Cancellation Filter Research Articles

Nonlinearity mitigation of intensity modulation and coherent detection systems.

Coherent optical communication systems are still too expensive for metro and short reach networks in the near future. Coherent detection with an intensity modulator replacing a costly dual-polarization in-phase and quadrature modulator could offer a good trade-off between cost and performance for metro networks. The major performance limit of such systems is the nonlinearity. To the best of our knowledge, this Letter offers the first known investigation on nonlinearity of a high speed intensity modulation and coherent detection system using an integrated laser and electro-absorption modulator. Advanced nonlinearity mitigation digital signal processing (DSP) are proposed including a 2×1 multiple-input single-output complex transversal Volterra feed-forward equalizer (FFE) filter, a first proposed post-FFE noise cancellation filter taking into account both pre- and post-cursor noise correlations based on a simple autocorrelation coefficient calculation, and a maximum likelihood sequence estimator. The receiver DSP needs no carrier and phase recovery that is required by a conventional coherent receiver DSP. Results show that, compared to a simple linear FFE equalization, the proposed nonlinear DSP enables over 8 and 4dB improvement in optical signal to noise ratio (OSNR) sensitivity at 7% overhead hard-decision feed-forward error correction (FEC) and 20% overhead soft-decision FEC threshold bit error rates (BERs), respectively, for a 56Gb/s intensity modulation and coherent detection system. To the best of our knowledge, a record 18dB OSNR sensitivity at a hard-decision FEC threshold BER of 3.8×10-3 is also achieved.

Heart Rate Estimated from Body Movements at Six Degrees of Freedom by Convolutional Neural Networks.

Cardiac activity has been monitored continuously in daily life by virtue of advanced medical instruments with microelectromechanical system (MEMS) technology. Seismocardiography (SCG) has been considered to be free from the burden of measurement for cardiac activity, but it has been limited in its application in daily life. The most important issues regarding SCG are to overcome the limitations of motion artifacts due to the sensitivity of motion sensor. Although novel adaptive filters for noise cancellation have been developed, they depend on the researcher’s subjective decision. Convolutional neural networks (CNNs) can extract significant features from data automatically without a researcher’s subjective decision, so that signal processing has been recently replaced as CNNs. Thus, this study aimed to develop a novel method to enhance heart rate estimation from thoracic movement by CNNs. Thoracic movement was measured by six-axis accelerometer and gyroscope signals using a wearable sensor that can be worn by simply clipping on clothes. The dataset was collected from 30 participants (15 males, 15 females) using 12 measurement conditions according to two physical conditions (i.e., relaxed and aroused conditions), three body postures (i.e., sitting, standing, and supine), and six movement speeds (i.e., 3.2, 4.5, 5.8, 6.4, 8.5, and 10.3 km/h). The motion data (i.e., six-axis accelerometer and gyroscope) and heart rate (i.e., electrocardiogram (ECG)) were determined as the input data and labels in the dataset, respectively. The CNN model was developed based on VGG Net and optimized by testing according to network depth and data augmentation. The ensemble network of the VGG-16 without data augmentation and the VGG-19 with data augmentation was determined as optimal architecture for generalization. As a result, the proposed method showed higher accuracy than the previous SCG method using signal processing in most measurement conditions. The three main contributions are as follows: (1) the CNN model enhanced heart rate estimation with the benefits of automatic feature extraction from the data; (2) the proposed method was compared with the previous SCG method using signal processing; (3) the method was tested in 12 measurement conditions related to daily motion for a more practical application.

Novel Technique for Comprehensive Noise Identification and Cancellation in GSM Signal

Presence of noise significantly degrades the contents being transmitted over GSM channel. With the evolution of next generation of communication system, the challenges in noise cancellation in voice along with data transmission are not being addressed effectively by existing filters. Therefore, the proposed system offers a mechanism where emphasis is laid on identification of superior and inferior forms of GSM transient signal followed by cancellation of its noise level. Designed using analytical methodology, the proposed system harness the potential of probability theory to perform a modeling that associates allocated power of the transmitting device with the level of noise. The outcome of the study is found to offer a comprehensive identification of different forms of noise and can precisely determine the level of superior and inferior quality of signal. The outcome significantly assists in designing an accurate filter for noise cancellation in GSM signal.

