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The calibration model of near-infrared (NIR) spectra established using the Kalman filter-partial least square (partial least squares combined with a Kalman filter) method can be adapted to outdated...

To improve the control performance for grid-tied power converters, the fundamental frequency and harmonics of the grid voltage should be quickly and accurately estimated even under distorted voltage condition. This paper introduces a straightforward discrete adaptive filter to observe fundamental and harmonic sequence components of the grid voltage. The proposed filter possesses a simple and straightforward implementation. Based on the newly developed filter, frequency error transmission was analyzed when the frequency of the input voltage was unequal to the center frequency of the filter. Consequently, a frequency estimation loop was designed to adapt to the frequency variation for the filter. Compared to previous algorithms, the proposed algorithm has a better dynamic performance rating for frequency estimation, robust stability, and a simpler implementation. The effectiveness of the algorithm is verified by simulations and laboratory experiments.

In wireless communication, the space-time anti-jamming method is widely applied because it shows better performance than the pure airspace and pure temporal anti-jamming methods. However, its application is limited by its computational complexity, and it cannot suppress narrowband interference that is in the same direction as the navigation signal. To solve these problems, we propose improved frequency filter to filter the narrowband interference from the desired signal direction in advance, meanwhile, an improved variable step Least Mean Square (LMS) method is proposed to complete the space-time array weights with fast iteration, thereby reducing computational complexity. The simulation results show that, compared with conventional methods, the anti-jamming capability of the proposed algorithm is significantly enhanced; and its complexity is significantly reduced.

The convergence rate and the continuous tracking precision are two main problems of the existing adaptive notch filter (ANF) for frequency tracking. To solve the problems, the frequency is detected by interpolation FFT at first, which aims to overcome the convergence rate of the ANF. Then, referring to the idea of negative feedback, an evaluation factor is designed to monitor the ANF parameters and realize continuously high frequency tracking accuracy. According to the principle, a novel adaptive frequency estimation algorithm based on interpolation FFT and improved ANF is put forward. Its basic idea, specific measures and implementation steps are described in detail. The proposed algorithm obtains a fast estimation of the signal frequency, higher accuracy and better universality qualities. Simulation results verified the superiority and validity of the proposed algorithm when compared with original algorithms.

Symmetric adaptive decorrelation (SAD) is a semi-blind method of separating convolutely mixed signals. While it has restrictions on the physical layout of the demixing equipment, it is better suited for some applications (e.g., live sound mixing) as no post-processing is required to ascertain which output corresponds with which source. Since SAD is based on the least mean squares algorithm, it can be modified to perform the bulk of the processing in the frequency domain. This makes it more efficient for larger filter sizes and/or larger number of sources but renders it unsuitable for real-time applications as there is a lag between the output and the input. In this paper, we propose a hybrid approach that does not suffer from the lag of the frequency domain approach. While the proposed algorithm is slightly less computationally efferent than the pure frequency domain algorithm, it is significantly more efficient than the time domain approach. A comparison of the frequency domain and hybrid algorithms shows that both achieve separation equivalent to the time domain algorithm in a real-world environment.

Restoration of images corrupted with periodic noise is a fundamental task in digital image processing since periodic noise affects all imaging processes. The paper presents an adaptive threshold based frequency domain filter for denoising periodic noise from corrupted digital images. The algorithm applies forward origin shifting and Fourier transform to convert spatial domain image to frequency domain image. The proposed algorithm adaptively determines the threshold function for identifying the noisy peak areas of frequency domain image. The identified noisy frequencies corresponding to noisy peak areas of frequency domain image are diffused by the minimum filter for the effective restoration of frequency domain images. The restored frequency domain image is applied with inverses of Fourier transform and shifting operations to reconstruct the final restored image in spatial domain. Experimental analysis on different images with varying noisy conditions shows that the proposed algorithm is capable of producing better restored images than other algorithms used in the comparative study.

This article focuses on the application of adaptive filter based on the LMS algorithm. An adaptive filter of the closed-loop system is introduced, including the elimination of interference signal, the prediction of useful signal, and the approximation of expected signal. LMS (Least Mean Square) algorithm is used to meet the optimum norm of error between estimated signal and expected signal. The structure of LMS algorithm is presented and the simulation of LMS algorithm is carried out. The results indicate that the convergence performances of LMS algorithm are prefect, and the input signal can converge to the expected signal. The application of adaptive filtering technology in this article includes the correction of channel mismatch by an adaptive linear filter, the improvement of system performance by an adaptive equalizer, and the filter of frequency signal by an adaptive notch filter. The analysis on adaptive linear filter shows that the constant channel mismatch can be corrected quite well by the correction algorithm. The analysis on adaptive equalizer shows that the error rate of system with an adaptive equalizer has significant improvement gains over that of system without an adaptive equalizer. The smaller the error rate, the larger the SNR. The relationship between error rate and multi-path loss show that the error rate is largest when the loss factor is 0.5. The analysis on adaptive notch filter shows that the interference signal with two different known frequencies can be eliminated effectively by the adaptive notch filter. The filtered signals accord with the corresponding useful signals very well.

