Real-time Noise Research Articles

Kalman filtering to real-time trace water level measurements using ultrasonic sensor

Water level monitoring systems are needed in various related fields. However, to accurately determine water levels typically difficult due to the effect like sloshing in dynamic environments. In this paper, we proposed Kalman filter based approach to reduce uncertainty in the water level measurements. Experiments have been performed by filling the tank with water and installing an ultrasonic sensor module at the top center of the tank. The water tank is vibrated with an artificial vibrator, then measuring the water level is done by the ultrasonic sensor. To reduce measurement errors we introduced the Kalman filtering. This technique is developed for improving the precision of water level measurements in the presence of various noise sources. The Kalman filter performance is demonstrated to be adaptive to real-time noise. Experiment results indicate reducing the errors of measurement significantly up to about 60% for a dynamic water condition by adjusting appropriately the Kalman filter tuning parameters.

An Adaptive Energy Detection Scheme with Real-Time Noise Variance Estimation

Energy detection-based spectrum sensing techniques are ideally suited for power-constrained cognitive radio applications because of their lower computational complexity compared to feature detection techniques. However, their detection performance is dependent on multiple factors like accuracy of noise variance estimation and signal-to-noise ratio (SNR). Many variations of energy detection techniques have been proposed to address these challenges; however, they achieve the desired detection accuracy at the cost of increased computational complexity. This restricts the use of enhanced energy detection schemes in power-constrained applications such as aeronautical communication. In this paper, an adaptive low-complexity energy detection scheme is proposed for spectrum sensing in an L-band Digital Aeronautical Communication System (LDACS) at lower SNR levels. Our scheme uses a real-time noise variance estimation technique using autocorrelation which is induced by the cyclic prefix property in LDACS signals. The proposed technique does not incur dedicated hardware blocks for noise variance estimation, leading to an efficient hardware implementation of the scheme without significant resource overheads. The simulation studies of the proposed scheme show that the desired accuracy (90% detection accuracy with only 10% of false alarms) can be achieved even at $$-16.5$$ dB SNR, significantly lowering the SNR wall over existing energy detection schemes.

Video coding technique with a parametric modelling of noise

This paper presents a video encoding method in which noise is encoded using a novel parametric model representing spectral envelope and spatial distribution of energy. The proposed method has been experimentally assessed using video test sequences in a practical setup consisting of a simple, real-time noise reduction technique and High Efficiency Video Codec (HEVC). The attained results show that the use of the proposed parametric modelling of noise can improve the subjective quality of reconstructed video by approximately 1.8 Mean Opinion Scope (MOS) points (in 11-point scale) related to the classical video coding. Moreover, the present work confirms results attained in the previous works that the usage of even sole noise reduction prior to the encoding provides quality increase.

Temporal characteristics of triple-frequency GNSS scintillation during a visible aurora borealis event over the Faroe Islands amid a period of very low solar activity

Ionospheric scintillation causes noise in GNSS measurements and hence affects the resulting coordinates or even the ability to track data. We investigate the characteristics of GNSS scintillation during a visible aurora borealis event over the Faroe Islands during October 7, 2018, which lasted for around 30 min. Data of 1 Hz rate from a typical geodetic GNSS receiver are analyzed during a 3-h window around the event, as well as during the same period the days before and after. Data from all four GNSS are analyzed, which consist of 31 satellites during the occurrence, resulting in a good overview of the temporal distribution of the scintillation. Two approaches are used, called the time difference code-minus-carrier and the time difference phase ionospheric residual. The results illustrate that there is little effect on the code measurements, but significant repercussion on the carrier phase observables, resulting in erroneous position solutions. The approaches we present can be used in GNSS processing software to detect scintillation noise in real time on individual satellites, allowing such noisy data to be rejected.

An intelligent algorithm for coherent sound source localization based on a strong tracking filter

Background noise is inevitable when sensor arrays are used for aeroacoustic measurements in wind tunnels. The direct removal of background noise, however, would affect the measurement accuracy. In particular, the existing array signal processing algorithms are either invalid or inefficient for removing the noise that is coherent with the signal of interest. In this paper, an intelligent algorithm is developed to localize the coherent sound sources and background noise in real time by iteratively checking the collected data from the array. The proposed method can automatically adjust the suboptimal fading factor to extract useful information as much as possible in residual sequence. This algorithm is tested in simulations and then demonstrated in an experiment. Compared to the two existing methods, the results indicate that the new method has a good phase-shift tracking ability and rapid estimation-error convergence speed, and can achieve an acceptable performance even for low-cost acoustic sensors. Overall, the proposed method should assist array beamforming and hence benefit aeroacoustic measurement.

