Standard Adaptive Filter Research Articles

Adaptive Filter Model of Cerebellum for Biological Muscle Control With Spike Train Inputs.

Prior applications of the cerebellar adaptive filter model have included a range of tasks within simulated and robotic systems. However, this has been limited to systems driven by continuous signals. Here, the adaptive filter model of the cerebellum is applied to the control of a system driven by spiking inputs by considering the problem of controlling muscle force. The performance of the standard adaptive filter algorithm is compared with the algorithm with a modified learning rule that minimizes inputs and a simple proportional-integral-derivative (PID) controller. Control performance is evaluated in terms of the number of spikes, the accuracy of spike input locations, and the accuracy of muscle force output. Results show that the cerebellar adaptive filter model can be applied without change to the control of systems driven by spiking inputs. The cerebellar algorithm results in good agreement between input spikes and force outputs and significantly improves on a PID controller. Input minimization can be used to reduce the number of spike inputs, but at the expense of a decrease in accuracy of spike input location and force output. This work extends the applications of the cerebellar algorithm and demonstrates the potential of the adaptive filter model to be used to improve functional electrical stimulation muscle control.

Computational scrutiny of image denoising method found on DBAMF under SPN surrounding

Traditionally, rank order absolute difference (ROAD) has a great similarity capacity for identifying whether the pixel is SPN or noiseless because statistical characteristic of ROAD is desired for a noise identifying objective. As a result, the decision based adaptive median filter (DBAMF) that is found on ROAD technique has been initially proposed for eliminating an impulsive noise since 2010. Consequently, this analyzed report focuses to examine the similarity capacity of denoising method found on DBAMF for diverse SPN Surrounding. In order to examine the denoising capacity and its obstruction of the denoising method found on DBAMF, the four original digital images, comprised of Airplane, Pepper, Girl and Lena, are examined in these computational simulation for SPN surrounding by initially contaminating the SPN with diverse intensity. Later, all contaminated digital images are denoised by the denoising method found on DBAMF. In addition, the proposed denoised image, which is computed by this DBAMF denoising method, is confronted with the other denoised images, which is computed by Standard median filter (SMF), Gaussian Filter and Adaptive median filter (AMF) for demonstrating the DBAMF capacity under subjective measurement aspect.

Modified Second-Order Generalized Integrators With Modified Frequency Locked Loop for Fast Harmonics Estimation of Distorted Single-Phase Signals

This paper proposes modified Second-Order Generalized Integrators (mSOGIs) for a fast estimation of all harmonic components of arbitrarily distorted single-phase signals such as voltages or currents in power systems. The estimation is based on the internal model principle leading to an overall observer system consisting of parallelized mSOGIs. The observer is tuned by pole placement. For a constant fundamental frequency, the observer is capable of estimating all harmonic components with prescribed settling time by choosing the observer poles appropriately. For time-varying fundamental frequencies, the harmonic estimation is combined with a modified Frequency Locked Loop (mFLL) with gain normalization, sign-correct anti-windup and rate limitation. The estimation performances of the proposed parallelized mSOGIs with and without mFLL are illustrated and validated by measurement results. The results are compared to standard approaches such as parallelized standard SOGIs (sSOGIs) and adaptive notch filters (ANFs).

Hardware-in-the-loop flight motion simulation of flexible variable sweep aircraft

PurposeThis paper aims to propose an improved and computationally efficient motion simulation of a flexible variable sweep aircraft.Design/methodology/approachThe motion simulation is performed on hardware-in-the-loop simulation setup using 6 degree-of-freedom motion platform. The dynamic model of a flexible variable sweep aircraft, Rockwell B-1 Lancer is presented using equations of motions for combined rigid and flexible motions. The peak filter is introduced as a new method to separate flexible motion from aircraft motion data. Standard adaptive washout filter is modified and redesigned for an accurate flexible aircraft flight simulation. The flight data are generated using FlightGear software. Another motion profile with significant oscillations is also tested. The peak filter and the modified adaptive washout filter both are used to process the data according to the motion envelop of motion platform.FindingsThe performance of the modified adaptive washout filter is evaluated using hardware-in-the-loop simulation setup and results are compared with the standard adaptive washout filter. Results exhibit that the proposed method is computationally cost-effective and improves the motion simulation of flexible aircraft with close to realistic motion cues.Originality/valueThe proposed work presents motion simulation of a flexible aircraft by introducing a peak filter to extract flexible motion in contrast to the traditional motion separation methods. Also, a modified adaptive washout filter is designed and implemented in place of the traditional washout filters for improved flexible aircraft flight motion simulation.

