Adaptive Digital Filter Research Articles

Technique to Adjust Adaptive Digital Filter Coefficients in Residue Number System Based Filters

The paper discusses adaptive filtering using Least Mean Square (LMS) and Recursive Least Square (RLS) algorithms. An algorithm for adjusting the coefficients of an adaptive digital filter in the Residue Number System and a procedure of developed algorithm applying depending on filter length and signal length are proposed. Mathematical modeling of the considered algorithms is performed. Examples are presented to demonstrate how the proposed technique can help the designer in the adjustment of the filter coefficients without the need for extensive trial-and-error procedures. The analysis of the denoising quality and computational complexity is made. Synthetic and real data (earthquake recording) were used while testing. The proposed algorithm surpasses the existing ones like LMS and RLS, and their modifications in a number of parameters: adaptation (denoising) quality, ease of implementation, execution time. The main difference between the developed algorithm is the sequential adaptation of each coefficient with zero error. In the known algorithms, the entire vector of coefficients is iteratively adapted, with some specified accuracy. The iterations (steps) number is determined by the input signal length for all algorithms.

A Novel Multiplier-Less LMS Adaptive Filter Design Based on Offset Binary Coded Distributed Arithmetic

Multiply-Accumulate (MAC) operation is the backbone of Least Mean Squares (LMS) digital adaptive filters. Implementing LMS on hardware platform as a Fully Dedicated Architecture (FDA) multiplier becomes bottleneck for higher order filters, prompting high area, cost and power requirements and hence renders the design unsuited for practical implementation. In this paper, we have proposed a composite design that makes use of Distributed Arithmetic (DA) to replace the bottleneck multiplier with memory units that store Partial Products (PPs) to emulate multiplication. The depth of these memory units tends to exponentially grow as the filter order rises. To manage that, we have used Half Memory algorithm (HM) and Offset Binary Coding (OBC) to refine the structure of PPs such that the memory size is reduced at least by a factor of 4 for the same filter order. The proposed design improves system's Throughput, Critical Path Delay, Power Consumption and FPGA Resource Utilization. However, it introduces Latency in both the output and update segments of the LMS algorithm. To provide an option between resource utilization and latency, we have suggested a mechanism to halve the originally produced latency by the Parallel Processing of input bit steam w.r.t even and odd bits. Moreover, we have also proposed a method that reduces the latency of update module at the slight expense of other design attributes. The fundamental structure of the proposed design is flexible owing to the dynamic memory structure as well as the option to choose between latency and resource minimization. Simulations have been carried out in Xilinx Vivado and conclusions have been drawn by comparing both FDA and DA based designs. Results for a 16-tap filter indicate a remarkable improvement in Throughput, Area Utilization and Power Consumption by 18%, 5% and 3.5% respectively at the expense of 4× escalated latency. The Half Latency method allowed the latency to drop 2× but with slightly elevated power and area attributes.

Adaptive digital filter for the processing of atmospheric lidar signals measured by imaging lidar techniques.

The lidar signal measured by the atmospheric imaging lidar technique is subject to sunlight background noise, dark current noise, and fixed pattern noise (FPN) of the image sensor, etc., which presents quite different characteristics compared to the lidar signal measured by the pulsed lidar technique based on the time-of-flight principle. Enhancing the signal-to-noise ratio (SNR) of the measured lidar signal is of great importance for improving the performance of imaging lidar techniques. By carefully investigating the signal and noise characteristics of the lidar signal measured by a Scheimpflug lidar (SLidar) based on the Scheimpflug imaging principle, we have demonstrated an adaptive digital filter based on the Savitzky-Golay (S-G) filter and the Fourier analysis. The window length of the polynomial of the S-G filter is automatically optimized by iteratively examining the Fourier domain frequency characteristics of the residual signal between the filtered lidar signal and the raw lidar signal. The performance of the adaptive digital filter has been carefully investigated for lidar signals measured by a SLidar system under various atmospheric conditions. It has been found that the optimal window length for near horizontal measurements is concentrated in the region of 90-150, while it varies mainly in the region of 40-100 for slant measurements due to the frequent presence of the peak echoes from clouds, aerosol layers, etc. The promising result has demonstrated great potential for utilizing the proposed adaptive digital filter for the lidar signal processing of imaging lidar techniques in the future.