Adaptive Nonlinear RF Cancellation for Improved Isolation in Simultaneous Transmit–Receive Systems

This paper proposes an active radio frequency (RF) cancellation solution to suppress the transmitter (TX) passband leakage signal in radio transceivers supporting simultaneous transmission and reception. The proposed technique is based on creating an opposite-phase baseband equivalent replica of the TX leakage signal in the transceiver digital front-end through adaptive nonlinear filtering of the known transmit data, to facilitate highly accurate cancellation under a nonlinear TX power amplifier (PA). The active RF cancellation is then accomplished by employing an auxiliary transmitter chain, to generate the actual RF cancellation signal, and combining it with the received signal at the receiver (RX) low noise amplifier (LNA) input. A closed-loop parameter learning approach, based on the decorrelation principle, is also developed to efficiently estimate the coefficients of the nonlinear cancellation filter in the presence of a nonlinear TX PA with memory, finite passive isolation, and a nonlinear RX LNA. The performance of the proposed cancellation technique is evaluated through comprehensive RF measurements adopting commercial LTE-Advanced transceiver hardware components. The results show that the proposed technique can provide an additional suppression of up to 54 dB for the TX passband leakage signal at the RX LNA input, even at considerably high transmit power levels and with wide transmission bandwidths. Such novel cancellation solution can therefore substantially improve the TX-RX isolation, hence reducing the requirements on passive isolation and RF component linearity, as well as increasing the efficiency and flexibility of the RF spectrum use in the emerging 5G radio networks.

A hybrid real-time auralization system for binaural reproduction of virtual acoustic environments with simulated room acoustics adapted for hearing aid users

A recently developed binaural real-time reproduction system has been extended with an interface to research hearing aids allowing for the conduction of auditory research on subjects with hearing loss. Simulated hearing aid signals based on measurements of generic hearing aid-related transfer functions are additionally processed on a master hearing aid to emulate conventional hearing aid algorithms and played back through the hearing aid receivers. Designed for subjects with mild to moderate hearing loss, the system also facilitates the use of residual hearing capabilities by simulating an external sound field based on generic head-related transfer functions which is reproduced via loudspeakers and acoustic crosstalk cancellation filters. For increased ecological validity, plausible room acoustics are simulated using adjusted simulation models relying on geometrical acoustics. The proposed system was evaluated objectively on different levels by investigating the listening environment and various system components, running a benchmark analysis on the acoustical simulation and auralization, and measuring the combined system latency.

Adaptive Feedback Cancellation Using a Partitioned-Block Frequency-Domain Kalman Filter Approach With PEM-Based Signal Prewhitening

Adaptive filtering based feedback cancellation is a widespread approach to acoustic feedback control. However, traditional adaptive filtering algorithms have to be modified in order to work satisfactorily in a closed-loop scenario. In particular, the undesired signal correlation between the loudspeaker signal and the source signal in a closed-loop scenario is one of the major problems to address when using adaptive filters for feedback cancellation. Slow convergence speed and limited tracking capabilities are other important limitations to be considered. Additionally, computationally expensive algorithms as well as long delays should be avoided, for instance, in hearing aid applications, because of power constraints, important to extend battery life, and real-time implementations requirements, respectively. We present an algorithm combining good decorrelation properties, by means of the prediction-error method based signal prewhitening, fast convergence, good tracking behavior, and low computational complexity by means of the frequency-domain Kalman filter, and low delay by means of a partitioned-block implementation.

Multiple DSC Filter-Based Three-Phase EPLL for Nonideal Grid Synchronization

This paper originally analyzes the effect of the harmonics, dc offsets, and unbalance on the three-phase enhanced phase-locked loop (3P-EPLL). It proves that 3P-EPLL is not able to accurately estimate the amplitude, phase angle, and frequency of the grid in such cases. In order to correctly estimate the grid synchronous information even under the nonideal grid conditions, an improved 3P-EPLL combined with the multiple delayed signal cancellation (DSC) filters is proposed. A DSC operator is set at the each input of 3P-EPLL to eliminate the input dc offsets and even-order harmonics. And the cascaded DSC (CDSC) module is plugged in both the frequency and amplitude loops to remove the effect of most of the odd-order harmonics and unbalance. The relationship between the delay constant of the DSC operator and the order of the harmonics hoping to eliminate is derived to design the structure of the CDSC module and determine the delay constant of each DSC operator in the CDSC module. Detailed experimental results of the standard 3P-EPLL and proposed one validate the theoretical analysis and the proposed phase-locked loop.

