Compressed sensing based multi-rate sub-Nyquist sampling system

Signal sampling is a vital component in modern information technology. As the signal bandwidth becomes wider, the sampling rate of analog-to-digital conversion(ADC) based on Shannon-Nyquist theorem is more and more high and may be beyond its capacity. However the analog to information converter(AIC) based on compressed sensing(CS) is designed to sample the analog signals at a sub-Nyquist sampling rate. A new multi-rate sub-Nyquist sampling(MSS) system was proposed in this article, it has one mixer, one integrator and several parallel ADCs with different sampling rates. Simulation shows the signals can be reconstructed in high probability even though the sampling rate is much lower than the Nyquist sampling rate.     

Novel Sampling and Reconstruction Method for Non-Bandlimited Impulse Signals

To sample non-bandlimited impulse signals, an extremely high-sampling rate analog-todigital converters （ADC） is required. Such an ADC is very difficult to be implemented with present semiconductor technology. In this paper, a novel sampling and reconstruction method for impulse signals is proposed. The required sampling rate of the proposed method is close to the signal innovation rate, which is much lower than the Nyquist rate in conventional Shannon sampling theory. Analysis and simulation results show that the proposed method can achieve very good reconstruction performance in the presence of noise.     

A novel approach for DOA estimation in unknown correlated noise fields

The key of the subspace-based Direction Of Arrival （DOA） estimation lies in the estimation of signal subspace with high quality. In the case of uncorrelated signals while the signals are temporally correlated, a novel approach for the estimation of DOA in unknown correlated noise fields is proposed in this paper. The approach is based on the biorthogonality between a matrix and its Moore-Penrose pseudo inverse, and made no assumption on the spatial covariance matrix of the noise. The approach exploits the structural information of a set of spatio-temporal correlation matrices, and it can give a robust and precise estimation of signal subspace, so a precise estimation of DOA is obtained. Its performances are confirmed by computer simulation results.     

COLORED NOISE PREDICTION BASED ON NEURAL NETWORK

A method for predicting colored noise by introducing prediction of nonlinear time series is presented. By adopting three kinds of neural networks prediction models, the colored noise prediction is studied through changing the filter bandwidth for stochastic noise and the sampling rate for colored noise. The results show that colored noise can be predicted. The prediction error decreases with the increasing of the sampling rate or the narrowing of the filter bandwidth. If the parameters are selected properly, the prediction precision can meet the requirement of engineering implementation. The results offer a new reference way for increasing the ability for detecting weak signal in signal processing system.     

2-D SPATIAL-SPECTRUM ESTIMATION FORWIDE-BAND SOURCES BASED ON INTERPOLATED CIRCULAR ARRAYS

In array signal processing, 2-D spatial-spectrum estimation is required to determine DOA of multiple signals. The circular array of sensors is found to possess several nice properties for DOA estimation of wide-band sources. C. U. Padmini, et al.(1994) had suggested that the frequency-direction ambiguity in azimuth estimation of wide-baud signals received by a uniform linear array (ULA) can be avoided by using a circular array, even without the use of any delay elements. In 2-D spatial-spectrum estimation for wide-band signals, the authors find that it is impossible to avoid the ambiguity in source frequency-elevation angle pairs using a circular array. In this paper, interpolated circular arrays are used to perform 2-D spatial-spectrum estimation for wide-band sources. In the estimation, a large aperture circular array (Υ>λmin/2) is found to possess superior resolution capability and robustness.     

Frequency estimation of sinusiode from wideband using sub-Nyquist sampling

A novel frequency estimation algorithm for wideband signal with sub-Nyquist sampling is proposed in this paper. With the aid of information provided by the auxiliary delayed sampling channel and the aliased frequency estimation for wideband signal with sub-Nyquist sampling, the frequency aliasing due to sub-Nyquist sampling can be solved. This method can reduce the complexity of the overall hardware at the cost of an auxiliary sampling channel. Furthermore, in order to alleviate the computation burden for its practicability, a more simplified algorithm is put forward and its validity is proved by our numerical simulation results. The Cramer-Rao Lower Bound （CRLB） of the frequency estimation is also derived at the end of this paper.     

