Signal-to-noise Ratio Estimation Method Research Articles

A Long-Term Recurrent Convolutional Network for SNR Estimation of Cone-Shaped Target

Signal-to-Noise Ratio (SNR) is an important prior information for many micro-motion echo processing approaches and techniques. Estimating SNR in advance can effectively enhance the performance of such technologies. This letter proposes a Long Term Recurrent Convolutional Network (LRCN) based SNR estimation method for cone-shaped target. First, mathematical expression of the echo is derived by studying the cone-shaped target micro-motion model, and hence the characteristics of the echo and its Short-Time Fourier Transform (STFT) are analyzed. Second, the motivation for the proposed are analyzed, and the echo is converted as an time-frequency graph (TFG) with STFT, so that an estimation technique based on LRCN (a hybrid of convolutional neural network (CNN) and recurrent neural networks (RNN)) is designed to estimate the SNR. Experiments indicate that the proposed method outperforms other already existing methods with better accuracy and robustness.

A New Method for Estimating Signal-to-Noise Ratio in UAV Hyperspectral Images Based on Pure Pixel Extraction

Signal-to-noise ratio (SNR) is an important radiation characteristic parameter for remote sensing image quality assessment as well as a key performance indicator for remote sensing sensors. At present, the SNR estimation methods based on regular segmentation or continuous segmentation are generally used to obtain image SNR. However, the land cover type has a great influence on the results of the SNR estimation method using regular segmentation, especially the high spectral resolution and high spatial resolution remote sensing images obtained by the low-altitude UAV hyperspectral sensor. In addition, some land cover types are difficult to achieve continuous segmentation. In view of this limitation of the existing SNR estimation methods, a new unsupervised method for estimating SNR in UAV hyperspectral images has been developed in this article, called pure pixel extraction and spectral decorrelation. By directly extracting pure pixels in the spatial dimension and combining the correlation of the spectral dimension to obtain the SNR of the hyperspectral image, this new method replaces the conventional method of improving the segmentation algorithm to improve the accuracy of SNR estimation. Additionally, the box counting method is introduced to determine the image SNR aggregation interval. The results showed that the proposed method had higher accuracy and smaller errors than the other SNR estimation methods. Besides, this method had stronger robustness, it can be used for both radiance and reflectance (atmospherically corrected) UAV hyperspectral images.

A non-data-aided SNR estimator based on maximum likelihood method for communication between orbiters

Signal-to-noise ratio (SNR) is an important metric for performance assessment in numerous scenerios. In order to ensure the reliability and effectiveness of the system performance, plenty of situations require the information of SNR estimate. At the same time, Mars exploration has been a hot topic in recent years, which leads to the research attention of scholars extending to deep space. In this paper, a new SNR estimator related to deep space scene is proposed. On the one hand, the time of essential data transmission is limited in Mars exploration system. On the other hand, the relative position and condition between orbiters vary quickly all the time, which makes it difficult to obtain the accurate and significant information for Mars exploration. Therefore, it is obvious that the information of SNR can promote the system to adjust the signal transmission rate automatically. Subsequently, the estimation of SNR becomes a fundamental research in automatic digital communications. In this paper, an SNR estimation method based on non-data-aided (NDA) with maximum likelihood (ML) estimation is proposed to enhance the accuracy and reliability of Mars exploration process. Additionally, the Cramer-Rao lower bound (CRLB) related to the designed ML algorithm is derived. Finally, the Monte Carlo simulation results demonstrate that the proposed ML estimator algorithm obtains a superior performance when compared to the existing SNR estimators.

Non Data-Aided SNR Estimation for UAV OFDM Systems

Signal-to-noise ratio (SNR) estimation is essential in the unmanned aerial vehicle (UAV) orthogonal frequency division multiplexing (OFDM) system for getting accurate channel estimation. In this paper, we propose a novel non-data-aided (NDA) SNR estimation method for UAV OFDM system to overcome the carrier interference caused by the frequency offset. First, an absolute value series is achieved which is based on the sampled received sequence, where each sampling point is validated by the data length apart. Second, by dividing absolute value series into the different series according to the total length of symbol, we obtain an output series by stacking each part. Third, the root mean squares of noise power and total power are estimated by utilizing the maximum and minimum platform in the characteristic curve of the output series after the wavelet denoising. Simulation results show that the proposed method performs better than other methods, especially in the low synchronization precision, and it has low computation complexity.

