SUBSPACE-BASED NOISE VARIANCE AND SNR ESTIMATION FOR MIMO OFDM SYSTEMS

This paper proposes a subspace-based noise variance and Signal-to-Noise Ratio (SNR) estimation algorithm for Multi-Input Multi-Output (MIMO) wireless Orthogonal Frequency Division Multiplexing (OFDM) systems. The special training sequences with the property of orthogonality and phase shift orthogonality are used in pilot tones to obtain the estimated channel correlation matrix. Partitioning the observation space into a delay subspace and a noise subspace, we achieve the measurement of noise variance and SNR. Simulation results show that the proposed estimator can obtain accurate and real-time measurements of the noise variance and SNR for various multipath fading channels, demonstrating its strong robustness against different channels.

Signal-to-noise ratio comparison of angular signal radiography and phase stepping method

Grating-based x-ray phase contrast imaging has the potential to be applied in future medical applications as it is compatible with both laboratory and synchrotron source. However, information retrieval methods are important because acquisition speed, scanning mode, image quality, and radiation dose depend on them. Phase-stepping （PS） is a widely used method to retrieve information, while angular signal radiography （ASR） is a newly established method. In this manuscript, signal-to-noise ratios （SNRs） of ASR are compared with that of PS. Numerical experiments are performed to validate theoretical results. SNRs comparison shows that for refraction and scattering images ASR has higher SNR than PS method, while for absorption image both methods have same SNR. Therefore, our conclusions would have guideline in future preclinical and clinical applications.

Nonsaturation Throughput Enhancement of IEEE 802.11b Distributed Coordination Function for Heterogeneous Traffic under Noisy Environment

In this paper, we propose a mechanism named modified backoff （MB） mechanism to decrease the channel idle time in IEEE 802.11 distributed coordination function （DCF）. In the noisy channel, when signal-to-noise ratio （SNR） is low, applying this mechanism in DCF greatly improves the throughput and lowers the channel idle time. This paper presents an analytical model for the performance study of IEEE 802.11 MB-DCF for nonsaturated heterogeneous traffic in the presence of transmission errors. First, we introduce the MB-DCF and compare its performance to IEEE 802.11 DCF with binary exponential backoff （BEB）. The IEEE 802.11 DCF with BEB mechanism suffers from more channel idle time under low SNR. The MB-DCF ensures high throughput and low packet delay by reducing the channel idle time under the low traffic in the network. However, to the best of the authors＇ knowledge, there are no previous works that enhance the performance of the DCF under imperfect wireless channel. We show through analysis that the proposed mechanism greatly outperforms the original IEEE 802.11 DCF in the imperfect channel condition. The effectiveness of physical and link layer parameters on throughput performance is explored. We also present a throughput investigation of the heterogeneous traffic for different radio conditions.

THE METHOD OF SEPARATING HARMONIC SIGNALS FROM MULTIPLICATIVE AND ADDITIVE NOISES

This paper focuses on the extraction of a harmonic signal from multiplicative and additive noises. A method is proposed in two stages： （1） to square the original discrete time series, which includes both signals and noises, and form a new time series. By this means, the multiplicative noise is converted to additive noise; and （2） to filter out the noise by using existing noise removal schemes. With a large amount of simulation, experimental results demonstrated the efficiency and effectiveness of this newly developed method in terms of Signal-to-Noise Ratio （SNR） and other criteria. Prom the experiment, it is also found that： the two kinds of noises affect the SNR differently. In general, the SNR is not influenced by multiplicative Gaussian noise regardless of its variance. However, if both kinds of noise exist, the SNR decreases with the incensement of the Variance of Additive Noise to Multiplicative Noise Ratio （VAMNR）. This analysis is also supported by simulation work.

New Wavelet Thresholding Algorithm in Dropping Ambient Noise from Underwater Acoustic Signals

Underwater Wireless Communication, largely dependent on the acoustic communication between the machines, is largely affected by various types of noise in the shallow and deep water. However ambient noise which is due to multiple sources (e.g. shipping, wind) and no one source dominates. Ambient noise masks the acoustic signal to a large extent. Hence today it has drawn the attention of the experts to reduce its effect on the received signal. This paper discusses ambient noise problem and devises a new wavelet thresholding method to reduce its effect. Afterwards a comparative study on statistical parameters is shown to prove the efficiency of the devised method.

Performance Analysis Concerning Antinoise for Several Pseudo-random Code Fuzes

Introduces in brief the principle of three kinds pseudo-random code fuzes, viz. the pseudo-random code phase modulation fuze,the pseudo-random code phase modulation and pulse amplitude modulation(PAM) combined fuze,and the pseudo-random code phase modulation and random(pseudo-random) pulse position modulation(PPM) combined fuze. On this basis, their SNR gains in signal processing after band pass filter to the correlation detection, and the overall SNR gains of the whole procedure after correlation detection are deduced in detail. The results show that the latter two kinds of fuzes have the same performances concerning antinoise that are stronger than that of the pseudo-random code phase modulation fuze.

