基于有限新息率采样理论的超宽带信道估计

提出利用有限新息率采样理论解决超宽带信道估计问题,建立了可用于超宽带信道估计的框架,此框架的抽样率低于奈奎斯特速率.通过实例展示了科用奈奎斯特速率抽样而工作在低于奈奎斯特抽样率的算法性能,减少了功耗,也降低了硬件的复杂性.     

基于压缩感知的脉冲超宽带信号检测

传统的信号检测算法基于奈奎斯特采样定理来实现,这对于带宽极宽的超宽带(ultra-wideband,UWB)信号而言由于要求采样速率过高而很难用硬件去实现。为此,本文研究了基于压缩感知(compressive sensing,CS)的脉冲超宽带(impulse radio UWB, IR-UWB)信号检测问题,利用IR鄄UWB 信号在时域上的稀疏特性,设计了一种基于压缩感知的IR鄄UWB 信号检测框架,在此基础上提出了一种自适应加权正交匹配追踪检测算法。仿真结果表明,新算法不仅能够通过远少于奈奎斯特定理所要求的采样速率检测出IR-UWB 信号,而且与基于匹配追踪的压缩感知检测算法相比,新算法在低信噪比的情况下对IR-UWB 信号的检测效果更佳。     

基于频域时延-多普勒二维聚焦的欠采样雷达信号参数估方法

针对欠采样脉冲多普勒雷达信号参数估计中已有方法抗噪性差、顺序参数估计方法中后续参数估计受前面参数估计精度影响严重等问题，该文提出一种基于有限新息率(Finite Rate of Innovation, FRI)采样的频域时延-多普勒2维聚焦(FD2TF)算法。在该算法中，利用FRI采样结构能够以低于奈奎斯特采样频率的速率获得信号的一系列傅里叶系数，通过频域2维聚焦过程能够同时估计时延和多普勒参数，避免了参数顺序估计中误差累积的问题，理论分析证明了该算法能够大幅提升采样信号的信噪比，提高算法抗噪性和鲁棒性。在2维聚焦算法的基础上该文还提出了基于逆傅里叶变换的2维聚焦简化算法，在提高参数估计网格密度的同时，大大减低了2维聚焦算法的计算量。仿真和对比实验结果证明了该方法的有效性和良好的抗噪性。     

基于频域时延-多普勒二维聚焦的欠采样雷达信号参数估方法

针对欠采样脉冲多普勒雷达信号参数估计中已有方法抗噪性差、顺序参数估计方法中后续参数估计受前面参数估计精度影响严重等问题,该文提出一种基于有限新息率(Finite Rate of Innovation,FRI)采样的频域时延-多普勒2维聚焦(FD2TF)算法.在该算法中,利用FRI采样结构能够以低于奈奎斯特采样频率的速率获得信号的一系列傅里叶系数,通过频域2维聚焦过程能够同时估计时延和多普勒参数,避免了参数顺序估计中误差累积的问题,理论分析证明了该算法能够大幅提升采样信号的信噪比,提高算法抗噪性和鲁棒性.在2维聚焦算法的基础上该文还提出了基于逆傅里叶变换的2维聚焦简化算法,在提高参数估计网格密度的同时,大大减低了2维聚焦算法的计算量.仿真和对比实验结果证明了该方法的有效性和良好的抗噪性.     

基于多速率欠采样的超宽带LFM信号参数估计

针对基于多速率欠采样的超宽带LFM信号瞬时频率估计方法中,余数的很小测量误差将导致很大的测频误差问题,提出一种基于欠采样的超宽带LFM信号参数估计算法。该算法在多速率欠采样方法的基础上,对错误的瞬时频率估计值进行重估计,剔除大误差后通过线性拟合的方法可在-6 dB信噪比环境下稳定地对线性调频信号进行参数估计。计算机仿真实验验证了该方法的有效性。     

基于压缩感知的多发多收高分辨SAR成像算法研究

压缩感知理论指出,稀疏信号可以通过以低于奈奎斯特采样的测量数据重建出原始信号。针对高分辨率SAR成像在奈奎斯特理论下所面临的高速A／D采样、大数据量存储、传输等问题挑战。本文提出了一种基于压缩感知理论的多发多收高分辨率SAR二维成像算法。该算法减轻了高分辨率SAR成像的压力,采用压缩感知处理降低了A／D采样速率、数据量...     

