Low Signal-to-noise Ratio Conditions Research Articles

Vibration phases estimation based on multi-channel interferometry for ISAL.

Target vibration introduces unexpected vibration phases in the echo signal of inverse synthetic aperture LADAR (ISAL). The vibration phases cause the imaging results to be defocused. How to estimate the vibration phases has always been one of the difficulties in ISAL imaging, and there has not been an effective solution. In this paper, two methods are proposed to estimate the vibration phases in different situations. The method based on multi-channel along-track interferometry (MCATI method) is for some cases that the target velocity vector is parallel to some baselines of ISAL. The method based on orthogonal interferometry (OI method) is for other cases that the target velocity vector is not parallel to any baseline of ISAL. In addition, a high signal to noise ratio (SNR) is essential for the MCATI and OI methods to get high estimation accuracy. Therefore, a large power-aperture product should be carefully considered in the ISAL system design. Furthermore, on the condition of low SNR, range cell accumulation is also helpful to reduce the influence of noise. Take the ISAL, which is utilized to image the satellite in 500km orbit, as an example, the estimation accuracy can satisfy the demand of synthetic aperture imaging with 0dB single-pulse SNR and 300 range cell accumulation. The proposed methods are verified with simulation and experiment.

Tracking and detecting highs maneuvering weak targets

Modern military targets as aircraft are able to perform high maneuvers due to their complex design. This ability of maneuvers includes sudden changes in acceleration and high-G turns that are not achievable from traditional military targets. Moreover, recent military targets generally have a low radar cross-section or low signal-to-noise ratio (SNR) profile, which makes the detection and tracking of those maneuvering targets a complicated dynamic state estimation problem. In this case, the track-before-detect filter (TBDF) that uses unthresholded measurements is considered as an effective method for tracking and detecting a single maneuvering target under low SNR conditions. Nevertheless, the performance of the algorithm will be affected by severe loss because of the mismatching of target model during maneuver. To resolve the target maneuvers, we propose an application of particle filtering, which depends on TBD. We employ the constant acceleration model and coordinate turn model. Our simulation results show that the detection and tracking of maneuvering target performance of TBDF has been improved using the proposed algorithm.

Periodic component analysis as a spatial filter for SSVEP-based brain–computer interface

BackgroundTraditional spatial filters used for steady-state visual evoked potential (SSVEP) extraction such as minimum energy combination (MEC) require the estimation of the background electroencephalogram (EEG) noise components. Even though this leads to improved performance in low signal to noise ratio (SNR) conditions, it makes such algorithms slow compared to the standard detection methods like canonical correlation analysis (CCA) due to the additional computational cost. New methodIn this paper, Periodic component analysis (πCA) is presented as an alternative spatial filtering approach to extract the SSVEP component effectively without involving extensive modelling of the noise. The πCA can separate out components corresponding to a given frequency of interest from the background electroencephalogram (EEG) by capturing the temporal information and does not generalize SSVEP based on rigid templates. ResultsData from ten test subjects were used to evaluate the proposed method and the results demonstrate that the periodic component analysis acts as a reliable spatial filter for SSVEP extraction. Statistical tests were performed to validate the results. Comparison with existing methodsThe experimental results show that πCA provides significant improvement in accuracy compared to standard CCA and MEC in low SNR conditions. ConclusionsThe results demonstrate that πCA provides better detection accuracy compared to CCA and on par with that of MEC at a lower computational cost. Hence πCA is a reliable and efficient alternative detection algorithm for SSVEP based brain–computer interface (BCI).

Highly accurate adaptive TOF determination method for ultrasonic thickness measurement

Determining the time of flight (TOF) is very critical for precise ultrasonic thickness measurement. However, the relatively low signal-to-noise ratio (SNR) of the received signals would induce significant TOF determination errors. In this paper, an adaptive time delay estimation method has been developed to improve the TOF determination’s accuracy. An improved variable step size adaptive algorithm with comprehensive step size control function is proposed. Meanwhile, a cubic spline fitting approach is also employed to alleviate the restriction of finite sampling interval. Simulation experiments under different SNR conditions were conducted for performance analysis. Simulation results manifested the performance advantage of proposed TOF determination method over existing TOF determination methods. When comparing with the conventional fixed step size, and Kwong and Aboulnasr algorithms, the steady state mean square deviation of the proposed algorithm was generally lower, which makes the proposed algorithm more suitable for TOF determination. Further, ultrasonic thickness measurement experiments were performed on aluminum alloy plates with various thicknesses. They indicated that the proposed TOF determination method was more robust even under low SNR conditions, and the ultrasonic thickness measurement accuracy could be significantly improved.

