Adaptive Line Enhancer Research Articles

Adaptive Line Enhancer for Passive Sonars Based on Frequency-Domain Sparsity, Shannon Entropy Criterion and Mixed-Weighted Error

AbstractAdaptive line enhancer (ALE) is one of the vital signal processing techniques to the detection and recognition of underwater acoustic targets for passive sonars. Conventional ALEs, based on Gaussian noise assumption and least mean square (LMS) algorithm, can achieve good line enhancement property in Gaussian noise background. However, limited by the high steady-state misadjustment of LMS algorithm, the performance of conventional ALEs deteriorates under non-Gaussian noise background and degrades severely in processing signals with comparably lower signal-to-noise ratio (SNR). Therefore, it’s of great necessity to improve the line enhancement performances of ALE techniques to meet the demands of engineering application in passive sonars. In order to optimize the robustness and adaptability of conventional ALEs in dealing with underwater acoustic signals with much lower-SNR and in non-Gaussian noise background, a modified ALE algorithm called frequency-domain ALE based on l1-norm, Shannon entropy criterion and mixed-weighted norm (l1-SE-MWE-FALE) is proposed in this paper. The proposed l1-SE-MWE-FALE algorithm is based on the integration of frequency-domain sparsity, Shannon entropy (SE) criterion along with mixed-weighted error of LMS and least absolute deviation (LAD) to improve the ALE performance in situations above. The simulation results demonstrate that, when the input SNR is as low as – 25 dB, the local SNR (LSNR) gain for line spectrums by l1-SE-MWE-FALE is 9.8 dB, 3.7 dB and 2.3 dB higher than conventional ALE, l1-norm-based frequency-domain ALE (l1-FALE) and l1 norm-Shannon entropy criterion-based frequency-domain ALE (l1-SE-FALE), respectively. Meanwhile, the simulation results also indicate that the parameters of the proposed method can be chosen loosely and hence are insensitive to the choice of their values. Furthermore, the processing results of two different kinds of real ship-radiated noise signals recorded by passive sonars also imply the advantages of the proposed method over the other three ALEs both qualitatively and quantitatively in the respect of line spectrum LSNR gain and parameter insensitivity. The simulation and experiment results both validate the performance insensitivity to parameter adjustment and hence exhibit a good perspective of applications for passive sonars.

Application research on vector coherent frequency‐domain batch adaptive line enhancement in deep water

AbstractThe low frequency line spectrum noise radiated by ships has strong stability and is difficult to eliminate, which is the key information required for passive signal detection. A vector coherent frequency‐domain batch adaptive line enhancement method is proposed to address the issue of insufficient detection capability of traditional scalar adaptive line enhancement (ALE) algorithms for ship characteristic line spectra in complex deep‐sea environments. This method not only introduces the idea of frequency‐domain batch processing, but also uses synchronously collected sound pressure and particle velocity as dual input, fully utilising the coherence characteristics between vector channels to output high gain line spectrum signals and improve computational efficiency. In simulation and sea trial data validation, compared with the time‐domain vector coherent adaptive line enhancement algorithm, this method has shorter time consumption, higher efficiency, and can improve the detection ability of line spectrum signals under low signal‐to‐noise ratio conditions. The bearing estimation results output by this algorithm is also more accurate.

Blind cyclostationary fault feature extraction in rolling bearings: a dual adaptive filtering approach

Extracting cyclostationary features from vibration signals is one of the most effective approaches in bearing fault diagnosis. However, current methods require prior knowledge of cycle-frequencies or other statistical information, which constrains their applicability across various scenarios. In this paper, we introduce a novel dual adaptive filtering method, incorporating cycle-frequency estimation to solve the existing problem. The method firstly employs an adaptive line enhancer (ALE) to isolate the first-order cyclostationary signal, thereby the cycle-frequencies can be effectively detected using an exhaustive estimation technique. Subsequently, an adaptive frequency-shift (FRESH) filter is further applied to extract the second-order cyclostationary features from the residual components. The proposed method successfully overcomes the challenge of separating cyclostationary signals without prior knowledge and can be tailored to real-time application scenarios. Besides, the approach distinguishes between the two cyclostationary signal types, effectively resolving any aliasing concerns inherent in their statistical characteristics. The effectiveness of the method is verified through simulation, experiments, and engineering data analysis. It is demonstrated that the method significantly enhances diagnostic accuracy and is more suitable for early fault diagnosis of rolling bearings by estimating spectral coherence on the extracted signals.