Low-Complexity, Listener's Position-Adaptive Binaural Reproduction Over a Loudspeaker Array

This work presents a method for binaural reproduction over a loudspeaker array that adapts to the listener’s position by using a cross-talk cancellation approach that is updated in real-time. This is obtained by combining the audio signal processing system together with a computer vision mechanism that estimates listener’s position. A novel approach to adapt the cross-talk cancellation filters is introduced here, which employs filters created for a central listening position using a free-field propagation model and far field beamforming techniques, hence requiring little memory and processing. The paper introduces simulations to show the effectiveness and robustness of the formulation together with free-field measurements of performance using a 16 loudspeaker compact array.

Convex combinations of adaptive filters for feedback cancellation in hearing aids

In real-time applications, such as acoustic feedback cancellation in hearing aids, it is desired that the adaptive algorithm converges fast towards a good estimate of the feedback path, while incurring a low steady-state error. A high adaptation step of the least mean square (LMS) algorithm allows for a faster convergence but compromises the steady-state misalignment of the adaptive filter. In this paper, we propose the use of an affine combination of two adaptive filters with different step sizes, but a common combination parameter to break the precision-versus-speed compromise of the adaptive algorithm. Moreover, an improved version of this affine-combination scheme using a different combination parameter for each filter coefficient is also applied for better tracking. Further, a more sophisticated algorithm, which utilizes a convex combination of adaptive filters in a three-filter scheme along with the varying step size (VSS) approach, is introduced for an improved tracking and faster convergence to eliminate the effect of the feedback path. To reduce the estimation bias due to closed-loop signal correlations, the linear prediction-based adaptive feedback cancellation (AFC) design is used instead of a basic adaptive feedback canceller. Simulation results show that the convex combination schemes provide better feedback-cancellation performance than the single-filter VSS algorithm.

On the distributed order PID controller

This paper proposes a generalized fractional controller that covers the complete range of differintegrators of order −1 to order 1. This controller is named distributed order PID controller (DOPID) due to the fact that it broadens the structure of the conventional PID controller. Furthermore, a new method for implementation of distributed order PID controller (DOPID), as well as analysis of its properties in both time and frequency domain are proposed. As DOPID controller represents generalized classical PID controller, it is approximated by a compound fractional controller with multiple fractional differintegrators, with multiple gains, connected in parallel. Orders of these differintegrators have been equally spaced, with the first one being the first order classical integrator, and the last one being the first order classical differentiator. Noise cancellation filter is considered explicitly in the controller’s structure.

Robust SAR STAP via Kronecker decomposition

This paper proposes a spatiotemporal decomposition for the detection of moving targets in multi-antenna synthetic aperture radar (SAR). As a high-resolution radar imaging modality, SAR detects and localizes nonmoving targets accurately, giving it an advantage over lower-resolution ground-moving target indication (GMTI) radars. Moving target detection is more challenging due to target smearing and masking by clutter. Space-time adaptive processing (STAP) is often used to remove the stationary clutter and enhance the moving targets. In this work, it is shown that the performance of STAP can be improved by modeling the clutter covariance as a space versus time Kronecker product with low-rank factors. Based on this model, a low-rank Kronecker product covariance estimation algorithm is proposed, and a novel separable clutter cancelation filter based on the Kronecker covariance estimate is introduced. The proposed method provides orders of magnitude reduction in the required number of training samples as well as improved robustness to corruption of the training data. Simulation results and experiments using the Gotcha SAR GMTI challenge dataset are presented that confirm the advantages of our approach relative to existing techniques.