Bayesian Bistatic ISAR Imaging for Targets with Complex Motion under Low SNR Condition.

This paper proposes a novel bistatic inverse synthetic aperture radar (ISAR) imaging algorithm for the target with complex motion under low signal to noise ratio (SNR) condition. Note the bistatic ISAR system generally suffers from a lower SNR than the monostatic one because of its non-mirror reflection geometry. A de-noising method, therefore, is proposed to improve SNR of range profiles, which accumulates the aligned range profiles non-coherently to obtain a window for noise suppression. Additionally, since the complex motion of target induces nonstationary Doppler, which is destructive to ISAR imaging, an optimal coherent processing interval (CPI) selection algorithm is further proposed to find out the interval where the Doppler is relatively stationary, so as to produce well-focused ISAR images. It utilizes the reassigned time-frequency (TF) method to obtain the high resolution instantaneous Doppler spectrum, and the minimum entropy criterion to select the optimal CPI, respectively. Note the selected CPI often contains too limited pulses to produce ISAR images with high resolution. A sparse aperture ISAR imaging method within the Bayesian framework is further proposed, which introduces the Laplacian scale mixture (LSM) model as the sparse prior, so as to reconstruct well-focused ISAR images with high resolution and low side lobes from the limited data. Compared with the traditional sparse Bayesian learning method, the proposed LSM based ISAR imaging performs superiorly on resolution improvement and noise reduction. Experimental results based on both simulated and measured data validate the effectiveness of the proposed algorithms.

The Improved Adaptive Silence Period Algorithm over Time-Variant Channels in the Cognitive Radio System

In the field of cognitive radio spectrum sensing, the adaptive silence period management mechanism (ASPM) has improved the problem of the low time-resource utilization rate of the traditional silence period management mechanism (TSPM). However, in the case of the low signal-to-noise ratio (SNR), the ASPM algorithm will increase the probability of missed detection for the primary user (PU). Focusing on this problem, this paper proposes an improved adaptive silence period management (IA-SPM) algorithm which can adaptively adjust the sensing parameters of the current period in combination with the feedback information from the data communication with the sensing results of the previous period. The feedback information in the channel is achieved with frequency resources rather than time resources in order to adapt to the parameter change in the time-varying channel. The Monte Carlo simulation results show that the detection probability of the IA-SPM is 10–15% higher than that of the ASPM under low SNR conditions.

A Robust and Higher Precision Time Delay Estimation Method Facing Low Signal to Noise Ratio Conditions

Many available signals in the real world are usually weak with impulse noises and/or outliers, and we also need to have higher estimation precision in applications. Our focus of attention is pretty much on integrating robustness and accuracy under lower signal to noise ratio (SNR) with impulse noises. Although traditional fractional adaptive time delay estimation (TDE) methods have higher precision, the results of estimation are unreasonable when the signals contain some impulse noises. While, most proposed robust algorithms later can work well mainly with high SNR. In this paper, considering the practical problem in equipment fault acoustic localization based on TDE methods, an improved robust fractional adaptive time delay estimation method is addressed facing lower SNR conditions. First, the impulse noises are modeled as Alpha stable distribution, and the integer part of TDE is getting by using covariate correlation approach. Then, the integer estimation value is used as initial parameter value of time delay. Covariant sequence is the input of time delay estimator. Next, fractional TDE value is adaptive obtained by iteration under minimum average <i>p</i> norm criterion. Covariant sequence weakens irrelevant noises, meanwhile preserves time delay information between original sequences. Computer simulations and comparative experiments show that improved method has better estimation results. This method is robust and higher precision, and especially under impulse environment and low SNR conditions.