ALTIS: A new algorithm for adaptive long-term SNR estimation in multi-talker babble

We introduce a real-time capable algorithm which estimates the long-term signal to noise ratio (SNR) of the speech in multi-talker babble noise. In real-time applications, long-term SNR is calculated over a sufficiently long moving frame of the noisy speech ending at the current time. The algorithm performs the real-time long-term SNR estimation by averaging “speech-likeness” values of multiple consecutive short-frames of the noisy speech which collectively form a long-frame with an adaptive length. The algorithm is calibrated to be insensitive to short-term fluctuations and transient changes in speech or noise level. However, it quickly responds to non-transient changes in long-term SNR by adjusting the duration of the long-frame on which the long-term SNR is measured. This ability is obtained by employing an event detector and adaptive frame duration. The event detector identifies non-transient changes of the long-term SNR and optimizes the duration of the long-frame accordingly. The algorithm was trained and tested for randomly generated speech samples corrupted with multi-talker babble. In addition to its ability to provide an adaptive long-term SNR estimation in a dynamic noisy situation, the evaluation results show that the algorithm outperforms the existing overall SNR estimation methods in multi-talker babble over a wide range of number of talkers and SNRs. The relatively low computational cost and the ability to update the estimated long-term SNR several times per second make this algorithm capable of operating in real-time speech processing applications.

Long-Term SNR Estimation of Speech Signals in Known and Unknown Channel Conditions

Many speech processing algorithms and applications rely on the explicit knowledge of signal-to-noise ratio (SNR) in their design and implementation. Estimating the SNR of a signal can enhance the performance of such technologies. We propose a novel method for estimating the long-term SNR of speech signals based on features, from which we can approximately detect regions of speech presence in a noisy signal. By measuring the energy in these regions, we create sets of energy ratios, from which we train regression models for different types of noise. If the type of noise that corrupts a signal is known, we use the corresponding regression model to estimate the SNR. When the noise is unknown, we use a deep neural network to find the “closest” regression model to estimate the SNR. Evaluations were done based on the TIMIT speech corpus, using noises from the NOISEX-92 noise database. Furthermore, we performed cross-corpora experiments by training on TIMIT and NOISEX-92 and testing on the Wall Street Journal speech corpus and DEMAND noise database. Our results show that our system provides accurate SNR estimations across different noise types, corpora, and that it outperforms other SNR estimation methods.

Energy conservation‐based thresholding for effective wavelet denoising of partial discharge signals

Recent studies have shown that wavelet transform can effectively be used for noise reduction in the context of partial discharge (PD) signal detection and classification. Several thresholding approaches for wavelet denoising have been reported in the literature. In this study, a novel wavelet threshold estimation method, named energy conservation-based thresholding (ECBT), is introduced. The proposed thresholding function is capable of conserving a significant portion of the original signal energy, while the threshold value is determined based on the relative difference between the original and noisy signal energies. The proposed method is first applied to PD signals contaminated with different levels of simulated noise. Results show that ECBT produces a denoised PD signal with higher signal-to-noise ratio (SNR) and less distortion than PDs produced by the existing wavelet methods. Then, ECBT is modified to address actual PD signals corrupted with real noise, where a robust SNR estimation method is derived to estimate the noise level embedded in the measured PD signals. The denoised PD signals indicate that the proposed method yields higher reduction in noise levels than other methods.

Signal‐to‐noise ratio estimation for DVB‐S2 based on eigenvalue decomposition

Signal-to-noise ratio (SNR) estimation is required in many receivers and systems of communications. In this study, the authors propose a novel SNR estimation method for DVB-S2 over additive white Gaussian noise channel. This is a non-data-aided, eigenvalue decomposition-based method. First, they use the even and odd component vectors of received signal to construct a novel 2 × 2 matrix, and propose an estimation variable using the standard condition number of this matrix. And then, they derive the relationship between this variable and SNR, and give the detailed expressions for SNR estimation on different modulations used in DVB-S2, including quadrature phase shift keying, 8PSK, 16 amplitude PSK (APSK) and 32APSK. Monte Carlo simulation results show the good estimation performance for different modulations. The proposed method is asymptotic unbiased and can be used for both constant and non-constant modulus constellations such as M-ary PSK, MAPSK and M-ary quadrature amplitude modulation.

Investigation and Analysis of SNR Estimation in OFDM system

Estimation of signal to noise ratio (SNR) of received signal and to transmit the signal effectively for the modern communication system. The performance of existing non-data-aided (NDA) SNR estimation methods are substantially degraded for high level modulation scheme such as M-ary amplitude and phase shift keying (APSK) or quadrature amplitude modulation (QAM).In this paper SNR estimation proposed method which uses zero point auto-correlation of received signal per block and auto/cross- correlation of decision feedback signal in orthogonal frequency division multiplexing (OFDM) system. Proposed method can be studied into two types; Type 1 can estimate SNR by zero point auto-correlation of decision feedback signal based on the second moment property. Type 2 uses both zero point auto-correlation and cross-correlation based on the fourth moment property. In block-by-block reception of OFDM system, these two SNR estimation methods can be possible for the practical implementation due to correlation based the estimation method and they show more stable estimation performance than the earlier SNR estimation methods.