Adaptive Line Enhancer Based on Maximum Correntropy Criterion and Frequency Domain Sparsity for Passive Sonars

The low-frequency narrowband components (known as lines) in the radiated noise of underwater acoustic targets are an important feature of passive sonar detection. Conventional adaptive line enhancer (ALE) based on the least mean square algorithm has limited performance under colored background noise and low signal-to-noise ratio (SNR). In this paper, by combining the frequency domain sparse model of lines and maximum correntropy criterion (MCC), a β-adaptive l0-MCC-ALE is proposed to solve the above-mentioned problem. The proposed ALE uses a sparse-driven MCC algorithm to update the weight vector in the frequency domain to further suppress the colored background noise. For the problem that the value of parameter β is sensitive to the performance, β is updated adaptively according to the frequency response of ALE in each iteration. Simulation and real data processing results show that the proposed ALE is insensitive to the given parameter β and has excellent performance for line enhancement. Compared with conventional ALE, the SNR of lines can be improved by 7~8 dB.

Least Mean p-Power-Based Sparsity-Driven Adaptive Line Enhancer for Passive Sonars Amid Under-Ice Noise

In order to detect weak underwater tonals, adaptive line enhancers (ALEs) have been widely applied in passive sonars. Unfortunately, conventional ALEs cannot perform well amid impulse noise generated by ice cracking, snapping shrimp or other factors. This kind of noise has a different noise model compared to Gaussian noise and leads to noise model mismatch problems in conventional ALEs. To mitigate the performance degradation of conventional ALEs in under-ice impulse noise, in this study, a modified ALE is proposed for passive sonars. The proposed ALE is based on the least mean p-power (LMP) error criterion and the prior information of the frequency domain sparsity to improve the enhancement performance under impulse noise. The signal-to-noise ratio (SNR) gain is chosen as the metric for evaluating the proposed ALE. The simulation results show that the output SNR gain of the proposed ALE was, respectively, 9.3 and 2.6 dB higher than that of the sparsity-based ALE (SALE) and the least mean p-power ALE (PALE) when the input GSNR was −12 dB. The results of processing the under-ice noise data also demonstrate that the proposed ALE is distinguished among the four ALEs.

Wind-noise reduction for infrasonic measurements using adaptive line enhancement.

Measurement quality and analysis capability of infrasonic signals are both affected by background wind-noise. Physical filters, i.e., barriers and pipe arrays, are traditionally employed to reduce such noise. However, limited efficacy, site dependence, cost, requirement of space, non-portability, and frequent maintenance are some of their major drawbacks. This work proposes an adaptive filtering-based adaptive line enhancer (ALE) noise cancellation scheme as an alternative. Two infrasonic sensors (Chaparral Physics 50A), are adjacently deployed. One sensor is fitted with a conventional four-armed non-porous hose array (physical filter), while the ALE scheme is applied to the second sensor, sans physical filter. In high wind-noise conditions, the ALE scheme seems to behave as a lowpass filter (cutoff at 0.2 Hz), with a maximum attenuation of 26 dB at 8 Hz, while the physical filter provides significant attenuation only above 4 Hz with a maximum attenuation of 17 dB at 8 Hz. Generally, at other frequencies, the ALE scheme provides up to 20 dB superior noise attenuation as compared to the physical filter. The ALE also provides up to 6 dB gain in the signal-to-noise ratio as compared to the physical filter, due to non-attenuation of the infrasonic signal.