Hardware implementation of adaptive filter for noise cancellation using TMS320C6713

Daily, we experience the effects of audio noise, which contaminates the original information bearing signal with noise from its surrounding environment. This paper focuses on real-time hardware implementation of multi-tap adaptive noise cancellation （ANC） system by using the least mean square （LMS） algorithm on TMS320C6713 to remove undesired noise from a received signal for various audio related applications. Three different experiments are carried out by considering different audio inputs to test the efficiency of the designed ANC system. The ＇C＇ code implementation of LMS algorithm is introduced and simulated in code composer studio （CCS）, then realized on the digital signal processor （DSP） C6713. The 300 Hz, 500 Hz, 800 Hz, 1 kHz and 3 kHz of tone signals and male speech signal are used as the reference inputs to trace the noise of signal until it is eliminated. The performance of ANC system is studied in terms of convergence speed, order of the filter and signal-to-noise ratio （SNR）. The experimentam results demonstrate that the designed system shows a consider- able improvement in SNR.

基于自适应估计SNR的分组Turbo码译码算法

针对分组Turbo码自适应Chase译码算法中SNR是预先设定值的情况,提出一种新的自适应估计信噪比(SNR)的译码算法。该方案利用了接收码字的统计信息与SNR之间存在的对应关系来调整门限函数,达到控制译码复杂度的目的。仿真结果表明,所提出的译码算法能自适应反映信道情况,降低译码运算复杂度,提高译码处理速度,获得了较好的性能。

基于SNR比较的协作感知用户选择问题的研究

在实际情况的认知无线电环境下,由于认知用户的感知可靠性存在差异,并不是参与协作的认知用户越多,感知性能越好.综合考虑协作的必要性与协作的增益两方面因素,提出了一种基于信噪比(SNR)比较的协作感知用户选择方案.理论分析及仿真结果一致表明:该方法在有效地提高频谱感知检测性能的同时,减小了参与协作感知的用户数目,提高了协作效率.

LTE-Advanced MIMO低SNR下调制技术性能研究

采用多输入多输出(multiple input multiple output,MIMO)技术来提高无线信道的传输增益,从而提高调制系统中的信噪比、降低调制系统的比特误码率;仿真量化分析了在LTE-Advanced MIMO不同无线场景中,调制系统中信噪比与MIMO技术的天线数,以及调制系统中信噪比与MIMO技术天线的发射功率之间的关系。仿真结果表明,MIMO技术能提高低功率无线场景中调制系统中的信噪比,进而提高调制系统的可靠性。

SNR估计误差对DDEA-Turbo系统的影响

基于数据引导均衡算法(DDEA)的Turbo均衡,求取符号外信息对数似然比(LLR)时需要用到信噪比(或者说噪声方差)信息,在均衡的过程中没法获得准确的信噪比,只能通过估计获得其粗略值。而信噪比的估计误差势必会对均衡的性能产生影响。给出了一种半分析的方法,研究信噪比(SNR)的估计误差对基于DDEA算法的Turbo均衡器性能的影响,相比较于仅靠大量的仿真工作来获取此方面信息的方法来说,该方法省略了很多的仿真工作,而是通过推导的公式计算,所以复杂度降低。通过仿真验证了给出方法的有效性,同时可以看出信噪比过估计对基于DDEA算法的Turbo均衡系统的性能基本不产生什么影响,但是信噪比欠估计会造成误码率的大幅度上升。

低SNR下汉语单音节清晰度测量与可懂度估计

在实验室条件下测量了不同频带范围、不同信噪比条件时的语言清晰度和STI-PA,分析了清晰度,STI-PA与信噪比的关系,并根据单音节清晰度与语言可懂度的关系,估计了在满足一定可懂度条件下的最低信噪比及相应的STI-PA。

软件无线电中ADC孔径抖动对SNR影响的建模与仿真

利用Simulink建立了两种仿真模型验证分析软件无线电中ADC孔径抖动对SNR的影响,模型一采用输入信号的抖动来仿真ADC采样孔径抖动产生的影响,模型二采用时钟源加入高斯噪声的方法较真实地模拟了ADC采样时钟的抖动情况。两种模型的仿真曲线与理论曲线基本吻合,证明了模型的正确性,尤其是模型二建模解决了有抖动的采样脉冲产生和参数计算中信号同步的关键问题,得到了更真实、准确的特性曲线,为深入研究高速高精度ADC的孔径抖动测量、分析和控制提供了一个比较可靠的依据,对系统设计提供了有益帮助。

A fast decoupled ISAR high-resolution imaging method using structural sparse information under low SNR

Inverse synthetic aperture radar (ISAR) image can be represented and reconstructed by sparse recovery (SR) approaches. However, the existing SR algorithms, which are used for ISAR imaging, have suffered from high computational cost and poor imaging quality under a low signal to noise ratio (SNR) condition. This paper proposes a fast decoupled ISAR imaging method by exploiting the inherent structural sparse information of the targets. Firstly, the ISAR imaging problem is decoupled into two sub-problems. One is range direction imaging and the other is azimuth direction focusing. Secondly, an efficient two-stage SR method is proposed to obtain higher resolution range profiles by using jointly sparse information. Finally, the residual linear Bregman iteration via fast Fourier transforms (RLBI-FFT) is proposed to perform the azimuth focusing on low SNR efficiently. Theoretical analysis and simulation results show that the proposed method has better performence to efficiently implement higher-resolution ISAR imaging under the low SNR condition.