Fault Diagnosis of a Helical Gearbox Based on an Adaptive Empirical Wavelet Transform in Combination with a Spectral Subtraction Method

Fault characteristic extraction is attracting a great deal of attention from researchers for the fault diagnosis of rotating machinery. Generally, when a gearbox is damaged, accurate identification of the side-band features can be used to detect the condition of the machinery equipment to reduce financial losses. However, the side-band feature of damaged gears that are constantly disturbed by strong jamming is embedded in the background noise. In this paper, a hybrid signal-processing method is proposed based on a spectral subtraction (SS) denoising algorithm combined with an empirical wavelet transform (EWT) to extract the side-band feature of gear faults. Firstly, SS is used to estimate the real-time noise information, which is used to enhance the fault signal of the helical gearbox from a vibration signal with strong noise disturbance. The empirical wavelet transform can extract amplitude-modulated/frequency-modulated (AM-FM) components of a signal using different filter bands that are designed in accordance with the signal properties. The fault signal is obtained by building a flexible gear for a helical gearbox with ADAMS software. The experiment shows the feasibility and availability of the multi-body dynamics model. The spectral subtraction-based adaptive empirical wavelet transform (SS-AEWT) method was applied to estimate the gear side-band feature for different tooth breakages and the strong background noise. The verification results show that the proposed method gives a clearer indication of gear fault characteristics with different tooth breakages and the different signal-noise ratio (SNR) than the conventional EMD and LMD methods. Finally, the fault characteristic frequency of a damaged gear suggests that the proposed SS-AEWT method can accurately and reliably diagnose faults of a gearbox.

Underwater Acoustic Channel Denoising Algorithm Based on Least Mean Square Algorithm

In order to decrease the high transmission error rate caused by background noise and impulse noise during underwater acoustic communication, this paper considers the influence of noise during the communication process based on the frequency division multiplexing system model. A filter is carried out to estimate the real-time noise, which is implemented by the least mean square error algorithm and is improved according to the characteristics of short duration and high energy of impulse noise. Finally, the effectiveness of the algorithm is verified by computer simulation.

A Robust Localization Method for Unmanned Surface Vehicle (USV) Navigation Using Fuzzy Adaptive Kalman Filtering

Recently, multi-sensor navigation has emerged as a viable approach in autonomous vehicles’ development. Kalman filtering has been widely applied in multi-sensor data fusion, and researchers are trialing variants of the Kalman Filter (KF) to improve the operational robustness of vehicles in a range of environments under varying dynamic constraints. This paper proposes a novel sensor data fusion algorithm employing an Unscented Kalman Filter (UKF) for the autonomous navigation of an Unmanned Surface Vehicle (USV). Since the navigational sensors on-board the USV are subject to operational uncertainties caused by equipment limitations and environmental disturbances, an improved UKF algorithm with the capability of adaptive estimation, namely fuzzy adaptive UKF data fusion algorithm, has been proposed to obtain reliable navigational information. The conventional UKF is capable of fusing a number of raw sensor measurements and generating relatively accurate estimations with proper a priori knowledge of system noise. To deal with systems that lack such information, a fuzzy adaptive estimation method is introduced to enhance the performance of the conventional UKF, making the algorithm capable of verifying and correcting the associated sensor noise in real time. The proposed fuzzy adaptive UKF data fusion algorithm has been tested and evaluated in different simulations modeled using practical maritime environments and the results are compared with the conventional UKF. The sensor measurements taken from a practical USV trial have also been applied to the proposed algorithm for further validation.

Development of the real-time adaptive active noise control system based on SAN algorithm

Development of the real-time adaptive active noise control system based on SAN algorithm

线性扫频激光器实时动态相频噪声特性研究

线性扫频激光器实时动态相频噪声特性研究

A Coarse Alignment Based on the Sliding Fixed-Interval Least Squares Denoising Method

The observation vectors in traditional coarse alignment contain random noise caused by the errors of inertial instruments, which will slow down the convergence rate. To solve the above problem, a real-time noise reduction method, sliding fixed-interval least squares (SFI-LS), is devised to depress the noise in the observation vectors. In this paper, the least square method, improved by a sliding fixed-interval approach, is applied for the real-time noise reduction. In order to achieve a better-performed coarse alignment, the proposed method is utilized to de-noise the random noise in observation vectors. First, the principles of proposed SFI-LS algorithm and coarse alignment are devised. A simulation test and turntable experiment were executed to demonstrate the availability of the designed method. It is indicated that, from the results of the simulation and turntable tests, the designed algorithm can effectively reduce the random noise in observation vectors. Therefore, the proposed method can enhance the performance of coarse alignment availably.