Improving battery voltage prediction in an electric bicycle using altitude measurements and kernel adaptive filters

The time-varying nature of consumption patterns is critical in the development of reliable electric vehicles and real-time schemes for assessing energy autonomy. Most of these schemes use battery voltage observations as a primary source of information and neglect variables external to the vehicle that affect its autonomy and help to characterise the behaviour of the battery as main energy storage device. Using an electric bicycle as case study, we show that the incorporation of external variables (e.g., altitude measurements) improves predictions associated with evolution of the battery voltage in time. We achieve this by proposing a novel kernel adaptive filter for multiple inputs and with a data-dependent dictionary construction. This allows us to model the dependency between battery voltage and altitude variations in a sequential manner. The proposed methodology combines automatic discovery of the relationship between voltage and altitude from data, and a kernel-based voltage predictor to address an important issue in reliability of electric vehicles. The proposed method is validated against a standard kernel adaptive filter, fixed linear filters and adaptive linear filters as baselines on the short- and long-term prediction of real-world battery voltage data.

Field implementation feasibility study of cumulative travel‐time responsive (CTR) traffic signal control algorithm

SummaryThe cumulative travel‐time responsive (CTR) algorithm determines optimal green split for the next time interval by identifying the maximum cumulative travel time (CTT) estimated under the connected vehicle environment. This paper enhanced the CTR algorithm and evaluated its performance to verify a feasibility of field implementation in a near future. Standard Kalman filter (SKF) and adaptive Kalman filter (AKF) were applied to estimate CTT for each phase in the CTR algorithm. In addition, traffic demand, market penetration rate (MPR), and data availability were considered to evaluate the CTR algorithm's performance. An intersection in the Northern Virginia connected vehicle test bed is selected for a case study and evaluated within vissim and hardware in the loop simulations. As expected, the CTR algorithm's performance depends on MPR because the information collected from connected vehicle is a key enabling factor of the CTR algorithm. However, this paper found that the MPR requirement of the CTR algorithm could be addressed (i) when the data are collected from both connected vehicle and the infrastructure sensors and (ii) when the AKF is adopted. The minimum required MPRs to outperform the actuated traffic signal control were empirically found for each prediction technique (i.e., 30% for the SKF and 20% for the AKF) and data availability. Even without the infrastructure sensors, the CTR algorithm could be implemented at an intersection with high traffic demand and 50–60% MPR. The findings of this study are expected to contribute to the field implementation of the CTR algorithm to improve the traffic network performance. Copyright © 2017 John Wiley & Sons, Ltd.

Geometric-Algebra LMS Adaptive Filter and Its Application to Rotation Estimation

This letter exploits geometric (Clifford) algebra (GA) theory to devise and introduce a new adaptive filtering strategy. From a least-squares cost function, the gradient is calculated following results from geometric calculus (GC), the extension of GA to handle differential and integral calculus. The novel GA least-mean-squares (GA-LMS) adaptive filter, which inherits properties from standard adaptive filters and from GA, is developed to recursively estimate a rotor (multivector), a hypercomplex quantity able to describe rotations in any dimension. The adaptive filter (AF) performance is assessed via a 3-D point-clouds registration problem, which contains a rotation estimation step. Calculating the AF computational complexity suggests that it can contribute to reduce the cost of a full-blown 3-D registration algorithm, especially when the number of points to be processed grows. Moreover, the employed GA/GC framework allows for easily applying the resulting filter to estimating rotors in higher dimensions.

Two new methods for removing salt-and-pepper noise from digital images

In this study, we developed two new alternative methods to enhance images which operate satisfactorily with high levels of noise in any given window size. We also conducted a test by adding the most frequently used noise type to the test images of salt-and-pepper noise. The results are compared with other well-known noise remover methods in the literature such as, standard median filter (SMF) and adaptive median filter (AMF), and much better results were obtained. For instance, the results of SMF and AMF filters as well as peak signal-to-noise ratio (PSNR) are 33.2 and 29.8 respectively by adding a ratio of 0.1 salt-and-pepper noise to the Lena image. With the new methods,the results are 41.4 and 41.4. The newly developed methods also generate better results in reducing high volume of noise. For instance, if a ratio of 0.5 salt-and-pepper noise is applied to a Lena image, PSNR results are 15.22 and 22.78 in SMF and AMF, and 25.8 and 28.9 with newly developed methods.

Variable length LMS adaptive filter for carrier phase multipath mitigation

Multipath on carrier phase measurements is among the major error sources for short baseline positioning. A new method is proposed to improve the accuracy of the positioning results by mitigating the multipath effects on carrier phase measurements using the variable length Least Mean Square (VLLMS) adaptive filter. The performance of the filter is analyzed as well as compared with that of the standard LMS adaptive filter using a set of carrier phase observation data of two consecutive days collected in a short baseline experiment. Because the two antennas are static, the multipath error is the only dominant component in the carrier phase double-differenced residuals and indicates a repeated pattern. The numerical results show that both filters can significantly mitigate the multipath effects in carrier phase double-differenced residuals, and hence improve the positioning results. Furthermore, the VLLMS filter that reduces up to about 47.4% of the multipath effects on 3D positioning performs better than the LMS filter that only reduces 22.0%. Both filters are suitable for real-time applications.

