Low SNR Conditions Research Articles

A Study of Timing Recovery for High-Recording-Density Tape Systems

Today's tape storage systems are widely used as a low-cost solution for data backup and archiving for the rapidly growing amount of digital information produced in various fields. Following the Information Storage Industry Consortium (INSIC) Roadmap [1], tape cartridge capacity continues to scale by doubling the capacity every two years. Developments in read channel technology are expected to play an important role in improving data-storage capability. The introduction of powerful error-correction codes, such as low density parity check (LDPC) codes to the tape storage system (and which have already been applied to current HDD systems) is anticipated to achieve system operation under low SNR conditions, enabling additional increases in areal recording density. However, at a low SNR, a loss-of-lock (LOL) in the phase-locked loop (PLL) will frequently occur in the current timing recovery scheme. Previous studies of the iterative timing recovery scheme [2], [3] enable systems to operate robustly even at low SNR conditions. However, due to the complexity of these systems, the cost of hardware makes it difficult to introduce iterative timing recovery schemes into multichannel tape systems. In this work, it is shown that erroneous feedback in the PLL caused by misdetection of the detector is the major cause for LOL in tape systems at high recording densities. To cope with these misdetections of the detector while maintaining an acceptable latency in the feedback loop, a new timing recovery scheme that utilizes the soft-output information of the detector was studied. The results show that LOL rate performance can clearly be improved by such schemes, with only a small increase in implementation complexity.

Sliding window energy detection for spectrum sensing under low SNR conditions

AbstractFor spectrum sensing, energy detection has the advantages of low complexity, rapid analysis, and requires no knowledge of the transmission signal, which makes it suitable for a wide range of applications. However, under low signal‐to‐noise ratio conditions, the required window length (or the time‐bandwidth product) for energy detection to achieve a desired detection performance is large. In addition, conventional energy detection assumes that the detection tests are independent, that is, there is no overlap between individual detection tests. These properties significantly reduce the detection speed when energy detection is used for the continuous monitoring over a communication channel for the detection of signal transmission activities. In this paper, we propose a sliding window detection analysis with overlap among multiple tests. Algorithms for effective performance analysis of the proposed sliding window energy detection are proposed. The impact of window length on distribution of detection time is investigated. Simulation results on the proposed sliding window energy detection are also compared with the theoretically predicted and conventional energy detection performance estimates. Copyright © 2015 John Wiley & Sons, Ltd.

Application of signal sparse decomposition in the detection of partial discharge by ultrasonic array method

The detection of partial discharge of power transformer is important for the safe operation of a power system. The direction of arrival is an important part of ultrasonic array detection. Classic DOA estimation algorithms based on subspace analysis do not perform well under the conditions of low SNR and small snapshot numbers. The problem with using an improved algorithm is that it requires a huge amount of calculation. In this paper sparse signal decomposition is applied to spatial spectrum estimation. The authors derived the model of array signal direction finding by the use of matching pursuit algorithm, setting up overcomplete dictionaries for different types of sensor array and simulating different types of sensor array receiving signals. Simulations and experiments show that direction finding based on MP decomposition method performs better than using the MUSIC algorithm under the conditions of low SNR and small snapshot numbers, and that the influence of array types is less.

Reference-free removal of EEG-fMRI ballistocardiogram artifacts with harmonic regression

Combining electroencephalogram (EEG) recording and functional magnetic resonance imaging (fMRI) offers the potential for imaging brain activity with high spatial and temporal resolution. This potential remains limited by the significant ballistocardiogram (BCG) artifacts induced in the EEG by cardiac pulsation-related head movement within the magnetic field. We model the BCG artifact using a harmonic basis, pose the artifact removal problem as a local harmonic regression analysis, and develop an efficient maximum likelihood algorithm to estimate and remove BCG artifacts. Our analysis paradigm accounts for time–frequency overlap between the BCG artifacts and neurophysiologic EEG signals, and tracks the spatiotemporal variations in both the artifact and the signal. We evaluate performance on: simulated oscillatory and evoked responses constructed with realistic artifacts; actual anesthesia-induced oscillatory recordings; and actual visual evoked potential recordings. In each case, the local harmonic regression analysis effectively removes the BCG artifacts, and recovers the neurophysiologic EEG signals. We further show that our algorithm outperforms commonly used reference-based and component analysis techniques, particularly in low SNR conditions, the presence of significant time–frequency overlap between the artifact and the signal, and/or large spatiotemporal variations in the BCG. Because our algorithm does not require reference signals and has low computational complexity, it offers a practical tool for removing BCG artifacts from EEG data recorded in combination with fMRI.