Deep‐learning‐based line enhancer for passive sonar systems

Detection of acoustic tonals from surfaces and underwater vehicles is important for passive sonar systems. Enhancements of the tonals are usually necessary in passive sonar prior to detections. Conventionally, passive sonars employ adaptive line enhancers (ALE) in order to realise enhancements of the tonals. However, ALEs have requirements on their input signal-to-noise ratios (SNR). When the SNR inputs are too low, the ALEs cannot perform well. Therefore, for the purpose of overcoming the limitations of the SNR inputs to ALEs, this study proposes to enhance tonals using unsupervised deep-learning techniques. The proposed deep-learning-based line enhancer (DLE) is based on an autoencoder neural network. The simulation results show that when the input SNR is −28 dB, the proposed DLE still achieves an SNR gain of 15 dB. However, the reference ALE fails. The experimental results also demonstrate the superiority of the proposed DLE.

Adaptive line enhancer for nonstationary harmonic noise reduction

In this paper, we propose a frequency-domain adaptive line enhancer (ALE) to reduce nonstationary harmonic noise, such as medical equipment beeps, from a noisy speech signal captured by a single microphone. The reduction of nonstationary noise is very challenging, with the tradeoff between noise reduction and speech distortion, often resulting with much noise residuals. The proposed ALE is a combination of the commonly-used forward adaptive linear filter and a non-causal backward adaptive linear filter used together with an indicator for the presence of transient noise. The proposed combined filter results in less noise residuals while preserving the speech components. We compare the proposed approach to conventional and recent methods, and show that it can outperform these methods, achieving lower distortion, more noise reduction, and overall better speech quality and intelligibility.

Application of adaptive line enhancer based on NLMS algorithm in shaft-rate electric field signal detection

There is lots of useful signal feature in ship’s shaft-rate electric field. Extracting the signal feature is vital in underwater target recognition. In order to extract the feature of the shaft-rate electric field, the shaft-rate electric field is processed by ALE based on NLMS. The improved NLMS algorithm is proposed to construct an adaptive line-spectrum enhancer, and then the measured data of shaft-rate electric field is processed by ALE. The results show that the algorithm can effectively separate the weak shaft frequency electric field signal from the broadband background noise at low SNR. Compared with the ordinary ALE, the algorithm has more significant effect in improving SNR, faster convergence speed and smaller steady-state error, which greatly improves the detection ability of ship shaft frequency electric field.

Effect of adaptive line enhancement filters on noise cancellation in ECG signals

Power line interference is the main noise source that contaminates Electrocardiogram (ECG) signals and measurements. In recent years, adaptive filters with different approaches have been investigated to eliminate power line interference in ECG waveforms. Adaptive line enhancement filter is a special type of adaptive filter that, unlike other adaptive filters, does not require a reference signal and has potential application in ECG signal filtering. In this paper, a selflearning filter based on an adaptive line enhancement (ALE) filter is proposed to remove power line interference in ECG signals. We simulate the adaptive filter in MATLwith a noisy ECG signal and analyze the performance of algorithms in terms of signal-to-noise ratio (SNR) improvement. The proposed algorithm is validated with Physikalisch-Technische Bundesanstalt (PTB) ECG signals database. Additive white gaussian noise is added to the raw ECG signal. Influential parameters on the ALE filter performance such as filter delay, the convergence factor, and the filter length are analyzed and discussed.