Application of underdetermined blind source separation in ultra-wideband communication signals

Aiming to the estimation of source numbers, mixing matrix and separation of mixing signals under underdetermined case, the article puts forward a method of underdetermined blind source separation （UBSS） with an application in ultra-wideband （UWB） communication signals. The method is based on the sparse characteristic of UWB communication signals in the time domain. Firstly, finding the single source area by calculating the ratio of observed sampling points. Then an algorithm called hough-windowed method was introduced to estimate the number of sources and mixing matrix. Finally the separation of mixing signals using a method based on amended subspace projection. The simulation results indicate that the proposed method can separate UWB communication signals successfully, estimate the mixing matrix with higher accuracy and separate the mixing signals with higher gain compared with other conventional algorithms. At the same time, the method reflects the higher stability and the better noise immunity.     

On the PAR reduction of MB-OFDM ultra-wideband signals

The Peak to Average power Ratio （PAR） of a Multi-Band Orthogonal Frequency-Division Multiplexing （MB-OFDM） Ultra-Wide Band （UWB） signals can be substantially larger than that of single carrier or carrier-less ultra-wideband signals. In this letter, a novel PAR reduction scheme for the MB-OFDM UWB system based on spreading and interleaving is proposed. By spreading the coded bits over each subcarrier in corresponding band and interleaving the spread symbols across all bands, the PAR statistics of the MB-OFDM signals can be improved and the PAR is reduced obviously. In the PAR reduction scheme, there is no loss in transmission data rate or Bit Error Rate （BER） performance decreasing. Since the spreading and interleaving operation are implemented by unitary Hadamard sequences and used for an approach to provide the robustness of the UWB system to narrowband interference, there is no additional implementation burden. Simulation results show that the investigated scheme gives the PAR reduction of 3dB compared with that of the original MB-OFDM signals.     

Estimating Power for FPGAs Based on Signal Probability Theory

Power dissipation has become one of the key optimization conditions in logic design on field programmable gate arrays (FPGAs), thus the power estimation is necessary for logic design optimization. Nowadays, signal activity data created by logic simulation based on test vectors is essential to be used to determine the toggle rate of each signals and blocks in power estimation tools provided by field programmable gate array (FPGA) electronic design automation (EDA) tools. The accuracy of power estimation highly depends on the quality of test vectors, especially, pattern coverage. As probability distribution can describe the uncertainty signals, this work provides an algorithm which can estimate FPGAs power more effectively and accurately by using signal probability distribution rather than test vectors.     

A Feedback Correction De-noising Algorithm Based on the Wavelet Coefficients

The localization of satellite interference sources under the condition of low SNR has been a hot research topic at current, where the signal de-noising is important in the following positioning work. With the help of the wavelet analysis theory, this paper proposes a denoising algorithm based on the wavelet coefficients in low SNR. After correcting and reconstructing the signal by the correction coefficients, we could obtain the SNR and Root mean square error （RMSE）, and then we could correct the signal for the second time. The simulation results show that this de-noising algorithm can effectively remove the noise of the signal in low SNR, which would have a good effect on the estimation of time delay parameter in the localization of satellite interference sources.     

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.     

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.     

Random projection method for ultra wideband signal acquisition

High-speed high-resolution analog-to-digital(A/D) conversion demanded by ultra wideband(UWB) signal processing is a very challenging problem.This paper proposes a parallel random projection method for UWB signal acquisition.The proposed method can achieve high sampling rate,high resolution and technical feasibility of hardware implementation.In the proposed method,an analog UWB signal is projected over a set of random sign functions.Then the low-rate high-resolution analog-to-digital convertors(ADCs) are us...     

超宽带快速跳频信号侦察技术

在现有的器件水平下,采用多通道并行采集再进行数字信道化的方法对接收信号进行并行处理,可以完成极高频/超高频(EHF/SHF)频段战略战术通信中跳频带宽高达数吉赫、跳速高达每秒数万跳的超宽带快速跳频信号实时检测;同时,采用多通道高速采集、海量存储方法存储海量数据,再采用相应跳频信号分析方法可以完成信号分析和解跳解调以及信号解译。研究实践证明,该方法是目前进行超宽带快速跳频信号进行侦察的有效手段。     