Speech Enhancement Under Low SNR Conditions Via Noise Estimation Using Sparse and Low-Rank NMF with Kullback–Leibler Divergence

A key stage in speech enhancement is noise estimation which usually requires prior models for speech or noise or both. However, prior models can sometimes be difficult to obtain. In this paper, without any prior knowledge of speech and noise, sparse and low-rank nonnegative matrix factorization (NMF) with Kullback-Leibler divergence is proposed to noise and speech estimation by decomposing the input noisy magnitude spectrogram into a low-rank noise part and a sparse speech-like part. This initial unsupervised speech-noise estimation allows us to set a subsequent regularized version of NMF or convolutional NMF to reconstruct the noise and speech spectrogram, either by estimating a speech dictionary on the fly (categorized as unsupervised approaches) or by using a pre-trained speech dictionary on utterances with disjoint speakers (categorized as semi-supervised approaches). Information fusion was investigated by taking the geometric mean of the outputs from multiple enhancement algorithms. The performance of the algorithms were evaluated on five metrics (PESQ, SDR, SNR, STOI, and OVERALL) by making experiments on TIMIT with 15 noise types. The geometric means of the proposed unsupervised approaches outperformed spectral subtraction (SS), minimum mean square estimation (MMSE) under low input SNR conditions. All the proposed semi-supervised approaches showed superiority over SS and MMSE and also obtained better performance than the state-of-the-art algorithms which utilized a prior noise or speech dictionary under low SNR conditions.

Blind SNR Estimation of Gaussian-Distributed Signals in Nakagami Fading Channels

A blind (non-data-aided) SNR estimator using the statistical moments of the received signal is proposed. The proposed envelope-based non-data-aided estimator works for any time-domain Gaussian-distributed signal (e.g., OFDM signals). A closed-form expression for the estimated SNR as a function of the moments of the received signal and the Nakagami- $m$ parameter, is derived. Interestingly, the obtained expression shows that the proposed estimator operation and performance is independent of the constellation of the received signal. Moreover, the existence of the closed-form expression results in lower implementation complexity. Furthermore, to enable theoretical performance analysis, a general mathematical expression is derived for the even moments of the received signal in terms of SNR and the Nakagami- $m$ parameter. The performance of the proposed estimator is evaluated based on the mean-squared-error, under different conditions of the channel. An extension of our SNR estimation method into multiple antennas configurations is provided. Our results reveal that the proposed estimator works better in low SNR conditions, which is attractive to applications such as cognitive radio spectrum sharing scenarios.

A unified variational segmentation framework with a level-set based sparse composite shape prior

Image segmentation plays an essential role in many medical applications. Low SNR conditions and various artifacts makes its automation challenging. To achieve robust and accurate segmentation results, a good approach is to introduce proper shape priors. In this study, we present a unified variational segmentation framework that regularizes the target shape with a level-set based sparse composite prior. When the variational problem is solved with a block minimization/decent scheme, the regularizing impact of the sparse composite prior can be observed to adjust to the most recent shape estimate, and may be interpreted as a ‘dynamic’ shape prior, yet without compromising convergence thanks to the unified energy framework. The proposed method was applied to segment corpus callosum from 2D MR images and liver from 3D CT volumes. Its performance was evaluated using Dice Similarity Coefficient and Hausdorff distance, and compared with two benchmark level-set based segmentation methods. The proposed method has achieved statistically significant higher accuracy in both experiments and avoided faulty inclusion/exclusion of surrounding structures with similar intensities, as opposed to the benchmark methods.