An adaptive extraction method for rail crack acoustic emission signal under strong wheel-rail rolling noise of high-speed railway

Aiming to detect the weak rail crack signal under strong Wheel-rail Rolling Noise (WRRN) in high-speed railway by Acoustic Emission (AE) technology, a Hurst exponent-improved Adaptive Line Enhancer (ALE) is put forward. The Hurst exponent is adopted to describe the irregularity and fractality property, and is introduced into the cost function of ALE through its power-law relation with the structure function of the fractional Brownian motion so that the optimal objective of the adaptive filter is improved to adapt to the rail crack signal. Compared with the ALE and the Shannon entropy-improved ALE, the proposed method has the best performance. The Hurst exponent-improved ALE not only suppresses the strong WRRN in high-speed condition, but also enhances the rail crack signal and the signal to noise ratio to a large extent. Moreover, with the entire frequency components of the desired signal reserved, the method has the benefits of low computational cost and simple implementation. The research offers a method for extraction of the weak rail crack signal, solves the WRRN problem, and makes AE technology applicable in high-speed rail defect detection.

Sparsity-inducing frequency-domain adaptive line enhancer for unmanned underwater vehicle sonar

The radiated lines from underwater targets are a significant feature for passive sonar detection. Since the spatial and time processing gains are limited in an unmanned underwater vehicle (UUV) sonar, an adaptive line enhancer (ALE) is usually employed as a preprocessing step to enhance the signal-to-noise ratio (SNR) of the lines. However, the original time-domain ALE based on the least mean square (LMS) algorithm suffers from the weight noise in the adaptation, which limits the SNR gain severely. Inspired by the frequency-domain sparsity of the lines, a sparsity-inducing ALE is developed to break through this limitation. The proposed ALE is implemented in the frequency domain and a reweighted L1-norm sparse penalty is incorporated into the cost function of the adaptation. By means of the sparse penalty, a zero-attraction term is added in the adaptation, which results in the suppression of the weight noise. The SNR gain is thus enhanced. In the proposed ALE, the sliding discrete Fourier transform (SDFT) technique is utilized to transfer the time-domain input samples to the frequency domain, and the computational complexity of the proposed ALE is thus comparable to that of the original one, which meets the energy efficiency requirement of UUV. Simulation results demonstrate that the SNR gain of the proposed ALE is over 9 dB higher than that of the original ALE with an input SNR of −25 dB. Experimental data processing also verifies the superiority of the proposed ALE.

Adaptive Estimation and Reduction of Noises Affecting a Self-Mixing Interferometric Laser Sensor

Experimental Self-Mixing (SM) or optical feedback interferometric signals are usually affected by additive white Gaussian noise (AWGN) and impulsive noise. Depending on SM sensing set-up, these noises can significantly reduce the signal to noise ratio (SNR) of SM signals which in turn affects the measurement performance of signal processing algorithms employed for metric information retrieval. In this paper, adaptive line enhancement (ALE) technique is proposed to remove AWGN and impulsive noise from SM signals. Specifically, a recursive least squares (RLS) based ALE algorithm has been designed and the results have been compared with established methods such as high-order digital low-pass filtering and discrete wavelet transform. The comparison indicates better precision in case of use of RLS-ALE even when significant variations occur in the operating optical feedback regime and remote target velocity as well as in presence of speckle. The proposed algorithm can also estimate the SNR of SM signals belonging to weak-, moderate-, and strong-optical feedback regime with SNR ranging from 0 dB to 40 dB, with a mean absolute error of 1.35 dB and a 1.09 dB precision. Statistical analysis of noise recovered from different experimental SM signals attests the Gaussian- and impulsive-nature of noise. Thus, the proposed method also enables a simple and reliable quantitative analysis and comparison of different laser diode based SM laser sensors operating under variable optical conditions.