Active Noise Cancellation with MEMS Resonant Microphone Array.

This paper presents active noise cancelation (ANC) based on MEMS resonant microphone array (RMA) which offers very high sensitivities (and thus very low noise floors) near resonance frequencies and also provides filtering in acoustic domain. The ANC is targeted to actively cancel out any sound between 5 - 9 kHz (above the speech range of 300 - 3,400 Hz). The ANC works best around the resonance frequencies of the resonant microphones where the sensitivities are high. The ANC has been implemented with analog inverter, digital phase compensator, digital adaptive filter, and deep learning, and shown to perform better with a digital adaptive filter for both RMA-based and flat-band-microphone-based ANC. At the same time, when the sound intensity over 5 - 9 kHz is low, RMA-based ANC with adaptive filter works the best among different approaches tested. Automatic speech recognition under different noises (of different intensity levels) has been tested with ANC. In all the tested cases, word error rate improves with ANC.

A Single-Chip Bidirectional Neural Interface With High-Voltage Stimulation and Adaptive Artifact Cancellation in Standard CMOS

A single-chip, bidirectional brain–computer interface (BBCI) enables neuromodulation through simultaneous neural recording and stimulation. This article presents a prototype BBCI application-specified integrated circuit (ASIC) consisting of a 64-channel time-multiplexed recording front-end, an area-optimized four-channel high-voltage compliant stimulator, and electronics to support the concurrent multi-channel stimulus artifact cancellation. Stimulator power generation is integrated on a chip, providing ±11-V compliance from low-voltage supplies with a resonant charge pump. High-frequency (~3 GHz) self-resonant clocking is used to reduce the pumping capacitor area while suppressing the associated switching losses. A 32-tap least mean square (LMS)-based digital adaptive filter achieves 60-dB artifact suppression, enabling simultaneous neural stimulation and recording. The entire chip occupies 4 mm2 in a 65-nm low power (LP) process and is powered by 2.5-/1.2-V supplies, dissipating $205~\mu \text{W}$ in recording and $142~\mu \text{W}$ in the stimulation and cancellation back-ends. The stimulation output drivers achieve 31% dc–dc efficiency at a maximum output power of 24 mW.

Investigation of the Operation of the Extended Kalman Filter Supplemented by an Adaptive Digital Filter for Integrating Data from a Mobile Robot Control System

Purpose of reseach. The article deals with the study of the operation of the extended Kalman filter (EKF), supplemented with an adaptive digital filter in order to compensate for the error in the operation of the EKF when performing data integration of the mobile robot control system.Methods. The adaptive digital filter (ADF) is a self-tuning filter that iteratively changes its variable parameters to achieve the optimal desired values of the output data. The EKF supplemented with the ADF with the NLMS adaptation algorithm will be called the EKF–ADF system or the EKF+NLMS digital filter. An important task is the selection of the number of frames and ADF weighting coefficients at which the optimum quality of noise suppression and the convergence rate of the adaptation algorithm are achieved.Results. With various options of organizing the operation of the ADF buffer memory, the adjusted values for assessing the state of the ‘mobile robot–environment’ system may differ. When the number of input data frames and ADF weighting coefficients are small, low quality noise suppression will be observed. With an increase in the number of frames and weighting coefficients, the quality of noise suppression is improved, and the convergence rate of the adaptation algorithm decreases.Conclusion. The EKF+NLMS digital filter algorithm takes an intermediate place between the EKF algorithm and the serial filtering of the EKF and ADF signals with the NLMS algorithm according to the criteria for estimating the mean square error, mean absolute error, signal-to-noise ratio, and convergence rate.