Chaotic compressive measurement and reconstruction of binary signals

Compressive sensing of binary signals is corresponding to the problem of binary symbol detection in the faster-than-Nyquist signaling systems, which has significant research value. Traditional compressive measurement of a binary signal is based on Gaussian matrix, and l1 minimization is a classic algorithm for signal reconstruction. However, stochastic matrix such as the Gaussian matrix can hardly be realized by a digital circuit, and the reconstruction performance of l1 minimization is not well enough for binary signals. Thus, it is of great meaning to construct a new kind of measurement matrix as well as a better reconstruction algorithm for binary signals. This paper constructs a chaotic circulant measurement matrix based on Cat chaotic map (CCMM), and proposes a brand new algorithm for binary signal reconstructionsmooth function approximation method (SFAM). Chaotic sequence has characteristics of both internal certainty and external randomness, while a circulant matrix requires less elements and can be realized through fast Fourier transform. CCMM conbines the advantages of both chaotic sequence and circulant matrix, so that it not only satisfies the RIPless property required by the compressive measurement matrix because of external randomness, but also has the power to resist the effect of low signaling efficiency and low SNR due to the internal certainty. Moreover, the circle structure gives CCMM the potential to be digital realized in the future. In SFAM, we first use a non-convex function to approximate the original discontinuous objective function, in order to transfer the original combinatorial optimization problem into an optimization problem with equality constraints which can be solved much easier. Then we use the interior point method to solve this optimization problem. Furthermore, sparse Bayesian learning algorithm is used to correct the reconstruction error for a more accurate result. Compressive measurement and reconstruction of binary signals in additive Gaussian white noise channel are operated. Result of numerical experiments shows that CCMM is much better than the traditional Gaussian matrix for compressive measurement, especially in the condition of low signaling efficiency and low SNR, and SFAM is much better than l1 minimization for binary signal reconstruction. At the end of this paper, we explain the essential reason why CCMM performs better than the traditional Gaussian matrix, through calculating the autocorrelation function of compressive measurement vector in various conditions.

Subspace Compressive GLRT Detector for MIMO Radar in the Presence of Clutter.

The problem of optimising the target detection performance of MIMO radar in the presence of clutter is considered. The increased false alarm rate which is a consequence of the presence of clutter returns is known to seriously degrade the target detection performance of the radar target detector, especially under low SNR conditions. In this paper, a mathematical model is proposed to optimise the target detection performance of a MIMO radar detector in the presence of clutter. The number of samples that are required to be processed by a radar target detector regulates the amount of processing burden while achieving a given detection reliability. While Subspace Compressive GLRT (SSC-GLRT) detector is known to give optimised radar target detection performance with reduced computational complexity, it however suffers a significant deterioration in target detection performance in the presence of clutter. In this paper we provide evidence that the proposed mathematical model for SSC-GLRT detector outperforms the existing detectors in the presence of clutter. The performance analysis of the existing detectors and the proposed SSC-GLRT detector for MIMO radar in the presence of clutter are provided in this paper.

送電線用ディジタル電力線搬送における周波数オフセット補償方式

The transmission performance of a digital power line carrier system with adaptive equalizer degrades in the presence of the carrier frequency offset (CFO). Therefore, accurate CFO estimation and compensation is necessary. In this paper, we propose a CFO estimation method suitable for a digital power line carrier system with adaptive equalizer in the presence of delayed propagation paths. The proposed CFO estimation method estimates the CFO from the measured phase rotation per symbol which is obtained by the measurement of autocorrelation of a 4PSK training sequence. By setting the time separation for autocorrelation measurement to the training sequence length, the accurate CFO estimation is achieved in the presence of delayed propagation paths. In order to achieve sufficiently accurate CFO estimation in a low SNR condition, averaging the instantaneous autocorrelation measurements is done by a simple first-order filter. It was confirmed by computer simulation that even in the presence of delayed propagation paths, by using the proposed CFO estimation method, a digital power line carrier system with adaptive equalizer achieves a BER performance close to the case of no CFO.

Online Monaural Speech Enhancement Based on Periodicity Analysis and A Priori SNR Estimation

This paper describes an online algorithm for enhancing monaural noisy speech. First, a novel phase-corrected low-delay gammatone filterbank is derived for signal subband decomposition and resynthesis; the subband signals are then analyzed frame by frame. Second, a novel feature named periodicity degree (PD) is proposed to be used for detecting and estimating the fundamental period ( ${{\rm P}_0}$ ) in each frame and for estimating the signal-to-noise ratio (SNR) in each frame-subband signal unit. The PD is calculated in each unit as the multiplication of the normalized autocorrelation and the comb filter ratio, and shown to be robust in various low-SNR conditions. Third, the noise energy level in each signal unit is estimated recursively based on the estimated SNR for units with high PD and based on the noisy signal energy level for units with low PD. Then the a priori SNR is estimated using a decision-directed approach with the estimated noise level. Finally, a revised Wiener gain is calculated, smoothed, and applied to each unit; the processed units are summed across subbands and frames to form the enhanced signal. The ${{\rm P}_{ 0}}$ detection accuracy of the algorithm was evaluated on two corpora and showed comparable performance on one corpus and better performance on the other corpus when compared to a recently published pitch detection algorithm. The speech enhancement effect of the algorithm was evaluated on one corpus with two objective criteria and showed better performance in one highly non-stationary noise and comparable performance in two other noises when compared to a state-of-the-art statistical-model based algorithm.