A High-Precision Method of Phase-Derived Velocity Measurement and Its Application in Motion Compensation of ISAR Imaging

The existing methods for motion compensation in inverse synthetic aperture radar (ISAR) imaging are generally limited to the low-order target motion model, and require iterative optimization with limited velocity estimate precision and heavy computational burdens. This paper proposes a high-precision method of phase-derived velocity measurement (PDVM) and applies it to motion compensation of ISAR imaging. The method applies PDVM based on range profiles cross correlation to the translational velocity estimation of targets, and converts the velocity measurement results to the corresponding range increment. The equivalent phase-derived range measurement precision can reach the order of magnitude of millimeter (mm) or even sub-mm, which can satisfy the precision requirements of both envelope alignment and phase adjustment. The key to realizing PDVM is resolving phase ambiguity. The traditional method for resolving ambiguity has very high requirements for the signal-to-noise ratio (SNR). This work resolves ambiguity by combining multiframe data, i.e., by resolving ambiguity of multiframe data simultaneously instead of resolving ambiguity of single-frame data independently and correcting the above ambiguity-resolving results using a minimum-entropy method. Therefore, phase ambiguity can be correctly resolved under a relatively low SNR. Experimental results of an ISAR imaging of an airplane show that the method proposed in this paper can obtain high-quality ISAR imagery, and can efficiently realize robust imaging under the conditions of low SNR.

Impact of light-adaptive mechanisms on mammalian retinal visual encoding at high light levels.

A persistent change in illumination causes light-adaptive changes in retinal neurons. Light adaptation improves visual encoding by preventing saturation and by adjusting spatiotemporal integration to increase the signal-to-noise ratio (SNR) and utilize signaling bandwidth efficiently. In dim light, the visual input contains a greater relative amount of quantal noise, and vertebrate receptive fields are extended in space and time to increase SNR. Whereas in bright light, SNR of the visual input is high, the rate of synaptic vesicle release from the photoreceptors is low so that quantal noise in synaptic output may limit SNR postsynaptically. Whether and how reduced synaptic SNR impacts spatiotemporal integration in postsynaptic neurons remains unclear. To address this, we measured spatiotemporal integration in retinal horizontal cells and ganglion cells in the guinea pig retina across a broad illumination range, from low to high photopic levels. In both cell types, the extent of spatial and temporal integration changed according to an inverted U-shaped function consistent with adaptation to low SNR at both low and high light levels. We show how a simple mechanistic model with interacting, opponent filters can generate the observed changes in ganglion cell spatiotemporal receptive fields across light-adaptive states and postulate that retinal neurons postsynaptic to the cones in bright light adopt low-pass spatiotemporal response characteristics to improve visual encoding under conditions of low synaptic SNR.

Quantization of high dimensional Gaussian vector using permutation modulation with application to information reconciliation in continuous variable QKD

This paper is focused on the problem of Information Reconciliation (IR) for continuous variable Quantum Key Distribution (QKD). The main problem is quantization and assignment of labels to the samples of the Gaussian variables observed at Alice and Bob. Trouble is that most of the samples, assuming that the Gaussian variable is zero mean which is de-facto the case, tend to have small magnitudes and are easily disturbed by noise. Transmission over longer and longer distances increases the losses corresponding to a lower effective Signal-to-Noise Ratio (SNR) exasperating the problem. Quantization over higher dimensions is advantageous since it allows for fractional bit per sample accuracy which may be needed at very low SNR conditions whereby the achievable secret key rate is significantly less than one bit per sample. In this paper, we propose to use Permutation Modulation (PM) for quantization of Gaussian vectors potentially containing thousands of samples. PM is applied to the magnitudes of the Gaussian samples and we explore the dependence of the sign error probability on the magnitude of the samples. At very low SNR, we may transmit the entire label of the PM code from Bob to Alice in Reverse Reconciliation (RR) over public channel. The side information extracted from this label can then be used by Alice to characterize the sign error probability of her individual samples. Forward Error Correction (FEC) coding can be used by Bob on each subset of samples with similar sign error probability to aid Alice in error correction. This can be done for different subsets of samples with similar sign error probabilities leading to an Unequal Error Protection (UEP) coding paradigm.

Noise‐robust HRRP target recognition method via sparse‐low‐rank representation

A novel target recognition method is proposed for high-range resolution profiles (HRRPs) of radar targets under low signal-to-noise ratio (SNR) conditions. This method achieves good recognition performance for noisy HRRPs with discriminative sparse-low-rank representation. The framework of this method is constructed based on sparse representation and low-rank representation, which are applied to extract the local and global characteristics of target HRRPs. To guarantee the noise-robust and highly discriminative features of the HRRPs, dictionary learning is adopted. In the training stage, a discriminative dictionary is produced based on hinge loss theory to improve the recognition performance. Denoising dictionary optimisation is implemented for noise suppression during the testing stage. Experimental results on measured HRRP data demonstrate that the proposed method can recover the original HRRPs and significantly improve the recognition performance for HRRP test samples under relatively low SNR conditions.