ISDB-T 시스템을 위한 SNR 추정기 구현

본 논문에서는 ISDB-T (Integrated Services Digital Broadcasting-Terrestrial) 시스템을 기반으로 한 동기 방식의 OFDM 시스템에서 방송 시스템에서 중요한 지표가 되는 SNR (Signal to Noise Ratio) 추정기를 구현하고자 한다. 다양한 SNR 추정 방법 중 복잡도가 적어 ASIC 설계에 적합한 MSE (Mean Square Error) 알고리즘을 사용하여 ISDB-T 시스템의 OFDM 세그먼트를 구성하고 있는 요소 중 방송 정보 데이터를 사용하여 SNR을 추정하는 방법과 분산 파일럿 신호를 사용하여 SNR을 추정하는 방법을 각각 RTL(Register Transfer Level)로 구현하였다. 두 방법을 이상적인 채널인 AWGN (Additive White Gaussian Noise) 채널뿐만 아니라 SFN(Single Frequency Network) 채널 및 주파수 선택적 페이딩 채널과 같이 왜곡된 채널에서 모의실험을 통해 성능을 비교하고 RTL 구현을 통해 복잡도를 비교하여 분산 파일럿 신호를 사용하여 SNR을 추정하는 방법의 성능과 구현의 용이함을 보였다. This paper aims to realize a Signal to Noise Ratio Estimator which constitutes a critical index of the broadcasting system in OFDM system with a synchronized type based on ISDB-T system. Of the elements which are comprising OFDM segments of ISDB-T system using the MSE algorithm suitable for ASIC design owing to its low complexity among a diverse SNR estimation methods, SNR estimation method using the broadcasting information data and the SNR estimation method using scattered pilot signal were realized by RTL. These two methods were compared in terms of their performance through simulation test not only in the AWGN channel which is an ideal channel, but also in SFN channel and frequency selective fading channel, which are distorted channels. Complexity of two methods were also compared through RTL realization. As a result of this comparison analysis, it was concluded that the SNR estimation method using scattered pilot signal shows more excellent performance and easiness in realization.

Application of a joint and iterative MMSE-based estimation of SNR and frequency-selective channel for OFDM systems

This article presents an iterative minimum mean square error- (MMSE-) based method for the joint estimation of signal-to-noise ratio (SNR) and frequency-selective channel in an orthogonal frequency division multiplexing (OFDM) context. We estimate the SNR thanks to the MMSE criterion and the channel frequency response by means of the linear MMSE (LMMSE). As each estimation requires the other one to be performed, the proposed algorithm is iterative. In this article, a realistic case is considered; i.e., the channel covariance matrix used in LMMSE is supposed to be totally unknown at the receiver and must be estimated. We will theoretically prove that the algorithm converges for a relevantly chosen initialization value. Furthermore simulations show that the algorithm quickly converges to a solution that is close to the one in which the covariance matrix is perfectly known. Compared to existing SNR estimation methods, the algorithm improves the trade-off between the number of required pilots and the SNR estimation quality.

SNR Estimation in OFDM System by the Correlation of Decision Feedback Signal

In the channel-varying environment, it is very important to estimate the signal to noise ratio (SNR) of received signal and to transmit the signal effectively for the modern communication system. The performance of existing non-data-aided SNR estimation methods are substantially degraded for high level modulation scheme such as M-ary amplitude and phase shift keying or quadrature amplitude modulation. In this paper, we propose a SNR estimation method which uses zero point auto-correlation of received signal per block and auto/cross-correlation of decision feedback signal in orthogonal frequency division multiplexing (OFDM) system. Proposed method can be studied into two types; Type 1 can estimate SNR by zero point auto-correlation of decision feedback signal based on the second moment property. Type 2 uses both zero point auto-correlation and cross-correlation based on the fourth moment property. In block-by-block reception of OFDM system, these two SNR estimation methods can be possible for the practical implementation due to correlation based the estimation method and they show more stable estimation performance than the previous SNR estimation methods. Also, we mathematically derive the SNR estimation expression according to computational difference of auto/cross-correlation. Finally, Monte Carlo simulations are used to verify the proposed method.