Guaranteed Stability of Sparse Recovery in Distributed Compressive Sensing MIMO Radar

Low SNR condition has been a big challenge in the face of distributed compressive sensing MIMO radar (DCS-MIMO radar) and noise in measurements would decrease performance of radar system. In this paper, we first devise the scheme of DCS-MIMO radar including the joint sparse basis and the joint measurement matrix. Joint orthogonal matching pursuit (JOMP) algorithm is proposed to recover sparse targets scene. We then derive a recovery stability guarantee by employing the average coherence of the sensing matrix, further reducing the least amount of measurements which are necessary for stable recovery of the sparse scene in the presence of noise. Numerical results show that this scheme of DCS-MIMO radar could estimate targets’ parameters accurately and demonstrate that the proposed stability guarantee could further reduce the amount of data to be transferred and processed. We also show the phase transitions diagram of the DCS-MIMO radar system in simulations, pointing out the problem to be further solved in our future work.

Novel Compressed Sensing Algorithm Based on Modulation Classification and Symbol Rate Recognition

In the complex and changing radio environment, how to achieve the fast and accurate spectrum sensing over an ultra-wide bandwidth is a big challenge. A novel compressed sensing algorithm based on modulation classification and symbol rate recognition is proposed by using the minimal sampling rate to detect spectrum holes. It is more efficient than the Nyquist sampling rate and traditional compressed sampling rate, which requires the reconstruction of the original signal. Simulation results show that it can further decrease the compressed sampling rate depending on the relation between compression ratio with modulation scheme and symbol rate. Furthermore, in order to improve the accuracy of modulation classification and symbol rate recognition, a compressed sensing and wavelet transform (CS-WT) feature detector is proposed to perform wideband detection in low SNR condition. Simulation results show that CS-WT feature detector can effectively reduce the noise introduced by CS process. Given the false alarm of 0.05 and the detection probability of 0.9, the detection probability of proposed CS-WT feature detection algorithm can be improved 4 dB compared to traditional cyclostationary detection. Therefore, the overall sampling rate can be dramatically reduced without spectrum detection performance deterioration compared to the conventional static sampling algorithm.

Robust Direction of Arrival Estimation using Multiple Signal Analysis

Direction of arrival (DOA) estimation is one of the focal problems in the fields of Wireless communications, Radar, Sonar, Radio Astronomy and Seismology. The main objective of the DOA estimation is to obtain the desired signals direction as well as the interference signals direction based on the data received from the array sensor at the base station. In literature various techniques are researched for DOA estimation. Among which are two high resolution algorithms, viz. MUSIC (MUltiple SIgnal Classification) and ESPRIT (Estimation of Signal Parameters via Rotational Invariance Technique, MUSIC algorithm is implemented in this paper with Empirical Mode Decomposition technique. The key feature of EMD is to decompose a signal into sum of intrinsic mode functions (IMF) with a final residue of non linear and non stationary signals. The work presented in this paper deals with EMD application to the problem of DOA estimation as a preprocessing technique. This technique separately de-noises the rows of the array data matrix where each row corresponds to the output of a particular sensor array. Especially in low-SNR conditions, the estimation performance of MUSIC algorithm is enhanced significantly when de-noising is given to array data matrix prior to DOA estimation stage.

English

This paper investigates the improvement of Telugu speech quality in terms of six objective quality measures using Discrete Wavelet Transform and proposes two Hybrid thresholding methods which are formed by combining soft and Improved thresholding methods with Modified Improved thresholding method. The performance of the new Hybrid methods is compared with the other thresholding methods. It is observed that the new proposed scheme yields better results when applied to Telugu noisy speech signals with low SNR (0dB) conditions. In this method, noisy speech signal is divided in to overlapping frames and each frame is windowed using hamming window. The windowed speech blocks are applied to the wavelet based speech enhancement algorithm and the enhanced speech is reconstructed in its time domain. For denoising the Telugu speech signal, various techniques like hard, soft, improved, modified improved and hybrid thresholding methods are used. Analysis is done using daubechies and symlets wavelets with different white Gaussian noise environments. Six Objective quality measures are considered in this study to test the performance of the algorithm for enhanced Telugu speech quality and compared. Hybrid thresholding methods perform better than hard, soft, improved and modified improved thresholding methods for wavelet based speech denoising.