Micro-Doppler feature extraction of micro-rotor UAV under the background of low SNR

Micro-Doppler feature extraction of unmanned aerial vehicles(UAVs)is important for their identification and classification.Noise and the motion state of the UAV are the main factors that may affect feature extraction and estimation precision of the micro-motion parameters.The spectrum of UAV echoes is reconstructed to strengthen the micro-motion feature and reduce the influence of the noise on the condition of low signal to noise ratio(SNR).Then considering the rotor rate variance of UAV in the complex motion state,the cepstrum method is improved to extract the rotation rate of the UAV,and the blade length can be intensively estimated.The experiment results for the simulation data and measured data show that the reconstruction of the spectrum for the UAV echoes is helpful and the relative mean square root error of the rotating speed and blade length estimated by the proposed method can be improved.However,the computation complexity is higher and the heavier computation burden is required.

A Blackman-Harris windowed triple-spectrum-line interpolation method for measuring SNR of ADCs

In the practical measurement of signal to noise ratio(SNR)of analog-to-digital converters(ADCs)by using fast Fourier transformation(FFT)method,the non-coherent sampling is inevitable,leading to spectral leakage which in turn affects the calculation accuracy and final measurement results.In this paper,a new method based on the Blackman-Harris windowed triple-spectrum-line interpolation is presented for the measurement of ADCs SNR by FFT.The simulation platform is built based on MATLAB and the behavioral dynamic models of the high-speed ADC products of Analog Devices Inc.(ADI)are simulated.The simulation results show that,even in the case of the maximum non-coherent degree,the SNR error is less than0.23dB and reaches the testing standards provided by ADI,confirming that the proposed method is effective for suppressing the spectral leakage effects and improving the SNR test accuracy.

Denoising Letter Images from Scanned Invoices Using Stacked Autoencoders

Invoice document digitization is crucial for efficient management in industries.The scanned invoice image is often noisy due to various reasons.This affects the OCR(optical character recognition)detection accuracy.In this paper,letter data obtained from images of invoices are denoised using a modified autoencoder based deep learning method.A stacked denoising autoencoder(SDAE)is implemented with two hidden layers each in encoder network and decoder network.In order to capture the most salient features of training samples,a undercomplete autoencoder is designed with non-linear encoder and decoder function.This autoencoder is regularized for denoising application using a combined loss function which considers both mean square error and binary cross entropy.A dataset consisting of 59,119 letter images,which contains both English alphabets(upper and lower case)and numbers(0 to 9)is prepared from many scanned invoices images and windows true type(.ttf)files,are used for training the neural network.Performance is analyzed in terms of Signal to Noise Ratio(SNR),Peak Signal to Noise Ratio(PSNR),Structural Similarity Index(SSIM)and Universal Image Quality Index(UQI)and compared with other filtering techniques like Nonlocal Means filter,Anisotropic diffusion filter,Gaussian filters and Mean filters.Denoising performance of proposed SDAE is compared with existing SDAE with single loss function in terms of SNR and PSNR values.Results show the superior performance of proposed SDAE method.

Long term integration of radar signals with unknown Doppler shift for ubiquitous radar

Ubiquitous radar is a new radar system that provides continuous and uninterrupted multifunction capability within a coverage volume. Continuous coverage from close-in ＂pop-up＂ targets in clutter to long-range targets impacts selection of waveform parameters. The coherent processing interval （CPI） must be long enough to achieve a certain signal-to-noise ratio （SNR） that ensures the efficiency of detection. The condition of detection in the case of low SNR is analyzed, and three different cases that would occur during integration are discussed and a method to determine the CPI is presented. The simulation results show that targets detection with SNR as low as -26 dB in the experimental system can possibly determine the CPI.

A NOVEL METHOD FOR ESTIMATING THE SNR OF UNKNOWN EMITTER SIGNAL

It is important to estimate the Signal-to-Noise Ratio(SNR) of unknown emitter signal accurately.In order to resolve the disadvantages of present algorithm,a novel method is proposed in this letter.We extract and normalize the information of zero frequency of received signal by the Wigner-Vile Distribution(WVD) transformation and then get the approximate power of original signal by mathematic transformation,at last,we get the estimate value of SNR by the known account formula of SNR.Simulation results show that it is correct and feasible.

SVM辅助的GPS SNR雪深时间序列反演

研究了GPS干涉反射技术GPS-IR(GPS-Interferometric Reflectometry);在利用GPS卫星SNR信号进行积雪深度探测的基础上构建了支持向量机SVM(Support Vector Machine)辅助的GPS SNR雪深时间序列反演模型;对积雪深度进行时间序列预报和与传统GPS-IR积雪探测模型进行精度对比分析。实验结果显示,相比传统GPS-IR雪深反演模型,SVM辅助的GPS SNR雪深时间序列反演模型的雪深预报结果的精度更高,也更符合实测雪深的变化趋势,可为地面积雪雪深反演提供新方法。