РАСШИРЕННЫЙ ФИЛЬТР КАЛМАНА, ДОПОЛНЕННЫЙ АДАПТИВНЫМ ЦИФРОВЫМ ФИЛЬТРОМ, ДЛЯ КОМПЛЕКСИРОВАНИЯ ДАННЫХ СИСТЕМЫ УПРАВЛЕНИЯ МОБИЛЬНЫМ РОБОТОМ

The paper proposes the combination of the extended Kalman filter and an adaptive digital filter to compensate an operational error of the extended Kalman filter during data fusion of a mobile robot control system. The paper describes the structure and operation of such combination, shows the buffer memory configuration of an adaptive digital filter.

An optimized MAC based architecture for adaptive digital filter

Filter design in signal processing field plays a vital role in achieving low power dissipation, which is essential for portable gadgets. This paper proposes an effective flexible FIR filter structure, which is adaptive and utilizes multiply–accumulate (MAC) core. Most common algorithm for filter coefficient optimization includes least mean square (LMS) and recursive least square (RLS). Though the performance of the recursive least square (RLS) algorithm is superior as compared to the least mean square (LMS); because of higher arithmetic complexity in design, it has not been preferred for real time applications. The fundamental filter has used a LMS based tapped delay line filter, which is practically a feasible choice for adaptive filtering algorithm in order to attain lesser computation. In the proposed work, the adjustable coefficient filters using an optimized LMS approach has been implemented for the utilization of determining the unexplored system. The filter tap considered here is a 32-tap and its analysis and synthesis has been carried out using hardware description language (HDL) programming and synthesized in field programmable gate array (FPGA) devices. The placement and post routing design has offered good performance in terms of utilized resources. The implemented filter architecture requires 80% reduction in resources and has enhanced the clock frequency by about five times when examined with the reported architecture.

A Novel Adaptive LMS Algorithm with Genetic Search Capabilities for System Identification of Adaptive FIR and IIR Filters

In this paper we introduce a novel adaptation algorithm for adaptive filtering of FIR and IIR digital filters within the context of system identification. The standard LMS algorithm is hybridized with GA (Genetic Algorithm) to obtain a new integrated learning algorithm, namely, LMS-GA. The main aim of the proposed learning tool is to evade local minima, a common problem in standard LMS algorithm and its variants and approaching the global minimum by calculating the optimum parameters of the weights vector when just estimated data are accessible. In the proposed LMS-GA technique, first, it works as the standard LMS algorithm and calculates the optimum filter coefficients that minimize the mean square error, once the standard LMS algorithm gets stuck in local minimum, the LMS-GA switches to GA to update the filter coefficients and explore new region in the search space by applying the cross-over and mutation operators. The proposed LMS-GA is tested under different conditions of the input signal like input signals with colored characteristics, i.e., correlated input signals and investigated on FIR adaptive filter using the power spectral density of the input signal and the Fourier-transform of the input’s correlation matrix. Demonstrations via simulations on system identification of IIR and FIR adaptive digital filters revealed the effectiveness of the proposed LMS-GA under input signals with different characteristics.

Adaptive digital notch filter based on online grid impedance estimation for grid-tied LCL filter systems

In grid-connected systems, LCL filters are playing an important role in mitigating the current harmonics as compared with the traditional L filter. Nevertheless, the performance of the LCL filters might deteriorate under the variations in grid impedance conditions resulting in excessive resonance phenomenon and even system instability hazards. The digital notch filter is an effective solution to tackle the resonance frequency problem by eliminating the resonant peak value; however, the prior art is only designed under the consideration of unknown grid impedance. This paper presents a novel implementation of an adaptive digital notch filter based on on-line grid impedance estimation to ensure the robustness against grid impedance variations. Stability analysis to confirm the relationship between the system parameters and grid impedance value is carried out. Furthermore, an extended Kalman filter is applied to design an observer which can estimate the grid impedance accurately. Considering the uncertainty of the real-time system, a lookup table based on the estimated grid impedance value and the control gain is proposed to tune the parameters of the digital notch filter to increase the relative stability of the system. Finally, the effectiveness of the developed adaptive notch filter is verified by both simulation and experimental results under varying grid impedance conditions.