Interference Suppression of VLF Communications in Atmospheric Noise

This paper focuses on the weak signal reception and atmospheric noise suppression in VLF Chirp spread spectrum communication system. Due to interference of atmospheric noise generated by lightning phenomena, it makes the chirp signal detection under low SNR condition becoming more difficult. Therefore, we select alpha stable distribution noise model to generate noise to simulate the real atmospheric noise. Then, we introduced a new technique of Chirp Spread Spectrum (CSS) communication based on Fractional Fourier Transform (FRFT). Finally, we study a nonlinear processing interference suppression method of clipping in time domain. Simulation results verified its feasibility.

Identification of radionuclides for the spectroscopic radiation portal monitor for pedestrian screening under a low signal-to-noise ratio condition

The spectroscopic radiation portal monitor (SPM) is widely used for homeland security. Many research groups are studying the radionuclide identification method which is one of the most important factors in the performance of the SPM using the large size of a thallium activated sodium iodide (NaI(Tl)) detector. In this study, we developed the radionuclide identification method for the SPM for pedestrian screening using a single NaI(Tl) detector that is small in size (2in.), which is much smaller than those in the existing studies under the low signal-to-noise-ratio (SNR) condition. From the anomalous radionuclide spectrum, the noise component was effectively reduced by the wavelet decomposition and the proposed background subtraction method, and the signal component was enhanced by the principal component analysis. Finally, peak locations which have been determined by the peak search algorithm with a valley check method were compared with a pre-calibrated and constructed radionuclide database. To verify the radiation identification performance of the proposed method, experiments with various kinds of sources (137Cs, 133Ba, 22Na, and 57Co) and different SNR values (from distances of 10–150cm and for scan times of 1–5s) were performed. Although the high-SNR condition was explored as well, most experiments were conducted under the low-SNR condition to verify the robustness and reproducibility of the proposed algorithm. The results showed that over 98.3% of the single radionuclide detection rate was achieved regardless of which radionuclides were used, up to 50cm under the worst SNR condition (1s of scan time) and up to 90cm under the best SNR condition (5s of scan time). Furthermore we achieved accurate identification of multiple radionuclides at 40cm with only 1s of scan time. The results show that the proposed algorithm is competitive with the commercial method and our radionuclide identification method can be successfully applied to the SPM for pedestrian monitoring, with a small detector size and a short scan time.

Endpiont Detection in Noisy Speech Signal Using Teager Energy Entropy

In this paper, we propose a new method using teager energy operator and entropy to solve endpoint detection problem in noisy environment. With the teager energy operator, it is sensitive on AM and FM signal and noise suppression capability on noisy speech signal, calculate teager energy of noisy speech signal. According to the different teager energy probability distribution between noise and speech signal, teager energy entropy is different. Set two soft thresholds of four states to detect the endpoint of noisy speech signal. The simulation shows that the method has good effect of endpoint detection in low SNR conditions, the simulation results show that teager energy entropy of speech signal endpoint detection in noisy environments is feasible and effective, and improves the reliability of endpoint detection.

Buzz, squeak, and rattle noise classification by using acoustic-fingerprinting technology

The buzz, squeak, and rattle (BSR) noises are three representative types of the automotive interior or exterior noise. Some of BSR noises have very short duration in time, and hence, it is difficult to detect various BSR noises in low SNR situation. However, each BSR noise signal has a unique time-frequency characteristic, depending on the various contacting materials as well as the excitation forces. Therefore, it is necessary to utilize the time-frequency characteristic of the BSR noise to specify the origin of a noise source. In this paper, we propose a novel method and system for identifying BSR noises. For accurate classification of noise sources, a noise-fingerprinting and matching technique based on the pattern classification is devised. The identification test with the real BSR noise data shows that the proposed method can accurately classify the noise source even in the low SNR condition.

Under Low SNR Condition Rapidly Varying Doppler Frequency Offset Influence on Carrier Frequency Acquisition and Simulation Analysis

The Quickly changed Doppler frequency offset is calculated and researched under low SNR during mobile satellite communications receiver carrier capture process. Firstly, current carrier frequency offset calculation technology is introduced, and the influence of the Doppler shift caused by the carrier capture is analyzed; Secondly, carrier frequency offset acquisition affected by changing rate of Doppler frequency offset is analyzed. Its mainly considered that first order changing rate and second order changing rate affect carrier acquisition. The changing rate would seriously reduces non-coherent accumulation number during carrier frequency offset estimation at low SNR; Finally, carrier frequency offset caused by the rate of change of the first order and second-order Doppler shift is analyzed and researched. It is demonstrated that platform effort caused by Doppler frequency offset can be improved, and the efficiency of the carrier capture increased through sampling points N reasonable by MATLAB simulation.