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.

High-Sensitivity Acquisition of Ultrahigh Dynamic Direct Sequence Spread Spectrum Signals in Space Communications

According to the requirements of the high-sensitivity acquisition of Direct Sequence Spread Spectrum(DSSS) signals under ultrahigh dynamic environments in space communications, a three-dimensional joint search of the phase of Pseudo-Noise-code(PN-code),Doppler frequency and its rate-of-change is presented to achieve high sensitivity in sensing high-frequency dynamics. By eliminating the correlation peak loss caused by ultrahigh Doppler frequency and its rate-of-change offset,the proposed method improves the acquisition sensitivity by increasing the non-coherent accumulation time. The validity of the algorithm is proved by theoretical analysis and simulation results. It is shown that signals with a carrier- to-noise ratio as low as 39 dBHz can be captured with high performance when the Doppler frequency is up to ±1 MHz and its rate-of-change is up to ±200 kHz/s.

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.

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.

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.

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。

Chirp-DS-BPSK雷达通信一体化波形设计

针对线性调频-二进制相移键控(Chirp-Binary Phase Shift Keying,Chirp-BPSK)雷达通信一体化信号存在模糊函数受通信数据严重影响的问题,分析了传统周期扩频、混沌序列扩频及Logistic映射混沌序列的自相关性能,设计了一种扩频信号与线性调频脉冲相结合的雷达通信一体化共用波形,即Chirp-DS-BPSK一体化信号。该一体化波形分别采用混沌序列和BPSK方式对通信数据进行直接序列扩频与调制,以线性调频信号作为载波,并采用脉冲成型滤波器来提高频谱利用率,降低脉压旁瓣。最后,通过仿真分析证明了所提波形具有良好的模糊函数特性和误码率性能。在扩频比为7的条件下,与传统的Chirp-BPSK波形相比,Chirp-DS-BPSK波形的脉压峰值旁瓣比降低了约2.69 dB,通信误码率下降约8.45 dB,表明Chirp-DS-BPSK比Chirp-BPSK更适合作为雷达通信一体化共用波形。

复杂环境下的DSSS信号抗均匀频谱干扰研究

随着直接序列扩频(DSSS)信号的广泛应用,复杂环境下的DSSS信号抗干扰研究尤为重要。以直扩系统中常见的均匀频谱信号为例,进行信号抗干扰研究。首先分析均匀频谱信号特性,进而设计直扩信号同步环路模型,通过均匀频谱信号带宽、中心频偏、能量的有机配置,研究均匀频谱信号对DSSS信号同步环路的影响,给出不同条件下最佳抗干扰方案,为通信接收机抗干扰及保障技术提供理论基础。

一种DS/SS信号PN码序列估计的神经网络方法

本文提出了一种利用神经网络（Ｎ．Ｎ．）完成直接序列扩频（ＤＳ／ＳＳ）信号 ＰＮ（伪噪声码）码序列估计的新方法。该方法充分利用了无监督Ｎ．Ｎ．的自适应主元提取特性，加上其高速运算能力，解决了传统方法在ＰＮ码的实时处理及实现上的困难，理论分析和数值结果表明了本方案能在较低的输入信噪比条件下完成对ＰＮ码的正确估计。

基于四阶累积量的DSSS/QPSK信号检测方法研究

根据高斯白噪声的四阶累积量恒为零这一特性,研究了DSSS/QPSK信号的四阶累积量,提出了基于四阶累积量不同切片c4x(0,0,0)、c4x(τ,τ,τ)、c4x(0,τ,τ)和c4x(0,0,τ)的时域和频域检测方法.仿真结果表明,频域检测方法性能优于时域检测方法;综合考虑检测性能和实现难度,基于c4x(0,0,τ)的频域检测方法最好,在虚警概率为1%,信噪比为-11dB时,检测概率仍可达到50%以上.没有引入大规模的矩阵运算,运算量小,易于实现.