Development of Multi-Staged Adaptive Filtering Algorithm for Periodic Structure-Based Active Vibration Control System

A digital adaptive filtering system is applied to various fields such as current disturbance, noise cancellation, and active vibration and noise control. The least mean squares (LMS) algorithm is widely adopted, owing to its simplicity and low computational burden. A limitation of the LMS algorithm with fixed step size is the trade-off between convergence speed and stability. Several studies have tried to overcome this limitation by varying the step size according to filter input and error; however, the related algorithms with variable step size have not been suitable for signals with complex frequency spectra. As the error decreases, the quality of the output signal deteriorates due to the increase in the higher-order components, depending on the characteristics of the algorithm. Therefore, a novel adaptive filtering algorithm was proposed to overcome these drawbacks. It increased the stability of the system by decreasing the step size using an exponential function. In addition, the error was reduced through normalization using the power of the input signal in the initial state, and the misadjustments in the system were adjusted properly by introducing an energy autocorrelation function of instantaneous error. Furthermore, a novel multi-staged adaptive LMS (MSA-LMS) algorithm was introduced and applied to active periodic structures. The proposed algorithm was validated by simulation and observed to be superior to the conventional LMS algorithms. The results of this study can be applied to active control systems for the reduction of vibration and noise signals with complex spectra in next-generation powertrains, such as hybrid and electric vehicles.

The method of primary processing of thermograms obtained using small-size thermal imagers

The purpose of the article is to develop and study the method of noise removal on low spatial resolution thermograms obtained by commercial small-sized thermal imaging cameras, which due to their weight and sizes can be used on mini-UAVs for thermal monitoring of power facilities. The main parameter that carries information about the operation mode of the object and the presence of defects during thermal monitoring is the temperature distribution over the surface of the object, which can be recorded by a thermal camera. The characteristics of commercial thermal imagers with uncooled arrays were analyzed. Due to low cost and mass, their usage can significantly reduce the cost of the monitoring system by reducing both the cost of the payload and the requirements for UAV payload capabilities. An issue of thermal imaging is a relatively low resolution of thermograms and a significant level of noise on them, which requires the image processing methods that would minimize the noise level. The process of obtaining thermograms using a compact thermal imager in real-time was considered. A method of removing the main types of thermogram distortions, based on an adaptive digital median filter, was proposed and implemented. The process of occurrence of a thermal gradient that occurs during the operation of the bolometric matrix was analyzed, and a method for was removal is given. It is shown that the proposed methods allow almost completely eliminated the thermogram artifacts.

Measurement of Airport Operations Using a Low-Cost Transponder Data System

Knowledge of the precise number of operations of aircraft occurring at general aviation airports is essential due to the application of that information in the process of allocating funds for airport development and improvement; however, fewer than 270 of these 2950 airports have personnel available to register the operation counts. Data from aircraft transponders, critical for the safe and efficient management of airspace, may also be used to accurately count airport operations when collected by a receiver and analyzed with appropriate algorithms. Whereas 91% of transponder data records do not include aircraft positions, 9% (extended mode S) contain position information. This information may be used to calibrate an adaptive digital filter that can subsequently estimate distances for the aircraft that do not report positions, a novel technical approach to the problem. Aircraft distance, altitude, and other parameters may then be used as inputs to decision heuristics that register airport operations. This study collected over 16 million data records from two receiver installations at a general aviation airport and used these data to evaluate the algorithms developed in the course of the research. These automated operation counts were compared with official air-traffic-control-tower counts obtained from the Air Traffic Activity Data System database provided by the Federal Aviation Administration. A 180-day evaluation found that the algorithm provided counts within 2.2% of 52,750 actual operations. It is evident, then, that this method is useful for determining operation counts at airports with limited personnel.