Stability and Convergence Analysis of Transform-Domain LMS Adaptive Filters With Second-Order Autoregressive Process

In this paper, the stability and convergence properties of the class of transform-domain least mean square (LMS) adaptive filters with second-order autoregressive (AR) process are investigated. It is well known that this class of adaptive filters improve convergence property of the standard LMS adaptive filters by applying the fixed data-independent orthogonal transforms and power normalization. However, the convergence performance of this class of adaptive filters can be quite different for various input processes, and it has not been fully explored. In this paper, we first discuss the mean-square stability and steady-state performance of this class of adaptive filters. We then analyze the effects of the transforms and power normalization performed in the various adaptive filters for both first-order and second-order AR processes. We derive the input asymptotic eigenvalue distributions and make comparisons on their convergence performance. Finally, computer simulations on AR process as well as moving-average (MA) process and autoregressive-moving-average (ARMA) process are demonstrated for the support of the analytical results.

Suppression of Impulse Noise in Medical Images with the Use of Fuzzy Adaptive Median Filter

A new rule based fuzzy filter for removal of highly impulse noise, called Rule Based Fuzzy Adaptive Median (RBFAM) Filter, is aimed to be discussed in this paper. The RBFAM filter is an improved version of Adaptive Median Filter (AMF) and is presented in the aim of noise reduction of images corrupted with additive impulse noise. The filter has three stages. Two of those stages are fuzzy rule based and last stage is based on standard median and adaptive median filter. The proposed filter can preserve image details better then AMF while suppressing additive salt & pepper or impulse type noise. In this paper, we placed our preference on bell-shaped membership function instead of triangular membership function in order to observe better results. Experimental results indicates that the proposed filter is improvable with increased fuzzy rules to reduce more noise corrupted images and to remove salt and pepper noise in a more effective way than what AMF filter does.

Alternative Adaptive Filter Structures for Improved Radio Frequency Interference Cancellation in Radio Astronomy

In radio astronomy, reference signals from auxiliary antennas that receive only the radio frequency interference (RFI) can be modified to model the RFI environment at the astronomy receivers. The RFI can then be canceled from the astronomy signal paths. However, astronomers typically only require signal statistics. If the RFI statistics are changing slowly, the cancellation can be applied to the signal correlations at a much lower rate than is required for standard adaptive filters. In this paper we describe five canceler setups; precorrelation and postcorrelation cancelers that use one or two reference signals in different ways. The theoretical residual RFI and added noise levels are examined and are demonstrated using microwave television RFI at the Australia Telescope Compact Array. The RFI is attenuated to below the system noise, a reduction of at least 20 dB. While dual-reference cancelers add more reference noise than single-reference cancelers, this noise is zero-mean and only adds to the system noise, decreasing the sensitivity. The residual RFI that remains in the output of single-reference cancelers (but not dual-reference cancelers) sets a nonzero noise floor that does not act like random system noise and may limit the achievable sensitivity. Thus, dual-reference cancelers often result in superior cancellation. Dual-reference precorrelation cancelers require a double-canceler setup to be useful and to give equivalent results to dual-reference postcorrelation cancelers.

Recursive blind image deconvolution via dispersion minimization

AbstractThis paper presents an adaptive autoregressive (AR) approach to the blind image deconvolution problem which has several advantages over standard adaptive FIR filters. There is no need to figure out the optimum filter support when using an AR deconvolution filter because it is the same as the support of the blur. Thus there is no distortion introduced by the finite support of the FIR filter. While an FIR filter provides an approximate inverse to the blur at convergence, the AR filter converges to an approximation of the blur itself. Hence, the method can be used for blur identification. Simulations suggest that convergence of the adaptive AR filter coefficients occur rapidly and the improvement in signal‐to‐noise ratios are higher than in the FIR case for a given blur (and with the same step‐size for the adaptive algorithms). When the adaptive AR method is derived naively to minimize the dispersion, it requires a recursion within a recursion which is computationally complex. We propose a simplification that removes the inner recursion, and prove conditions under which this simplification is valid when dealing with binary images. Simulations are used to show that the method may also be applied to certain multi‐valued images as well. Copyright © 2005 John Wiley & Sons, Ltd.