基于贝叶斯压缩感知UWB单站定位方法

脉冲超宽带(IR-UWB)能够在无线定位中取得较高的精确度,但是存在ADC瓶颈问题,利用压缩感知理论(CS)对信号压缩采样可以显著降低信号采样速率。本文将贝叶斯压缩感知应用于UWB单站定位,接收节点利用L型天线阵列接收信号,对信号压缩采样,由贝叶斯压缩感知重构算法(BCS)还原信号并估计时延参数,最后由定位算法解算位置信息。基于IEEE 802.15.4a信号模型的仿真结果表明,该方法最低能以20%的奈奎斯特采样速率获得分米级的定位精确度。     

差分调制信号亚奈奎斯特率检测方法

压缩感知是一种基于亚奈奎斯特率的信息采样方法。基于压缩感知的符号检测方法通常先将亚奈奎斯特率样本重构为奈奎斯特率信号,然后再依据传统符号检测的原理检测接收符号。本文针对基于重构的压缩感知符号检测方法采样率过高的问题,研究广义似然比检测和信息采样样本之间的关系,提出了一种不需要重构奈奎斯特率信号的压缩检测方法。该方法首先通过双通道时延结构分离接收信号的参考部分和信息符号部分,然后依据两部分信号的稀疏相关特性,对亚奈奎斯特率接收符号进行检测。实验结果说明本文提出的方法能够有效地抵抗多径衰弱和符号间干扰（Inter Symbol Interference,ISI）。      

基于双路多本振SNYFR 的多分量信号检测及参数估计

奈奎斯特折叠接收机(NYFR)为一种新颖的超宽带接收机结构,它可以利用单片ADC实现超宽带范围内信号的采集,而同步奈奎斯特折叠接收机(SNYFR)为其一种改进结构。文章针对SNYFR难于实现的缺点,提出了一种双路多本振的SNYFR结构。并在此结构下提出了基于正弦调频匹配(SFMM)的多分量单频信号奈奎斯特区域(NZ)判定算法,同时给出了相应参数估计方法。经计算机仿真证明,在监测带宽为S+C+X波段的情况下,该结构结合文中提出的算法可以有效地对多分量单频信号进行检测并实现各分量参数的高精度估计。     

MWC欠采样方法中伪随机序列的改进

最近提出的调制宽带转换器(MWC)采样方法,针对稀疏宽带模拟信号可以实现低于奈奎斯特率的不失真采样,为稀疏宽带信号采样率高的问题提供了一种欠采样方案。文中针对MWC系统在实现过程中伪随机符号序列不易精确实现的问题,提出了一种易于实现的0/1伪随机序列,简化了MWC的实现方法。通过数值仿真对MWC系统重构性能进行了检验。实验结果表明,MWC系统采用所提出的伪随机序列时仍然可以实现低于奈奎斯特率的采样,并能高概率地精确重构原信号。     

Sub-Nyquist Sampling and Parameters Estimation of Wideband LFM Signals Based on FRFT

Last years, most sub-Nyquist sampling and parameters estimation methods for linear frequency modulated (LFM) signals are based on compressed sensing (CS) theory. However, nearly all CS reconstruction algorithms are with high computational complexity and difficult to be implemented in hardware. In this paper, a novel framework of sub-Nyquist sampling and low-complexity parameters estimation for LFM signals is proposed. The incoherent sampling in CS theory is introduced into the construction of sub-Nyquist sampling system, but no CS reconstruction algorithm is employed in the estimation of parameters. Based on the energy aggregation of LFM signals in the proper fractional Fourier transform (FRFT) domain, the chirp rate and center frequency can be estimated by linear operations. Accordingly, the proposed estimation method is easily realized compared with existing estimation methods based on CS. Simulation results verify its effectiveness and accuracy.     

Model-Based Multichannel Compressive Sampling with Ultra-Low Sampling Rate

The emerging compressive sampling (CS) theory makes processing ultra-wide-band (UWB) signal at a low sampling rate possible if the underlying signal has a sparse representation in a certain basis. The feasibility of model based compressive sampling for ultra-wide-band (UWB) signal is investigated. In this paper, a multichannel compressive sampling architecture is developed to capture UWB signal at a rate much lower than Nyquist rate. The proposed framework considers sub-Nyquist sampling stream of delayed and weighted versions of a known signal with finite support in time domain. A basis function is constructed to realize sparse signal representation. To reduce the hardware cost, a segmented architecture is suggested. In addition, a joint signal recovery algorithm is presented. Experimental results indicate that, with this system, a UWB signal sampled at about 4% of Nyquist rate still can be recovered with overwhelming probability.     