Spectral trimming technique: a new approach for suppressing motion artefacts in stress electrocardiography

Motion artefacts in electrocardiographic (ECG) signal are suppressed mainly by adaptive noise cancellation and wavelet denoising. While the former requires a motion sensor in addition to ECG electrodes, the latter removes some of the desired low-frequency components in the signal. In this paper spectral trimming technique is being introduced for suppressing the motion artefacts in stress electrocardiography. In this method, Fourier spectral coefficients up to 1.221 Hz of noisy signal are trimmed on the basis of template derived from resting ECG signal in the same subject. The proposed spectral trimming technique has yielded the lowest value of mean ± standard deviation for root mean square error (18.92 ± 8.71) and highest value of the signal to noise ratio (6.439 ± 4.266) as compared to other three methods, namely adaptive noise cancellation, wavelet decomposition and adaptive line enhancement with compatible value of correlation coefficient. Subsequently, spectral trimming technique has been implemented in real-time (deferred by 8.2 s) application for stress electrocardiography. Spectral trimming technique thus offers a method of choice for motion artefact suppression in offline as well as deferred online applications. This method takes care of the limitations of conventional methods such as adaptive noise cancellation or wavelet denoising for suppressing motion artefacts in stress electrocardiography.

Implementation of Neural Network with ALE for the Removal of Artifacts in EEG Signals

Objective: The EEG signal extraction offers an opportunity to improve the quality of life in patients, which has lost to control the ability of their body, with impairment of locomotion. Electroencephalogram (EEG) signal is an important information source for underlying brain processes. Materials and Methods: The signal extraction and denoising technique obtained through timedomain was then processed by Adaptive Line Enhancer (ALE) to extract the signal coefficient and classify the EEG signals based on FF network. The adaptive line enhancer is used to update the coefficient during the runtime with the help of adaptive algorithms (LMS, RLS, Kalman Filter). Results: In this work, the least mean square algorithm was employed to obtain the coefficient update with respect to the corresponding input signal. Finally, Mat lab and verilog HDL language are used to simulate the signals and got the classification accuracy rate of 80%. Conclusion: Experiments show that this method can get high and accurate rate of classification. In this paper, it is proposed that a low-cost use of Field Programmable Gate Arrays (FPGAs) can be used to process EEG signals for extracting and denoising. As a preliminary study, this work shows the implementation of a Neural Network, integrated with ALE for EEG signal processing. The preliminary tests through the proposed architecture for the activation function shows to be reasonable both in terms of precision and in processing speed.

Adaptive Intrawell Matched Stochastic Resonance with a Potential Constraint Aided Line Enhancer for Passive Sonars.

Remote passive sonar detection and classification are challenging problems that require the user to extract signatures under low signal-to-noise (SNR) ratio conditions. Adaptive line enhancers (ALEs) have been widely utilized in passive sonars for enhancing narrowband discrete components, but the performance is limited. In this paper, we propose an adaptive intrawell matched stochastic resonance (AIMSR) method, aiming to break through the limitation of the conventional ALE by nonlinear filtering effects. To make it practically applicable, we addressed two problems: (1) the parameterized implementation of stochastic resonance (SR) under the low sampling rate condition and (2) the feasibility of realization in an embedded system with low computational complexity. For the first problem, the framework of intrawell matched stochastic resonance with potential constraint is implemented with three distinct merits: (a) it can ease the insufficient time-scale matching constraint so as to weaken the uncertain affect on potential parameter tuning; (b) the inaccurate noise intensity estimation can be eased; (c) it can release the limitation on system response which allows a higher input frequency in breaking through the large sampling rate limitation. For the second problem, we assumed a particular case to ease the potential parameter . As a result, the computation complexity is greatly reduced, and the extremely large parameter limitation is relaxed simultaneously. Simulation analyses are conducted with a discrete line signature and harmonic related line signature that reflect the superior filtering performance with limited sampling rate conditions; without loss of generality of detection, we considered two circumstances corresponding to (periodic signal with noise) and (pure noise) hypotheses, respectively, which indicates the detection performance fairly well. Application verification was experimentally conducted in a reservoir with an autonomous underwater vehicle (AUV) to validate the feasibility and efficiency of the proposed method. The results indicate that the proposed method surpasses the conventional ALE method in lower frequency contexts, where there is about 10 dB improvement for the fundamental frequency in the sense of power spectrum density (PSD).