A practical sub-space adaptive filter

A Sub-Space Adaptive Filter (SSAF) model is developed using, as a basis, the Modified Probabilistic Neural Network (MPNN) and its extension the Tuneable Approximate Piecewise Linear Regression (TAPLR) model. The TAPLR model can be adjusted by a single smoothing parameter continuously from the best piecewise linear model in each sub-space to the best approximately piecewise linear model over the whole data space. A suitable value in between ensures that all neighbouring piecewise linear models merge together smoothly at their boundaries. This model was developed by altering the form of the MPNN, a network used for general nonlinear regression. The MPNNs special structure allows it to be easily used to model a process by appropriately weighting piecewise linear models associated with each of the network's radial basis functions. The model has now been further extended to allow each piecewise linear model section to be adapted separately as new data flows through it. By doing this, the proposed SSAF model represents a learning/filtering method for nonlinear processes that provides one solution to the stability/plasticity dilemma associated with standard adaptive filters.

Rejection of PSK interference in DS-SS/PSK system using adaptive transversal filter with conditional response recalculation

PSK interference rejection using complex adaptive filter with conditional response recalculation in DS-SS/PSK system is considered in this paper. New adaptive filter decreases error probability for several orders of magnitude when compared to the standard adaptive transversal filter.

Hybrid subband/frequency-domain adaptive systems

For many years now, subband and frequency-domain adaptive filtering techniques have been proposed for the identification of high-order FIR systems. Classical LMS-based algorithms are less attractive as their computational load is higher and their convergence behaviour for coloured inputs is worse. Subband processing has many desirable properties. However, when used to implement adaptive filters, various effects (such as residual errors and slow convergence due to aliasing) occur and reduce performance. On the other hand, it is known that frequency-domain adaptive filters do not suffer from these problems despite being (nearly) equivalent to subband adaptive filters, be it with “poor” filter banks (modulated sinc frequency responses). In this paper the operation of the frequency-domain techniques are unravelled and explained in the “subband jargon” and among other things it will be shown how aliasing is compensated for in the frequency-domain approach. This paper aims at generalising frequency-domain aliasing-compensation techniques to subband adaptive systems. Further, some design criteria for subband adaptive systems are formulated. Three realisation conditions and an alternative adaptation scheme will be proposed. Standard subband adaptive filters cannot fulfil all conditions, whereas for frequency-domain-based algorithms the design criteria are met.

Application of a wavelet adaptive filter to minimise distortion of the ST-segment.

A wavelet adaptive filter (WAF) for the removal of baseline wandering in ECG signals is described. The WAF consists of two parts. The first part is a wavelet transform that decomposes the ECG signal into seven frequency bands using Vaidyanathan-Hoang wavelets. The second part is an adaptive filter that uses the signal of the seventh lowest-frequency band among the wavelet transformed signals as primary input and a constant as reference input. To evaluate the performance of the WAF, two baseline wandering elimination filters are used, a commercial standard filter with a cutoff frequency of 0.5 Hz and a general adaptive filter. The MIT/BIH database and the European ST-T database are used for the evaluation. The WAF performs better in the average power of eliminated noise than the standard filter and adaptive filter. Furthermore, it shows a lower ST-segment distortion than the standard filter and the adaptive filter.

A pipelined LMS adaptive FIR filter architecture without adaptation delay

Past methods for mapping the least-mean-square (LMS) adaptive finite-impulse-response (FIR) filter onto parallel and pipelined architectures either introduce delays in the coefficient updates or have excessive hardware requirements. We describe a hardware-efficient pipelined architecture for the LMS adaptive FIR filter that produces the same output and error signals as would be produced by the standard LMS adaptive filter architecture without adaptation delays. Unlike existing architectures for delayless LMS adaptation, the new architecture's throughput is independent of the filter length.

An adaptive technique for determining a reduced model for a system

A reduced or sparse system model is discussed that will contain only the most significant components, as opposed to a complete finite impulse response (FIR) model which may not be very accurate with the requirement of only a few components. The technique presented uses an adaptive delay filter to provide the sparse model and compares it to the model obtained with the standard adaptive filter. >

System modeling using an adaptive delay filter

In this paper, we present a special technique for modeling an unknown system. This technique requires a type of adaptive filter called an Adaptive Delay Filter [1]-[3]. This filter structure includes variable delay taps in addition to variable gains. The Adaptive Delay Filter is especially applicable to system modeling problems in which the system to be modeled has a sparse impulse response [4]-[7]. Using the standard adaptive filter to model a sparse system could require a very large filter [8], while an Adaptive Delay Filter could model the sparse impulse response with very few elements in the filter since the delay taps spread out to adapt to the unknown system. An analysis of the mean-squared error surface using this technique is included, along with the computer simulation results of the performance in modeling both the delay taps and the gains of an unknown system. A comparison of this technique with the conventional approach using Widrow's LMS algorithm will be addressed.