基于欠奈奎斯特采样的超宽带信号总体最小二乘重建算法

该文针对超宽带无线通信中需要设计高速模数转换器的问题,提出了一种欠奈奎斯特采样方法,该方法所要求的采样率仅与信号新息率相关,低于奈奎斯特率1个数量级。基于欠采样得到的离散时间超宽带信号,从理论上推导出信号的傅里叶频谱表达式,由此给出了一种总体最小二乘参数估计算法,能够准确地估计出冲激串信号的幅度和时移;通过将估计出的冲激串信号与高斯单脉冲波形卷积,完成超宽带信号的波形重建。仿真和实验结果表明,该文算法能够准确地重建原始超宽带信号,且算法性能优于现有的零化滤波重建算法。     

IR-UWB数字相干接收机的硬件电路设计

在超宽带信号的接收中,相干接收比非相干接收拥有更高的分辨率,能充分发挥超宽带信号定位精度高等优点.相干接收机对UWB信号进行高速采样后再处理,采样的速度和精度是限制UWB相干接收机测距精度的主要因素.本文设计并实现了IR-UWB的数字相干接收机,接收机采用高速采样芯片ADC08D1000对脉冲超宽带（IR-UWB）信号进行双通道交织采样,然后使用FPGA对采样数据进行降速处理.测试结果证明,本文设计的接收机能准确捕获到脉宽为1 ns的UWB信号.     

一种超宽带线性调频信号源的设计

超宽带雷达因其距离分辨率高、抗干扰能力强、目标检测和识别能力高等优点,受到世界各国的重视。通常采用倍频器来产生超宽带信号,然而,倍频环节中存在一些影响信号质量的非理想特性。针对该问题,提出利用频谱拼接技术来产生超宽带雷达信号。采样频率越大则输出信号幅度包络失真越小。频谱拼接时间误差小于100 ns时,其主瓣比理想情况增大5%。通过频谱拼接技术能够有效解决幅度失真、高采样频率需要和拼接时间误差等问题。设计了两个直接数字频率合成器同步电路,确保两路正交基带信号同步。频谱拼接技术还能灵活控制信号带宽大小,产生较理想的超宽带线性调频信号。     

An efficient FPGA-based implementation of UWB radar system for through-wall imaging

The work presented in this paper aims to develop a low-cost ultra-wideband (UWB) radar system that has the capability to image through the wall of a human target. The proposed radar system relies heavily on a field-programmable gate array (FPGA) platform. The Xilinx FPGA Kintex-7 (KC705) board is employed to integrate the main functionalities of the radar system, such as the generation and acquisition of a UWB signal. The generated signal is a monocycle signal, which has an ultra-wide bandwidth. Due to the UWB nature of this signal, the radar system achieves better penetration ability, as well as high imaging resolution. However, the major challenge of a UWB radar system lies in the stage of digitization, as it requires a high sampling rate analog-to-digital converter (ADC). In fact, such a chip is very expensive. Hence, to reach a high sampling resolution using a low-cost and low-speed ADC, an efficient sampling strategy is implemented. In contrast to other UWB sampling methods, which require a hardware delay line chip, the new sampling scheme depends only on the FPGA firmware to realize a combination of real-time and equivalent-time sampling, which provides better jitter performance. Finally, to demonstrate the imaging capability, experimental tests are conducted in an indoor environment while human targets are located in different places. The measurement results revealed that the proposed radar system has the ability to provide 2D images that accurately determine the location of the target.     

基于PCA与EMD的超宽带雷达生命信号检测算法

 本文分析了脉冲超宽带(UWB)生命信号模型,提出了基于主元分析(PCA)和经验模态分解(EMD)的非接触生命信号检测方法.根据UWB信号杂波与生命目标回波特点,结合PCA去除杂波.提取适当的主元特征向量序列曲线上峰值所对应的时延,估计目标距离信息.采用EMD分解目标回波序列为有限个固有模态函数(IMF)分量,在时域上重构平滑生命特征曲线,且其在高信噪比下可实现心跳与呼吸信号的分离.实验研究表明该方法简单有效,能同时提供生命信号的频域和时域波形位置信息,且重构得到的生命信号较符合实际信号时变、非平稳特性.     

超宽带载波包络调制及其频谱相关解调

针对超宽带载波包络调制的频谱特性,提出新颖的正交梳状频谱相关解调方法,将超宽带脉冲信号的处理从时域扩展到了频域。阐述了该解调方式的基本原理和设计思路,推导了正交梳状频谱的载波选择原则。对系统性能的分析和仿真证明了频域相关解调的方法有着和时域解调相同的误码率性能。该方法接收机结构简单、成本低,可由全数字电路实现,便于采用其它数字信号处理技术进一步提高系统性能。