Vector Tracking Algorithm Based on Adaptive Cubature Kalman Filter

In the vector tracking loop, there is a great error in the output of discriminator owing to the disturbance of noise. Cubature Kalman filter is proposed to replace the discriminator to process I/Q data and generate code phase error and the carrier frequency error in this paper. The present algorithm not only can avoid the nonlinear problem of discriminator, but also can reduce the bad effect of noise. Moreover, using cubature Kalman filter to deal with the nonlinear I/Q data is beneficial to preserve the accuracy of data processing. Because noise is unknown or time-varying, the filter should have the ability to respond to the changes of environmental noise. The innovation of measurements is used to estimate the covariance matrix of measurement noise in real time. Finally, a comparison is carried out between the present algorithm and the vector tracking algorithm based on discriminator. The test results show that the code phase error and the carrier frequency error are smaller, and the accuracy of navigation solution is also higher.

Development of Earthquake Event Detection Technique Based on STA/LTA Algorithm for Seismic Alert System

Among natural disasters, earthquake is the most common calamity which result in a wide spectrum of destruction, loses of property and human life. In order to mitigate the damage impact, there is an urgent need to realize a technological solution for vital installations i.e. Seismic alert system (SAS). It is a solution to avert colossal loss as earthquake forecasting is not yet possible and is being devised for regional notification for a possible mitigation while it is in active mode. For designing SAS, a network of seismic sensing node (SSN) is to be configured to detect the seismic activity in the region of interest. SSN discriminates between local noise and seismic event in real time using inbuilt event detection technique. The true seismic event is declared on satisfying the field parameters programmed by the user i.e. short and long duration window, threshold ratio, stability factor, de-threshold ratio, number of threshold channels, sampling rate, pre-event and post-event duration, number of channel and packet size duration. This paper illustrates the design aspects related to the event detection technique incorporated in the seismic node of the alert system and configured around the seismic sensors, 24-bit high resolution digitizers, GPS modules, etc. The results are validated by varying the different field parameters on recorded signal.

Comparison and Noise Suppression of the Transmitted and Reflected Photoplethysmography Signals.

The photoplethysmography (PPG) is inevitably corrupted by many kinds of noise no matter whether its acquisition mode is transmittance or reflectance. To enhance the quality of PPG signals, many studies have made great progress in PPG denoising by adding extra sensors and developing complex algorithms. Considering the reasonable cost, compact size, and real-time and easy implementation, this study proposed a simple real-time denoising method based on double median filters which can be integrated in microcontroller of commercial or portable pulse oximeters without adding extra hardware. First, we used the boundary extension to preserve the signal boundary distortion and designed a first median filter with the time window at approximately 78 ms to eliminate the high-frequency components of the signal. Then, through the second median filter with a time window which was about 780 ms, we estimated the low-frequency components. Finally, we removed the estimated low-frequency components from the signal to obtain the denoised signal. Through comparing the multiple sets of signals under calmly sitting and slightly moving postures, the PPG signals contained noises no matter whether collected by the transmittance-mode or the reflectance-mode. To evaluate the proposed method, we conducted measured, simulated experiments and a strong noisy environment experiment. Through comparing the morphology distortions, frequency spectra, and the signal-to-noise ratios (SNRs), the results showed that the proposed method can suppress noise effectively and preserve the essential morphological features from PPG signals. As a result, the proposed method can enhance the quality of PPG signals and, thus, can contribute to the improvement of the calculation accuracy of the subsequent physiological parameters. In addition, the proposed method could be a good choice to address the real-time noise reduction of portable PPG measuring instruments.

Inner Ear Damage by Firecracker Trauma

Firecrackers are still popular among the general public of various populations worldwide. This study investigated inner ear damage in patients with firecracker trauma and analyzed noise levels in 6 kinds of commercially available firecrackers. During the past 20 years, we have experienced 30 patients with firecracker trauma. An inner ear test battery comprising audiometry, cervical and ocular vestibular-evoked myogenic potential (cVEMP and oVEMP) tests and a caloric test was performed. The real-time noise levels were measured outdoors at a distance of 2, 4 and 6 m away from a lighting firecracker to mimic a noise event. Mean hearing levels at high frequencies (4,000 and 8,000 Hz) were significantly higher than those at the low and middle frequencies, indicating that firecrackers mostly cause high-tone hearing loss. For the vestibular damage, abnormality percentages were higher in the results of cVEMP (80%) and oVEMP (60%) tests, but not in the caloric test (8%). In conclusion, most firecrackers exhibited noise levels > 110 dB SPL even at a distance of 6 m. This blast injury simultaneously damaged the cochlea, saccule and utricle, but spared the semicircular canals, indicating that blast exposure potentiates the adverse effect of noise exposure on both cochlear and vestibular partitions.

A proposal of the acoustic climate index for assess traffic impact in a crossroad area

The article presents a proposal for the definition of the acoustic climate quality index. The index is a measure to assess the negative impact of road traffic on the acoustic climate in the immediate vicinity of the road infrastructure. This measure will be useful in systems whose task is to assess the impact of road transport in short periods of time. The index was used to assess the acoustic climate in the crossroad area monitored due to traffic noise in near real time.