A robust non‐data‐aided SNR estimation method for OFDM systems

AbstractThe information of signal‐to‐noise ratio (SNR) plays an important role in orthogonal frequency division multiplexing (OFDM) systems. Some applications, such as cognitive radio, need non‐data‐aided (NDA) estimation of SNR. In this paper, we propose a robust NDA SNR estimation method, which is developed on the basis of the cyclic prefix of OFDM symbol. The proposed method is immune to the inter‐symbol interference caused by multipath channel and can thus obtain an unbiased estimate of the signal power even in the low‐SNR regime. Copyright © 2013 John Wiley & Sons, Ltd.

Investigation of SNR Estimation Algorithms of FM Signal for the Underwater Acoustic Channel

SNR (Signal to Noise Ratio) estimation is a very important parameter for channel estimation of the communication system. In this paper, some SNR estimation methods of digital modulation system are applied and improved in FM modulation system. Then we summarize several SNR estimation methods (EHM (Envelope High order Moment method), SE (Spectrum Estimation method), FD (Frequency Domain method), EMV (Envelope Mean Variance method), M2M4 (Second order and Fourth order Moment method)) . Furthermore, according to the characteristics of FM modulation system, we put forward the improvement of M2M4 and also discuss the optimal algorithm for underwater acoustic Rayleigh channel, Rician channel and Bellhop channel by comparing these methods.

Statistical model based SNR estimation method for speech signals

The performance of speech enhancement algorithms to a large extent is related to the employed signal-to-noise ratio (SNR) estimation techniques. Many of the existing SNR estimation techniques are based on approaches that require either an experimentally pre-specified weighting factor or prior assumptions of the parameters in the signal model. In this reported work, a closed form SNR estimator is derived by modelling the noisy speech signal as a generalised normal-Laplace distribution and estimating the variance of the signal and variance of the noise using high-order sample moments. The performance of the proposed technique is tested using real speech signals and compared with the well-known eigenvalue method.

SNR Estimation Algorithm Based on Pilot Symbols for DFT-Spread OFDM Systems over Underwater Acoustic Channels

Abstract Effective and low complexity methods for signal to noise ratio (SNR) estimation remain a challenge to underwater acoustic communication due to the ocean environment characteristic which can be summarized as simultaneously severe frequency selectivity and fast-time variability. In this study, a novel SNR estimation method was proposed by using only one training symbols in the pilots and applied to DFT-Spread OFDM transmission system over underwater acoustic channels. The method will be effective against frequency selectivity and fast time varying of underwater acoustic channels. Simulations showed technically feasibility of our proposal. Further, a systematic investigation in the shallow water near Xiamen demonstrated that single training symbol based SNR estimator would be applicable for DFT-Spread OFDM system over underwater acoustic channels.

OFDM 시스템에서 Decision Feedback 신호의 상관 관계를 이용하는 SNR 추정

채널의 상태가 변하는 전송 환경에서 수신된 신호에 대한 잡음비를 추정하여, 보다 효율적으로 신호를 전송하는 것은 현대 통신 시스템에서 중요한 기술이다. 기존 NDA(Non-Data-Aided) SNR 추정 방법은 M진 APSK 또는 같은 고차원 신호의 SNR 추정 성능이 떨어진다. 본 논문에서는 OFDM 시스템에서 블록 단위 수신 신호의 영점 자기 상관과 decision feedback 신호의 자기 상관 및 상호 상관을 이용하는 SNR 추정 방법을 제안한다. 본 논문에서 제안한 방법은 decision feedback 신호의 2차 모멘트인 영점 자기 상관을 이용하여 SNR을 추정하는 Type 1 방식과 4차 모멘트 성질을 갖고 있는 영점 자기 상관과 상호 상관을 이용한 Type 2 방식이다. 이 두가지 SNR 추정 방식은 OFDM 시스템에서 블록 단위 수신을 할 때, 신호의 상관 관계에 기반을 두고 있어 SNR 추정 방법의 실용적인 구현이 가능하게 하고, decision feedback 신호를 사용함으로써 QAM 신호에서도 종전의 SNR 추정 방식들보다 비교적 안정적인 추정 성능을 보인다. 또한, decision feedback 신호를 사용할 때 자기 상관과 상호 상관의 오차에 따른 SNR 추정 식을 수식적으로 유도한다. 그리고 Monte Carlo 시뮬레이션을 통해 제안한 SNR 추정 방법의 성능을 확인한다. In the channel-varying environment, it is very important to estimate the signal to noise ratio(SNR) of received signal and to transmit the signal effectively for the modern communication system. The performance of existing non-data-aided (NDA) SNR estimation methods are substantially degraded for high level modulation scheme such as M-ary APSK or QAM. In this paper, we propose a SNR estimation method which uses zero point auto-correlation of received signal per block and auto-/cross- correlation of decision feedback signal in OFDM system. Proposed method can be studied into two Types; Type 1 can estimate SNR by zero point auto-correlation of decision feedback signal based on the second moment property. Type 2 uses both zero point auto-correlation and cross-correlation based on the fourth moment property. In block-by-block reception of OFDM system, these two SNR estimation methods can be possible for the practical implementation due to correlation based the estimation method and they show more stable estimation performance than the previous SNR estimation methods. Also, we mathematically derive the SNR estimation expression according to computational difference of auto-/cross-correlation. Finally, Monte Carlo simulations are used to verify the proposed method.