A Study of Sensitivity of a Least Mean Square Filter on its Tap Weight Vector Length and Step Size in Adaptively Cancelling Noise in an Electrocardiogram Signal

m (ECG) is a procedure that records the activity of the heart and presents it as an electrical signal. It is an immensely important diagnostic tool since any deviation from the characteristic shape of an ECG may point to a cardiac anomaly, making it important for instrument and processing to be very accurate. Since the nature of noise that could corrupt the ECG is non-stationary, Adaptive Noise Cancellation (ANC) filters are required. In this paper, the Adaptive Noise Cancellation algorithm Least Mean Squares (LMS) has been implemented to reduce the noise in a corrupted ECG signal. The parameter sensitivity of the filter on its result had been studied, how the values of tap weight vector length and step size of the filter influence the quality of the resultant signal of the filter. This has been done through simulations in MATLAB, the noise have been simulated in MATLAB and the ECG signals have been collected from the ECG-ID database at PhysioNet. For various values of tap weight vector length and step size the performance of the LMS filter is analyzed in terms of PRD and MSE and the observations are tabulated. At the end of this study parameter values are suggested that render the most optimum results.

A probe-and-update method for tuning analog self-interference canceler in full-duplex radio systems

Accurate self-interference (SI) cancellation is one of the main challenges for the implementation of full-duplex radio (FDR) systems. In this letter, we proposed a new algorithm, called, probe-and-update method (PUM), to tune the gains of a multi-tap analog SI cancellation filter for orthogonal frequency division multiplexing FDR systems. PUM performs a novel gain updating procedure based on the comparison of two consecutive measurements of the residual SI power, namely, the probe value. Via sufficient number of updates, PUM could approach the minimum average residual SI power. PUM is applicable in a multipath environment and no channel knowledge is needed in advance, which considerably facilitate the implementation. Simulation results show that PUM could achieve a reasonably low residual SI power down to 5.5 dB of the noise floor in a 20-MHz system.

In-band full duplex broadband power line communications

In this paper, we introduce in-band full duplexing (IBFD) for broadband power line communication (BB-PLC) systems. Inspired by the use of IBFD in digital subscriber lines, Ethernet, cable communication, and recently in wireless communication, we investigate the constraints and requirements for a successful IBFD implementation in BB-PLC. We propose a two-stage IBFD structure consisting of an initial analog isolation using an active hybrid circuit, and a simplified mixed-domain digital echo cancellation procedure to suppress the self-interference. Furthermore, we enhance the digital cancellation filter to better adapt to linear periodically time-varying channel conditions, commonly encountered in PLC scenarios. We evaluate our solution under diverse power line channel and noise conditions to examine the overall data rate gains that can be achieved. Last, we extend IBFD to multiple-input multiple-output BB-PLC systems that enable faster and/or more robust data transmission.

ANOMALY IDENTIFICATION FROM SUPER-LOW FREQUENCY ELECTROMAGNETIC DATA FOR THE COALBED METHANE DETECTION

Abstract. Natural source Super Low Frequency(SLF) electromagnetic prospecting methods have become an increasingly promising way in the resource detection. The capacity estimation of the reservoirs is of great importance to evaluate their exploitation potency. In this paper, we built a signal-estimate model for SLF electromagnetic signal and processed the monitored data with adaptive filter. The non-normal distribution test showed that the distribution of the signal was obviously different from Gaussian probability distribution, and Class B instantaneous amplitude probability model can well describe the statistical properties of SLF electromagnetic data. The Class B model parameter estimation is very complicated because its kernel function is confluent hypergeometric function. The parameters of the model were estimated based on property spectral function using Least Square Gradient Method(LSGM). The simulation of this estimation method was carried out, and the results of simulation demonstrated that the LGSM estimation method can reflect important information of the Class B signal model, of which the Gaussian component was considered to be the systematic noise and random noise, and the Intermediate Event Component was considered to be the background ground and human activity noise. Then the observation data was processed using adaptive noise cancellation filter. With the noise components subtracted out adaptively, the remaining part is the signal of interest, i.e., the anomaly information. It was considered to be relevant to the reservoir position of the coalbed methane stratum.