DSSS信号的扩频序列估计

提出了一个在非协作通信中对直接扩频序列(Direct Spread Spectrum Sequence,DSSS)信号的扩频序列进行估计的简单而有效的算法。在算法中我们提出了两个测度函数,一个用于搜索扩频序列在接收信号中的起始位置,另一个用于序列中的逐码片(chip)估计。理论分析和计算机仿真表明了此算法的有效性。

基于双PN周期的短码DS-SS信号扩频波形及信息序列盲估计方法

针对短码DS-SS信号盲解扩,研究了DS-SS信号的扩频波形及信息序列盲估计问题,提出一种基于双PN周期分解的扩频波形与信息序列联合盲估计算法,该算法在低信噪比条件下同时完成扩频波形和信息序列的估计,计算量小,且避免传统方法利用单PN周期分解时扩频序列连接的相位模糊等问题,提高了盲估计的正确率。最后仿真验证了算法的有效性。

使用混沌扩频序列增强DS/SS系统性能

基于Logistic映射,利用交织器设计扩频序列发生器,其产生的混沌序列具有良好的相关特性和旁瓣特性,并且是不必寻优而自身平衡的。将这种序列作为扩频序列应用于直接序列扩频系统中,能够有效地减少多址干扰,提高系统的容量,增强系统性能,信息也能够被加密。将这种序列和传统的扩频序列以及普通的混沌序列应用于扩频系统,仿真结果表明,设计的混沌序列性能优于其他的扩频序列。

一种DS/SS信号盲同步算法

提出了一种DS/SS信号盲同步算法,在此基础上利用特征分析法可解决相位不确定性的缺陷。该算法简单有效、计算量小,利于硬件实时处理。理论分析及计算机仿真结果表明,该算法在低信噪比条件下可实现DS/SS信号的盲同步及PN码序列的精确估计。

窄带干扰环境下DS-SS信号的细微特征分析

通过对窄带干扰(NBI)条件下低信噪比直接序列扩频(DS-SS)信号的细微特征分析,得到了NBI特征波形和DS-SS扩频伪码(PN)的盲估计。首先按PN码周期和码速率(假设已获知)对接收DS-SS信号进行取样预处理形成观察向量集,接着用细微特征分析方法对该集合进行处理,最后可以估计出信号所夹杂的NBI特征波形和信号的PN码序列。为了减少所估计PN码的相位模糊性,本文在取样预处理时将取样时间窗口增大到PN码周期的二倍,结果使所估计的PN码或是原码或是反码,从而提高了估计准确性。同时利用所估计的NBI特征波形可消除原始信号所夹杂的NBI,而利用所估计的PN码序列则可以对DS-SS信号实施盲解扩处理。理论分析和数值结果表明,该方法有效,并能工作在较低的输入信噪比和信号干扰比环境下。

低复杂度的DSSS信号扩频序列估计算法

在非协作条件下直接序列扩频(DSSS)信号的扩频序列估计是盲解扩的必需环节。通过构造基于接收信号自相关矩阵的两个检验统计量,分别完成了扩频序列的同步和扩频序列的估计。所提的算法具有很低的运算复杂度。仿真实验验证了所提算法性能的有效性。综合算法的鲁棒性、计算复杂度和低信噪比下的估计性能等评价因素,算法相比其它的典型算法有着一定程度的优越性,并易于快速实现。

基于DS-SS信号相位特性的参数估计

利用DS-SS信号符号间相位信息,提出了一种可工作于低信噪比下的直接序列扩谱(DS-SS)信号噪声抑制方法。该方法在相参积累抑制噪声的同时,还完整地保留了信号特征,并实现QPSK信号的I/Q分量分离。结合参数估计方法,可进行DS-SS信号载频和码速率估计。计算机仿真证明了该方法的有效性。

基于延迟相乘和能量累积的DSSS信号参数估计算法

针对直接序列扩频(direct sequence spread spectrum,DSSS)信号截获和参数盲估计问题,提出了一种采用延迟相乘和能量累积实现DSSS信号参数估计的新方法。该算法采用延迟相乘估计伪随机码(pseudonoise,PN)周期,将延迟相乘序列分成连续的片断,通过累积能量最大值搜索估计信息码周期,实现了PN码序列和信息序列的估计。理论分析和仿真结果表明,在加性高斯白噪声条件下,对扩频增益为GpdB的DSSS信号,在信噪比高于-Gp/2 dB的条件下,该算法能够实现稳定的参数提取。该算法不需要预先设定门限和参数,适合采用硬件设计实现。

基于相位相关的低信噪比下BPSK DS/SS检测方法

基于相位相关的检测方法把相位差累积量作为检验统计量,通过检测载波二倍频信号来实现对BPSK DS/SS信号的检测,具有检测迅速并能够在低信噪比条件下工作的优点。文中对该方法进行理论推导和证明,给出了延时、采样速率、搜索时间和判决门限等参数的选择方法。仿真结果表明,基于相位相关的检测方法在恒虚警概率为0.01,判决时间为毫秒级,信噪比为一15 dB时,检测概率可达90%以上,同时还能够对直扩信号的载频做出粗略估计。