A FPGA-based Fast Converging Digital Adaptive Filter for Real-time RFI Mitigation on Ground Based Radio Telescopes

Radio Frequency Interference (RFI) is a growing concern in the radio astronomy community. Single-dish telescopes are particularly susceptible to RFI. Several methods have been developed to cope with RF-polluted environments, based on flagging, excision, and real-time blanking, among others. All these methods produce some degree of data loss or require assumptions to be made on the astronomical signal. We report the development of a real-time, digital adaptive filter implemented on a Field Programmable Gate Array (FPGA) capable of processing 4096 spectral channels in a 1 GHz of instantaneous bandwidth. The filter is able to cancel a broad range of interference signals and quickly adapt to changes on the RFI source, minimizing the data loss without any assumption on the astronomical or interfering signal properties. The speed of convergence (for a decrease to a 1%) was measured to be 208.1 μs for a broadband noise-like RFI signal and 125.5 μs for a multiple-carrier RFI signal recorded at the FAST radio telescope.

Fully Digital Feedforward Background Calibration of Clock Skews for Sub-Sampling TIADCs Using the Polyphase Decomposition

This paper presents a low-power fully digital clock skew feedforward background calibration technique in sub-sampling Time-Interleaved Analog-to-Digital Converters (TIADCs). Both estimation and correction algorithms share the common derivative filter, which makes them possible to reduce the chip area. Furthermore, these algorithms use the polyphase filtering technique and do not use adaptive digital synthesis filters. Thus, the proposed calibration can be implemented on a moderate hardware cost with low power dissipation. The adopted feedforward technology eliminates the stability issues encountered with the adaptive technique. The Hardware Description Language (HDL) design of the proposed calibration is synthesized using a 28nm FD-SOI process for a 60dB SNR TIADC clocked at 2.7GHz. The calibration is designed for both baseband and sub-sampling TIADC applications. For sub-sampling TIADCs with the input at the first four Nyquist bands, the synthesized calibration system occupies $0.04\rm {mm}^{2}$ of area and dissipates a total power of 33.2mW. For the baseband TIADC applications, it occupies $0.02\rm {mm}^{2}$ and consumes 15.5mW.

Efficient digit serial architecture for sign based least mean square adaptive filter for denoising of artefacts in ECG signals

Variants of Least Mean Square algorithm, namely the Sign Error, Sign Regressor and Sign-Sign Least Mean Square algorithms are meant for reducing computational complexity in digital adaptive filters in denoising electrocardiogram signals. In adaptive filtering using Sign-Sign algorithm, the adders and multipliers in the existing method take the coefficients in canonic signed power of two form which assures that between two non-zero there should be one zero in signed power of two digits and they can process only one bit per clock cycle and so is the filter resulting in low speed. In this paper, digit serial adder and digit serial multiplier are proposed to design the Sign-Sign Least Mean Square filter, which increases the speed by processing two bits per clock cycle. The digit serial adder and digit serial multiplier are used to design the Sign-Sign based adaptive filter in which unfolding technique is used for digit serial realisation. The unfolding factor of K = 2 is chosen to increase the speed of the design. The coefficients and its inputs are represented in Canonic Signed Digit form. Adder, multiplier, adaptive filter of both existing and proposed method are simulated using Mentor Graphics and synthesised using Synopsys Design Compiler Tool with 90 nm technology. From the results it is observed that the speed of the proposed digit serial Sign-Sign Least Mean Square filter is increased by 54.5% and the area of the filter is decreased by 4.21% compared to the bit serial Sign-Sign Least Mean Square filter.