UWB Echo Signal Detection With Ultra-Low Rate Sampling Based on Compressed Sensing

A major challenge in ultra-wide-band (UWB) signal processing is the requirement for very high sampling rate. The recently emerging compressed sensing (CS) theory makes processing UWB signal at a low sampling rate possible if the signal has a sparse representation in a certain space. Based on the CS theory, a system for sampling UWB echo signal at a rate much lower than Nyquist rate and performing signal detection is proposed in this paper. First, an approach of constructing basis functions according to matching rules is proposed to achieve sparse signal representation because the sparse representation of signal is the most important precondition for the use of CS theory. Second, based on the matching basis functions and using analog-to-information converter, a UWB signal detection system is designed in the framework of the CS theory. With this system, a UWB signal, such as a linear frequency-modulated signal in radar system, can be sampled at about 10% of Nyquist rate, but still can be reconstructed and detected with overwhelming probability. The simulation results show that the proposed method is effective for sampling and detecting UWB signal directly even without a very high-frequency analog-to-digital converter.     

一种超宽带信道估计算法及其特性

在传承经典谱估计和结构化线性系统思想的基础上,介绍了一种基于子空间的超宽带(Ultra-Wide Band,UWB)信道估计(Channel Estimation,CE)快速算法。该算法在低于Nyquist采样率的情况下可获得较高分辨率的信道估计,从而可降低系统的复杂度,减小系统的功耗。     

MWC欠采样方法中伪随机序列的改进

最近提出的调制宽带转换器（MWC）采样方法,针对稀疏宽带模拟信号可以实现低于奈奎斯特率的不失真采样,为稀疏宽带信号采样率高的问题提供了一种欠采样方案.文中针对MWC系统在实现过程中伪随机符号序列不易精确实现的问题,提出了一种易于实现的0/1伪随机序列,简化了MWC的实现方法.通过数值仿真对MWC系统重构性能进行了检验.实验结果表明,MWC系统采用所提出的伪随机序列时仍然可以实现低于奈奎斯特率的采样,并能高概率地精确重构原信号.     

基于随机解调器压缩采样的宽带频谱检测方法

宽带频谱检测的目的是完成对宽频段内信号的检测。在Nyquist采样理论下,为瞬时覆盖这么宽的带宽会对模数转换器（ADC,analog—to-digital）的采样率提出过高要求。研究了基于随机解调器压缩采样的宽带频谱检测方法。该方法能够以低于Nyquist采样率的速率完成对宽频段的采样,降低了ADC的负担。仿真结果表明,在频谱满足稀疏性的条件下,所研究的方法能够较准确检测宽频段内的各个信号。     

Optimal sub-Nyquist nonuniform sampling and reconstruction formultiband signals

We study the problem of optimal sub-Nyquist sampling for perfect reconstruction of multiband signals. The signals are assumed to have a known spectral support ℱ that does not tile under translation. Such signals admit perfect reconstruction from periodic nonuniform sampling at rates approaching Landau's (1967) lower bound equal to the measure of ℱ. For signals with sparse ℱ, this rate can be much smaller than the Nyquist rate. Unfortunately the reduced sampling rates afforded by this scheme can be accompanied by increased error sensitivity. In a previous study, we derived bounds on the error due to mismodeling and sample additive noise. Adopting these bounds as performance measures, we consider the problems of optimizing the reconstruction sections of the system, choosing the optimal base sampling rate, and designing the nonuniform sampling pattern. We find that optimizing these parameters can improve system performance significantly. Furthermore, uniform sampling is optimal for signals with ℱ that tiles under translation. For signals with nontiling ℱ, which are not amenable to efficient uniform sampling, the results reveal increased error sensitivities with sub-Nyquist sampling. However, these can be controlled by optimal design, demonstrating the potential for practical multifold reductions in sampling rate     

基于压缩感知的定位新型UWB信号抽样方法

Ultra-wide-band (UWB) signals are suitable for localization, since their high time resolution can provide precise time of arrival (TOA) estimation. However, one major challenge in UWB signal processing is the requirement of high sampling rate which leads to complicated signal processing and expensive hardware. In this paper, we present a novel UWB signal sampling method called UWB signal sampling via temporal sparsity (USSTS). Its sampling rate is much lower than Nyquist rate. Moreover, it is implemented in one step and no extra processing unit is needed. Simulation results show that USSTS can not recover the signal precisely, but for the use in localization, the accuracy of TOA estimation is the same as that in traditional methods. Therefore, USSTS gives a novel and effective solution for the use of UWB signals in localization.     

软件无线电中欠采样信号恢复方法研究

针对软件无线电中欠采样信号在D/A恢复后频谱失真现象提出了一种欠采样信号恢复方法。首先,通过对中频A/D采样信号进行插值滤波处理;其次,对插值滤波后的信号进行混频滤波处理;最后,对混频滤波后的信号进行D/A恢复。通过硬件平台试验验证了本文方法的有效性。