Audible noise sources (or characteristics?) from wind turbines using acoustic beam-forming

In many investigations of wind turbine noise, residents report specific and varying types of audible characteristics. With respect to the subject audible characteristics reported by residents (which are not picked in the dBA Leq measurements) turbines exhibit multiple audible sources at different operating and environmental conditions. Imaging techniques for real-time noise source identification have been used to identify tonal as well as dynamic pulsation during various environmental conditions and distances. Challenges with these techniques are presented using advanced imaging algorithms with successful imaging.

Enhancement of chaotic hydrological time series prediction with real-time noise reduction using Extended Kalman Filter

Short-term prediction of hydrological time series using chaotic dynamical systems approach has gained popularity. However, noise can severely affect the prediction accuracy of any data driven modelling approach. Noise reduction processes attempted in chaotic hydrological time series analysis were later questioned by Elshorbagy et al. (2002) for their appropriateness for noise reduction processes. Only a few studies on noise reduction have appeared since then, and these too were limited to introducing new noise reduction methods.Most of the past studies on chaotic time series noise reduction shared a general perception that the noise reduced data trained models can give better predictions; they used off line noise reduction methods to obtain a ‘noise reduced data set’ which was then used to make prediction models. However, the results of this study showed that this was not necessarily true and often flawed, when the input data was noisier. Instead, noise-reduced data inputs, were shown to yield higher prediction accuracy even with noisy data trained models. Thus, the study showed a real need to use real-time noise reduction methods instead of off-line noise reduction methods that had previously been used.The Extended Kalman Filter (EKF), a popular nonlinear state estimation method from controls literature, was introduced as a real-time noise reduction method and its effectiveness was demonstrated on both synthetic chaotic time series and real river flow time series. EKF produced prediction improvement as high as 15%–40% on the benchmark time series with noise levels varying from 1% to 30%. Two river flow series, with low average flows, showed prediction improvement whereas three other flow series, with relatively large average flows, did not. Artificial Neural Network (ANN) models were used as the state-space models in EKF, and adopting them to time delays different from 1 unit was also demonstrated. The study demonstrated an ‘indirect’ validation method to verify the effectiveness of noise reduction when several interrelated time series were available; this was supported in observed discharge time series of the Ciliwung River in Jakarta, Indonesia.

Noise cancellation for a whole-head magnetometer-based MEG system in hospital environment

We describe a strategy of removing magnetic field interference for a whole-head pediatric magnetoencephalography (MEG) system (‘babyMEG’) installed in a hospital. The 375-channel sensor array of babyMEG consists entirely of magnetometers in two layers to maximize the sensitivity for detecting MEG signals from infants, toddlers, and young children. It is equipped with a continuously operating closed-cycle helium recycler to reduce the operating costs. These two features pose special challenges for noise cancellation. Our strategy uses a combination of several methods. The system is installed in a light-weight, magnetically shielded room (MSR) equipped with an active external shielding. In addition we employ two software-based techniques—a signal space projection (SSP) technique and a synthetic gradiometer (SG) method—for removing the environmental magnetic noise in real time and displaying the output online. The shielding effects are: passive shielding–36 dB, active shielding–12 dB, SSP–40 dB and SG–40 dB, for a combined maximum shielding of about 90 dB at 0.1 Hz. We evaluated the performance of the babyMEG after applying the noise cancellation techniques. The dipole localization errors were <3 mm after averaging 50 epochs with empty room noise in a simulation study for dipoles >10 nAm, which is in the low range of empirically observed dipole moments. In a phantom study with realistic environmental noise, we could clearly recover an evoked cortical magnetic field produced by a 20 nAm dipole after averaging 50 epochs. The localization error was ∼6 mm after averaging 20 epochs. In infants, we could clearly detect a somatic evoked field after averaging ∼20 responses. The unique two-layer sensor design combined with the SSP or SG provides effective noise suppression for a magnetometer-based pediatric MEG system in hospital environment with the closed-cycle helium recycler operating continuously during MEG measurements.

Investigation of a DC OCT signal processing method based on an improved Kalman algorithm

In this paper, we propose an improved Kalman filtering algorithm, to solve problems associated with processing of direct current (DC) signals in optical current transformers (OCTs). The algorithm was realized as follows. Based on the characteristics of the OCT, mixed noise was included with the parameters and state information of the measurement model, to ensure that it could be processed. State predictions and update equations for the improved Kalman algorithm were obtained, according to the minimum mean squared error criterion, using the established model. Finally, real-time noise estimation and correction was achieved by designing a recursive noise filter. Results from simulations and experiments demonstrate that the improved signal processing algorithm is effective in filtering mixed noise from DC signals measured by an OCT. Furthermore, the proposed algorithm is able to achieve real-time noise correction during the filtering process.