Neural network-based design and evaluation of performance metrics using adaptive line enhancer with adaptive algorithms for auscultation analysis

Auscultation is an important key for the physical (respiratory and circulatory) examination and is helpful in diagnosing various disorders. Auscultation is performed for the purposes of examining the circulatory and respiratory sounds and gastrointestinal system (bowel sounds). Besides inconsistencies in the propagation of the normal sounds, there are also several types of specific irregularities that can be heard in respiratory sounds: commonly known abnormal sounds in lung sound (wheezes, crackles, stridor, squawks, rhonchi and crackles) and heart sounds (heart murmurs). However, detection of abnormal sounds during auscultation needs extensive training and experience. Real-time separation of these heart sound signals from the lung sound signals is of great research interest and difficult to achieve. In this work, the authors proposed a novel adaptive line enhancer using nonlinear ANN design used for auscultation analysis. Our proposed designs are trained using different networks training algorithms which resulted in better mean square error reduction within compact time.

Sparsity-driven adaptive enhancement of underwater acoustic tonals for passive sonars.

Acoustic tonals, radiated by underwater and surface vehicles, are an important feature for passive sonar detection. An adaptive line enhancer (ALE) is usually employed in passive sonar systems as a preprocessing step to enhance the acoustic tonals from these vehicles. Unfortunately, the performance of the conventional ALE is limited by the high steady-state misadjustment, which is caused by the weight noise in the adaptation process. This paper makes use of the frequency-domain sparsity of these tonals to develop better ALEs for passive sonars. The adaptation of the proposed ALE is performed in the frequency domain. Three typical sparse penalties, l1-norm, log-sum, and l0-pseudo-norm, are incorporated into the cost function of the frequency-domain adaptation, which yield three sparsity-driven ALEs: zero-attracting (ZA), reweighted zero-attracting (RZA), and l0. The simulation shows that the signal-to-noise ratio gains of the ZA-ALE, RZA-ALE, and l0-ALE are 5.9, 8.7, and 9.7 dB, higher than that of the conventional ALE, respectively. The results of processing the real data also validate that all the sparsity-driven ALEs outperform the conventional ALE, and the l0-ALE performs the best. The proposed sparsity-driven l0-ALE is thus a promising candidate for passive sonars to enhance the tonals.

Efficient Narrowband Noise Cancellation System Using Adaptive Line Enhancer

Rotating machines such as motors, generate noise at the fundamental frequency and its harmonics. The narrowband active noise control (NANC) algorithm is widely used to cancel such noise. Traditional feedforward NANC systems use non-acoustic sensors to measure rotation speeds, and then a bank of signal generators produce synchronized tonal signals as the reference signals according to the fundamental frequency of the undesired noise. However, the non-acoustic sensors such as encoders usually cost a lot and are not reliable. This study proposes using the feedback ANC structure and incorporates with several adaptive line enhancers (ALEs) to generate the reference signals. Without using non-acoustic sensors, the proposed system only updates the center frequency of the first ALE to track the fundamental frequency change, which greatly reduces complexity of the proposed ANC system. The frequency mismatch (FM) problem is overcome by properly setting the bandwidths of the ALE. Performance were verified by using both simulations and real-time experiments.

Detect Black Box Signals with Enhanced Spectrum and Support Vector Classifier

When the plane crash into the sea and the search and rescue team search the location black box, we need to detect a specific acoustic signal in the ocean. The underwater acoustic channel has several distinct features: multipath propagation, high attenuation, and sound velocity varying as a function of depth and temperature, which make the detection of underwater signals very difficult. This paper proposes a new method in the marine signal detection and discrimination based on adaptive line enhancer and support vector machine classifier, which can provide a new idea for marine black box search. The adaptive line enhancer of the least mean square algorithm can detect narrowband signals hidden in wideband noise, while the support vector machine classifier maps signals to high-dimensional space and screens out hyperplanes separating different signals from machine data through previous data. Experimental data show that this method can bring a very high recognition rate.