Moment-based SNR estimation over linearly-modulated wireless SIMO channels

In this paper, we develop a new method for signal-to-noise ratio (SNR) estimation when multiple antenna elements receive linearly-modulated signals in complex additive white Gaussian noise (AWGN) spatially uncorrelated between the antenna elements. We also derive extensions of other existing moment-based SNR estimators to the single-input multiple-output (SIMO) configuration. The new SIMO SNR estimation technique is non-data-aided (NDA) since it is a moment-based method and does not rely, therefore, on the a priori knowledge or detection of the transmitted symbols; it does not require the a priori knowledge of the modulation type or order. The new method is shown by Monte Carlo simulations to clearly outperform the best NDA moment-based SNR estimation methods in terms of normalized root mean square error (NRMSE) over QAM-modulated transmissions, namely the M <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> M <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> method and the estimators referred to, in this paper, as the GT and the M <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sub> methods, even when we extend them to the SIMO configuration.

Estimation of Postaverage SNR from Evoked Responses Under Nonstationary Noise

In any measure of event-related potentials, it is important to be able to estimate the postaverage signal-to-noise ratio (SNR) in order to assess the quality of the measured signals. The estimated postaverage SNR can be an important detection criteria (as in infant hearing-screening of evoked auditory potentials) and a control factor when comparing signals obtained during different conditions (accounting for residual noise variability). Standard SNR estimation methods, such as the fixed-single-point (Fsp) statistic (C. Elberling and M. Don, "Quality estimation of averaged auditory brainstem responses," Scandinavian Audiol., vol. 13, pp. 187-197, 1984), assume a single-stationary noise source, with the postaverage SNR increasing proportionally to the number of trials averaged. This study proposes a modified version of the Fsp statistic, the nonstationary fixed-multiple-point (NS Fmp), that can account for a discrete number of noise sources of different power, and can also be modified for weighted averaging (WNS Fmp). A new noise segmentation procedure is also proposed that dynamically partitions contiguous trials based on their noise power estimates and a series of F-tests. Results from computer simulation and real data from auditory brain stem recordings show that the NS Fmp method yields lower mean square error than do the Fsp, and that the WNS Fmp has higher receiver-operating-curve area than do the standard Fsp procedure.

Measurement of signal‐to‐noise ratios in sum‐of‐squares MR images

Dietrich et al (1) present an interesting comparison of different approaches in the estimation of signal-to-noise ratio (SNR) in MRI images and investigate the influence of the use of multichannel coils, parallel imaging, and reconstruction filters. One conclusion of the article is that the widely used SNR estimation method using a background region of interest (ROI) for the estimation of the signal standard deviation provides inaccurate results when multichannel coils are employed. For an eight-channel coil, such as the one employed in Ref.1, the factor between the noise mean in a background ROI (mair) and the noise standard deviation (σmean) obtained with Eq. [3] is 0.254 and is significantly different from the value of 0.797 obtained with Eq. [1]. This important difference probably explains why the ratios SNR(8CH)/SNR(1CH) presented in Table 1 and 2 of Ref.1 are significantly lower for SNRmean when compared to the values obtained with the other methods. The values of SNRstdv for the same ratios are in better agreement with the values obtained by using the other methods because Eqs. [2] and [4] give factors that are more similar (1.526 in comparison to 1.426). The evaluation of the signal standard deviation from a background ROI definitely appears risky when parallel imaging and reconstruction filters are employed or when the correlation between the channels is high. However, if correlation is low and as long as a standard sum-of-squares reconstruction without parallel imaging is employed, the use of the good factors should definitely improve the accuracy of the SNR estimation when multichannel coils are used, especially when coils with different number of channels are compared. Guillaume Gilbert BSc*, * Department of Radiology, Centre Hospitalier de l'Université de Montréal (CHUM), Montreal, Quebec, Canada.