Intrapulse modulation type recognition for pulse compression radar signal

The existing modulation recognition algorithms for a pulse compression radar (PCR) signal can hardly adapt to complex modulation types and low signal-to-noise ratio (SNR). To solve the problems, with respect to the seven kinds of widely used PCR signals—including linear frequency modulation signal, Baker code, Frank code, P1 code, P2 code, P3 code, and P4 code—a modulation type recognition algorithm based on integrated quadratic phase function (IQPF) and fractional Fourier transform (FrFT) is proposed. First, signals are preclassified according to their chirp rates (CRs) estimated through IQPF. Then, FrFT is carried out depending on the order, which is correlated to the estimated CR. Finally, signals in each class are subdivided and modulation recognition is accomplished according to the features of the FrFT spectrum. The simulation results validate the feasibility of the algorithm. They also demonstrate that, compared against existing research, the proposal achieves better correct recognition performance for various modulation types under low SNR condition.

Intrusion signal recognition in OFPS under multi-level wavelet decomposition based on RVFL neural network

The performance of the optical fiber pre-warning system (OFPS) is susceptible to the interference of background noise, especially in the low signal-to-noise ratio (SNR) environment. In this paper, a recognition method based on the multi-level wavelet decomposition is proposed to accurately identify the running and digging intrusion signals in OFPS under the low SNR condition. The method includes the cross-correlation operation, feature extraction, and recognition using a random vector functional-link (RVFL) neural network (NN). Firstly, the collected signals are processed by the cross-correlation operation. Compared with the conventional filtering method, the background noise can be suppressed to the greatest extent by the cross-correlation operation, which keeps the signal details as much as possible. Secondly, the cross-correlation functions obtained from the cross-correlation operation are decomposed into five levels by the multi-level wavelet decomposition. And then the average energy ratios can be obtained along with the decomposed levels, and we select these ratios in the five frequency bands as the feature of intrusion signals. Next, the feature samples are sent into the RVFL NN for training so as to complete the recognition of these intrusion signals. Finally, the effectiveness of the algorithm is verified by the field experiment.

Comparison of Hybrid Optical Modulation Schemes for Visible Light Communication

In this paper, three hybrid modulation schemes for visible light communication based on orthogonal frequency division multiplexing (OFDM) are compared, which are asymmetrically clipped dc-biased optical OFDM, hybrid asymmetrically clipped optical OFDM, and layered asymmetrically clipped optical OFDM (LACO-OFDM). The following aspects are analyzed and compared, such as the probability distribution functions, the peak-to-average power ratio, and the bit error ratio performance in terms of optical bit energy to noise power under different conditions. Furthermore, the optimal proportion of the optical power on different layers for LACO-OFDM is also investigated. Simulation results show that LACO-OFDM has better performance than other hybrid schemes in high signal-to-noise ratio (SNR) scenario, and the improvement is slight when more than four layers signals are utilized, while in low SNR condition, LACO-OFDM with two layers performs better than that with more layers.

Spectrum Sensing Algorithm Based on Double Threshold and Two-Stage Detection Under a Low SNR

In cognitive radio networks, spectrum sensing algorithms almost perform poor in low signal-to-noise ratio (SNR) sensing environment, it does need to improve detection probability by increasing the detection time. In order to solve the mentioned problems, a new spectrum sensing algorithm based on the double threshold and two-stage detection strategy under the condition of low SNR is put forward in this paper. First of all, when a false-alarm probability is given, the sampling length is found out under the different SNR level. Then the first stage detection is carried out by designing the two detection threshold. Finally the second stage detection is implemented for the center portion of the double threshold and the final detection result is gotten. Simulations and analysis show that, when the SNR is low, the proposed algorithm not only has a short average sensing time but also has a good performance in a small false-alarm probability constraint condition and low algorithm complexity.

Enhanced Speech Based Jointly Statistical Probability Distribution Function for Voice Activity Detection

Most of Voice activity detection (VAD) methods are based on statistical model. In these methods, the noise signal is always assumed to satisfy and characterized by Gaussian distribution, while the assumption of noise does not always hold in practice and which causes that these kinds of method fail to distinguish speech from noise at low Signal-noise-ratio (SNR) level in non-stationary noise condition. For going further to improve the robustness of VAD, a enhanced speech based method is proposed. In the proposed method, the Laplacian distribution is used to model the remained noise since we find that the remained noise in enhanced speech satisfy Laplacian distribution; in addition, Gaussian mixture model is used to characterize the Discrete Fourier transform (DFT) coefficients of reconstructed speech in enhanced speech. Experimental results show that the proposed method performs better than the baseline method, especially in low SNR and non-stationary noise conditions.

