有效增强相关峰辨识能力,使光伏组件检测数量增加4块;最后,综合考虑检测盲区和衰减特性对检测性能的影响,提出基于SSTDR的光伏阵列故障检测信号选择策略,用以确定测距范围和最优信号参数。

Approach to blind estimation of the PN sequence in DS-SS signals with residual carrier

This paper presents an approach of singular value de- composition plus digital phase lock loop to solve the difficult problem of blind pseudo-noise （PN） sequence estimation in low signal to noise ratios （SNR） direct sequence spread spectrum （DS-SS） signals with residual carrier. This approach needs some given parameters, such as the period and code rate of PN sequence. The received signal is firstly sampled and divided into non-overlapping signal vectors according to a temporal window, whose duration is two periods of PN sequence. An autocorrelation matrix is then computed and accumulated by those signal vectors one by one. The PN sequence with residual carrier can be estimated by the principal eigenvector of the autocorrelation matrix. Further more, a digital phase lock loop is used to process the estimated PN sequence, it estimates and tracks the residual carrier and removes the residual carrier in the end. Theory analysis and computer simulation results show that this approach can effectively realize the PN sequence blind estimation from the input DS-SS signals with residual carrier in lower SNR.

基于GAN的直扩信号生成算法

将深度学习模型应用至电子干扰技术来生成干扰信号具有重要的现实意义。将生成对抗网络(generative adversarial network,GAN)应用于信号生成领域,对电磁扩频信号频谱数据的分布进行深度学习,并生成与其相干的干扰信号。在实验中GAN的生成器和判别器互相博弈训练,通过自适应矩估计(adaptive moment estimation,Adam)进行优化,最终训练出良好的模型,可以生成所需信号。实验结果表明,基于GAN的信号生成算法生成的数据分布已基本具备真实数据分布普遍具有的特点,对同一信噪比的电磁频谱数据进行深度学习后,生成数据能够较为准确地学习到不同信噪比电磁频谱数据的不同特点。

一种突发DQPSK-DSSS信号解调方法

为了解决通信测控系统可靠性通信的问题,提出了一种将直接序列扩频和DQPSK相结合的通信方法。文章通过对突发信号的捕获、DQPSK解调两方面仿真和分析,验证了算法的性能。文章通过Vivado等工具进行了基于FPGA的硬件代码开发,在工程中实测,验证了算法的可行性,该方法具有很高的应用前景。

一种循环谱特征提取的直扩信号智能识别方法

为解决传统信号识别方法对直接序列扩频信号识别率低、智能化程度不高等问题,提出一种基于循环谱特征提取的直扩信号智能识别算法。在深入分析直扩信号循环谱特征基础上,以直扩信号循环谱独特的稀疏特性为依据,设计一种稀疏滤波-卷积神经网络模型对提取的循环谱等高线图进行识别,采用无监督预训练和有监督训练微调的方式对网络参数进行更新,提升网络对信号整体特征的表达能力和对小数据量信号样本的学习能力。仿真结果表明:本文算法能够有效识别直接序列扩频信号,在不低于-10 dB高斯噪声条件下对采用正交相移键控调制的直扩信号识别准确率达到98%以上;在混合其他调制信号条件下,相较于常见的几种深度学习算法,本文提出的算法具有更高的识别准确率和鲁棒性。

基于扩频方法的谐波雷达目标检测研究

针对谐波雷达探测非线性目标物,存在抗干扰能力差,虚警率高等问题,设计了一种基于扩频方法的谐波雷达收发系统。在发射端采用直接扩频的方法扩展信号的频谱,提高雷达信号的抗干扰能力。在接收端采用降采样滑动相关检测技术检测接收到的信号,完成对二次谐波信号的检测。相较于常规的滑动检测方法,当接收端抽取因子为4时,采用降采样滑动相关检测的计算量约为常规滑动检测方法的1/38,有效减少了系统的计算量。利用FPGA验证表明,雷达发射端合理有效,而接收机经过约50μs能检测到反射回的二次谐波信号。

基于CPPM-RSSI算法的可见光室内定位技术研究

针对如何提高室内定位精度问题,提出一种基于混沌脉冲位置调制(CPPM)和接收信号强度指示(RSSI)相结合的定位算法。利用二值混沌码作为扩频码,对LED灯的位置信息进行扩频处理,然后进行脉冲位置调制(PPM),将调制后的信号发送出去,接收端用相同的二值混沌码解扩恢复出每个LED的位置信息,通过光电检测器(PD)检测到来自各个灯的光强,再结合RSSI算法进行定位。仿真结果表明:在5 m×5 m×2 m的定位空间内,不扩频的情况下,最大误差为51 cm,最小误差为3 cm,平均误差为15.47 cm。随着扩频码长度增加,与之对应的误码率会降低,定位精度也随之提高,当扩频码长度为64位时,最大误差为10 cm,最小误差接近1 cm,平均误差为4 cm。

自适应传输技术在风机故障监测中的应用研究

对隧道中的轴流风机进行健康状态监测时,由于计算能力的限制,故障监测系统无法在本地进行故障类别的判断,需要通过无线传输方式使振动数据在接收端进行时频分析;由于隧道中传输环境复杂导致无线信号传输多径衰落严重,在这种情况下采用LoRa(long range radio,远距离无线电)扩频芯片搭建故障监测系统,其独有的扩频调制技术能使数据在恶劣环境下可靠传输,但是恶劣环境下的数据传输速率较低;针对该问题提出自适应传输机制,信道环境较好时增加采样频率和量化编码位数,传输更多的振动信息细节以提高故障判别的正确率;信道环境变差后减少上传的数据以减轻传输压力,并在不同信道环境下对该自适应传输策略的故障监测系统性能进行分析;最后通过实验分析,相较于普通的4G传输,添加自适应传输策略的LoRa故障监测系统,对于轴流风机的故障诊断可达到更高的正确率,更适合在隧道等恶劣环境下可靠高效传输数据。

基于完全互补码扩频的通信雷达一体化系统

海量的设备接入网络极易导致严重的频谱拥堵,通信雷达一体化是应对该挑战的关键方法之一。然而,如何有效应对多用户和多目标之间的自干扰和互干扰是通信雷达一体化亟待解决的基础难题。为此,借助码分复用机理,将完全互补码作为扩频码,设计通感一体化信号,构建了多用户和多目标场景下的通信雷达一体化方案。仿真结果表明,与典型的扩频方案相比,所提方案具有更低的误码率与更高的频谱效率;此外,当信噪比较低时,所提方案可以对更多目标进行探测。