ANOMALY IDENTIFICATION FROM SUPER-LOW FREQUENCY ELECTROMAGNETIC DATA FOR THE COALBED METHANE DETECTION

Natural source Super Low Frequency(SLF) electromagnetic prospecting methods have become an increasingly promising way in the resource detection. The capacity estimation of the reservoirs is of great importance to evaluate their exploitation potency. In this paper, we built a signal-estimate model for SLF electromagnetic signal and processed the monitored data with adaptive filter. The non-normal distribution test showed that the distribution of the signal was obviously different from Gaussian probability distribution, and Class B instantaneous amplitude probability model can well describe the statistical properties of SLF electromagnetic data. The Class B model parameter estimation is very complicated because its kernel function is confluent hypergeometric function. The parameters of the model were estimated based on property spectral function using Least Square Gradient Method(LSGM). The simulation of this estimation method was carried out, and the results of simulation demonstrated that the LGSM estimation method can reflect important information of the Class B signal model, of which the Gaussian component was considered to be the systematic noise and random noise, and the Intermediate Event Component was considered to be the background ground and human activity noise. Then the observation data was processed using adaptive noise cancellation filter. With the noise components subtracted out adaptively, the remaining part is the signal of interest, i.e., the anomaly information. It was considered to be relevant to the reservoir position of the coalbed methane stratum.

A Multitasking Control Algorithm for Grid-Connected Inverters in Distributed Generation Applications Using Adaptive Noise Cancellation Filters

This paper proposes a multitasking control algorithm for grid-connected inverters (GCIs) in distributed generation (DG) applications. A single-phase H-bridge voltage source inverter is used as a power electronic interface between the DG system and the grid. The proposed control algorithm operates the GCI in current control mode to achieve desired active power injection to the grid. In addition to active power injection, the proposed control algorithm has current harmonics mitigation and reactive power compensation capabilities for power quality enhancement. The control algorithm utilizes only the remaining capacity of the GCI for power quality improvement. The proposed control algorithm employs a current decomposition structure based on multiple adaptive noise cancellation (ANC) filters for extraction of harmonic and reactive currents of the local loads. The extracted harmonics are used in estimating compensating currents to be injected by the GCI. A single-phase phase-locked loop (PLL) using ANC filters has been developed to synchronize the GCI at fundamental frequency, which makes the proposed control algorithm adaptive to grid frequency fluctuations and immune to grid voltage distortions. The effectiveness of the proposed control is illustrated through computer simulation and experimental results. The influence of PLL dynamics on GCI performance has also been studied using results.

Design and Analysis of Cascaded LMS Adaptive Filters for Noise Cancellation

Adaptive filters have become active research area in the field of communication system. This paper investigates the innovative concept of adaptive noise cancellation (ANC) using cascaded form of least-mean-square (LMS) adaptive filters. The concept of cascading and its algorithm for real-time LMS-ANC are also described in detail. The model of the cascaded LMS-ANC is designed and simulated on MATLAB Simulink. The simulation model gives variation in the distinct signals of LMS-ANC like error, output and weights at various LMS filter parameters. The proposed algorithm utilizes two adaptive filters to estimate gradients accurately and results in good adaptation and performance. The objective of the present investigation is to provide solution in order to improve the performance of noise canceller in terms of filter parameters. The results are obtained with the help of adaptive algorithm with variable step size and different initial weight of filters which provides high convergence speed of error signal. This paper also includes the derivation for the convergence rate at different conditions and concludes that cascaded LMS-ANC results in higher convergence rate and better output signal as compared to single LMS-ANC. Higher signal-to-noise ratio for cascaded system is obtained for cascaded LMS-ANC as compared to that of single LMS-ANC system.

Performance Analysis of Adaptive Filters for Noise Cancellation in Audio Signal for Hearing Aid Application

Performance Analysis of Adaptive Filters for Noise Cancellation in Audio Signal for Hearing Aid Application

Design of ANC filter using modified cuckoo search technique for ECG signal enhancement

In this work, the design of an adaptive noise canceller (ANC) filter is presented using modified cuckoo search (MCS) optimization technique. The proposed scheme is applied for de-noising of ECG signals. Our simulation results reveal that the ANC filter based on MCS algorithm provides superior performance than other optimization techniques used to enhance the ECG signal. The performance of ANC filter is compared with other reported algorithms by evaluating the fidelity parameters such as the signal to noise ratio (SNR), maximum error (ME) and mean square error (MSE). The proposed ANC filter design with MCS scheme gives 18% improvement in output SNR, 87% decrease in ME, and 85% reduction in MSE over the recently reported Hilbert Huang Transform (HHT) technique.