Signal processing for fast Raman imaging under low SNR condition and its applications in Raman imaging

Imaging in Raman spectroscopy is a valuable tool for analytical chemistry, cell research and so on. At present, low SNR (signal-noise ratio) of the Raman spectral data set and the data processing speed of the imaging process are the two constraints of observing some dynamic change reactions. In this paper, a fast univariate Raman imaging algorithm named CPSA (Characteristic Peak Sparse Approximation) which is based on sparse approximation, least square and Butterworth low pass filter was presented to generate the Raman image under low SNR condition, ant it was proved to be timesaving as well as high imaging quality. In CPSA algorithm, sparse approximation is used to eliminate the noises of the target Raman signals; least square is used to enhance the useful Raman signals; Butterworth low pass filter is used to improve the image quality. Three experiments were conducted and the results were presented. The first experiment was used to validate the reliability of the proposed method, and the left two experiments were its applications in medical and human cancerous cell imaging.

X-ray pulsar signal de-noising for impulse noise using wavelet packet

In order to solve the impact of the impulse noise in the universe on the long time epoch folding of X-ray pulsar profile, an innovative denoising method using the wavelet packet transformation is proposed. First, the system model containing noisy signal for X-ray pulsar is established. The time series of X-ray pulsar are sampled as certain time interval and folded as its rotational period. Secondly, the signal transformed by wavelet packet is computed according to the given entropy, aiming to obtain the optimum basis. Finally, the impulse noise is filtered with the proper threshold. The experimental results show that the impulse noise can be filtered by the proposed method, and the algorithm improves the signal-to-noise ratio (SNR) by about 40% compared with other methods under low SNR conditions.

Hybrid Precoding for mmWave Massive MIMO Systems With Partially Connected Structure

Hybrid precoding is widely studied in millimetre-wave (mmWave) massive MIMO systems due to low cost as well as low power consumption. In general, there are two kinds of hybrid precoding structures: one is fully connected structure (FCS), where each radio frequency (RF) chain is connected to all antennas, and the other is partially connected structure (PCS), where each RF chain is connected to a sub-array. In this paper, we investigate the optimal hybrid precoder design problem for mmWave massive MIMO systems based on PCS, since this kind of structure is more practical for antenna deployment. We first focus on the optimization of analog precoder (AP) and propose two AP design schemes for high signal-to-noise ratio (SNR) condition and low SNR condition, respectively. For each of the schemes, the original optimization problem is reformulated to single-stream optimal transmitter beamforming problem with per-antenna power constraint, which has an optimal solution. Then, the optimal digital precoder is obtained by water-filling algorithm after AP is determined. Moreover, upper bounds of the achievable data rate for the proposed schemes with closed-form expression are derived.

Applications of Cell-Ratio Constant False-Alarm Rate Method in Coherent Doppler Wind Lidar

A cell-ratio constant false-alarm rate (CR-CFAR) method for detecting the Doppler frequency shift is proposed to improve the accuracy of velocity measured by coherent Doppler wind lidar (CWL) in low signal-to-noise ratio (SNR) environments. The method analyzes the spectrum to solve issues of weak signal submergence in noise encountered in the widely used periodogram method. This characteristic is that the signal region slope is larger than the noise region slope in the frequency spectrum. We combined the ratio and CFAR to propose the CR-CFAR method. The peak area is discriminated from the spectrum using this method. By removing background noise, the peak signal is obtained along with the Doppler shift. To verify the CR-CFAR method, a campaign experiment using both CWL and a commercial Doppler lidar was performed in Hami, China (42°32′ N, 94°03′ E) during 1–7 June 2016. The results showed that the proposed method significantly improved the reliability of CWL data under low SNR conditions. The height—at which both horizontal wind speed correlativity and horizontal wind direction correlativity exceeded 0.99—increased by 65 m. The relative deviation of the horizontal wind speed at 120 m decreased from 40.37% to 11.04%. We used the CR-CFAR method to analyze continuous data. A greater number of wind field characteristics were obtained during observation compared to those obtained using the common wind field inversion method.