Efficient digit serial architecture for sign based least mean square adaptive filter for denoising of artefacts in ECG signals

Variants of Least Mean Square algorithm, namely the Sign Error, Sign Regressor and Sign-Sign Least Mean Square algorithms are meant for reducing computational complexity in digital adaptive filters in denoising electrocardiogram signals. In adaptive filtering using Sign-Sign algorithm, the adders and multipliers in the existing method take the coefficients in canonic signed power of two form which assures that between two non-zero there should be one zero in signed power of two digits and they can process only one bit per clock cycle and so is the filter resulting in low speed. In this paper, digit serial adder and digit serial multiplier are proposed to design the Sign-Sign Least Mean Square filter, which increases the speed by processing two bits per clock cycle. The digit serial adder and digit serial multiplier are used to design the Sign-Sign based adaptive filter in which unfolding technique is used for digit serial realisation. The unfolding factor of K = 2 is chosen to increase the speed of the design. The coefficients and its inputs are represented in Canonic Signed Digit form. Adder, multiplier, adaptive filter of both existing and proposed method are simulated using Mentor Graphics and synthesised using Synopsys Design Compiler Tool with 90 nm technology. From the results it is observed that the speed of the proposed digit serial Sign-Sign Least Mean Square filter is increased by 54.5% and the area of the filter is decreased by 4.21% compared to the bit serial Sign-Sign Least Mean Square filter.

Acoustic echo and noise canceller for personal hands-free video IP phone

This paper presents implementation and evaluation of a proposed acoustic echo and noise canceller (AENC) for videotelephony-enabled personal hands-free Internet protocol (IP) phones. This canceller has the following features: noise-robust performance, low processing delay, and low computational complexity. The AENC employs an adaptive digital filter (ADF) and noise reduction (NR) methods that can effectively eliminate undesired acoustic echo and background noise included in a microphone signal even in a noisy environment. The ADF method uses the step-size control approach according to the level of disturbance such as background noise; it can minimize the effect of disturbance in a noisy environment. The NR method estimates the noise level under an assumption that the noise amplitude spectrum is constant in a short period, which cannot be applied to the amplitude spectrum of speech. In addition, this paper presents the method for decreasing the computational complexity of the ADF process without increasing the processing delay to make the processing suitable for real-time implementation. The experimental results demonstrate that the proposed AENC suppresses echo and noise sufficiently in a noisy environment; thus, resulting in natural-sounding speech.

Noise-Free Speech Channel Modeling for Technical Control Systems

The paper focuses on the application of digital adaptive filtering to ensure the noise-free speech channels of control systems. Noise is an extraneous signal, which enters the channel device input together with the speech. The article conducts a comparative analysis of the three adaptation algorithms (LMS, NLMS, RLS) to be useful for noise suppression. At the same time, an adaptive suppression mechanism with two microphones on the speech channel is used. The related publications compared the algorithms by one or two criteria. This paper offers three comparison criteria: computational complexity, quality of noise suppression, and rate of convergence to the steady-state condition. The number of vector operations in algorithm procedures estimates temporary computational complexity. To compare algorithms by the other two criteria, their implementation was simulated in MATLAB. As the noise, were used the white and pink noise, a sine wave, and a model of the non-stationary signal as well. The noise suppression coefficient and the number of iterations before transition to the steady-state condition have been obtained. The algorithm RLS showed the best quality of suppression while the NLMS algorithm revealed the highest rate of convergence. Experiments have shown that the white noise is suppressed worse, but faster than the sine one. The paper explores influence of some factors on the process of adaptation. It is shown that increase in filter dimension leads to improving quality of adaptation and its speed-down. From simulation results it follows that the noise suppression coefficient has the highest values when at the filter input there are approximately equal signal and noise powers. The results allow us to make commendation to use the NLMS algorithm for real-time systems, and the RLS one for technical systems aimed at recordings of speech signals. The above algorithms can be successfully implemented on modern technology platforms, comprising high-performance digital signal processors and associated peripherals.

A MODIFIED TWO DIMENSIONAL ADAPTIVE DIGITAL FILTER

In this paper, a new scheme of recursive two-dimensional (2D) adaptive digital filter is presented. It consists of a forward section, a feedback section, and decision block. A little complexity increase from [10] is used by adding adaptive quantizer to predictor error. Each section is realized as conventional non-recursive adaptive filter. Either 2D adaptive LMS or sign function algorithm can be used for adaptation. The proposed recursive filter operates as a 2D adaptive line enhancer (ALE) for images contaminated with noise. Its performance is compared with that given in [1].