This letter presents a new lattice-form complex adaptive IIR notch filter to estimate and track the frequency of a complex sinusoid signal. The IIR filter is a cascade of a direct-form all-pole prefilter and an adaptive lattice-form all-zero filter. A complex domain exponentially weighted recursive least square algorithm is adopted instead of the widely used least mean square algorithm to increase the convergence rate. The convergence property of this algorithm is investigated, and an expression for the steady-state asymptotic bias is derived. Analysis results indicate that the frequency estimate for a single complex sinusoid is unbiased. Simulation results demonstrate that the proposed method achieves faster convergence and better tracking performance than all traditional algorithms.

This paper presents an adaptive Volterra filter implementation based on an exponentially-weighted a posteriori H∞ filtering algorithm. Its fast array form is immediately obtained by following...

The hearing aid being a battery operated, portable device requires short processing delay, low computational complexity, with appreciable acoustic feedback cancellation effect. The prediction error method (PEM) and PEM with shadow filter (PEM-SH) based adaptive feedback canceller (AFC) referred as PEMAFC and PEMAFC-SH respectively reduces the amount of bias present in the estimate of feedback path. The available partitioned block frequency domain adaptive filter (PBFAF) based implementation of PEMAFC (PBFAF-P) and PEMAFC-SH (PBFAF-PS), offers a potential option for modelling an adaptive filter with many taps along with short block processing delay. However, the PBFAF suffers from large computational load because of the involvement of computationally expensive gradient constraints in each partition. Though removing or alternately applying the gradient constraint saves some computations but it results in significant performance degradation. With an objective of substantially reducing the computational burden and simultaneously retaining the performance, this paper develops an improved partitioned block Hartley domain adaptive filter (IPBHAF) and then employs it for effective feedback cancellation in hearing aids. Further, the IPBHAF with modified step size (IPBHAF-M) is proposed to achieve both fast convergence and better steady state performance. The simulation based experiments demonstrate the superior performance of IPBHAF-M based implementations of PEMAFC (IPBHAF-MP) and PEMAFC-SH (IPBHAF-MPS) over the PBFAF-P and PBFAF-PS in terms of both computational complexity and feedback cancellation performance.

WOS: 000353312900011, Recently, two dimensional (2D) adaptive filter, which can self-adjust the filter coefficients by using an optimization algorithm driven by an error function, has attracted much attention by researchers and practitioners, because 2D adaptive filtering can be employed in many image processing applications, such as image denoising, enhancement and deconvolution. In this paper, a novel 2D artificial bee colony (2DABC) adaptive filter algorithm was firstly proposed and to the best of our knowledge, there is no study describing 2D adaptive filter algorithm based on metaheuristic algorithms in the literature. At the first stage, in order to analyze the performance and computational efficiency of the novel 2D-ABC adaptive filter algorithm, it was used in the 2D adaptive noise cancellation (ANC) as recommend in literature. For a fair comparison, the competitor 2D adaptive filter algorithms were applied to the same 2D-ANC setup under same condition, such as same Gaussian noise, same filter order or same test images. The results of the novel 2D-ABC adaptive filter algorithm were compared with those of the 2D affine projection algorithms (APA), 2D normalized least mean square (NLMS) and 2D least mean square (LMS) adaptive filter algorithms. At the second stage, to demonstrate the robustness of the novel 2D-ABC adaptive filter algorithm, it was implemented for speckle noise filtering on noisy clinical ultrasound images. The results show that the novel 2D-ABC adaptive filter algorithm has a better performance than the other classical adaptive filter algorithms and its denoising efficiency is quite well on noisy images with different characteristics. (C) 2015 Elsevier Inc. All rights reserved., Research Fund of the Erciyes University [FDK-2012-4156], The authors are indebted to the reviewers for their constructive suggestions which significantly helped in improving the quality of this paper. This work was supported by Research Fund of the Erciyes University. Project Number: FDK-2012-4156.

According to the theory of dark channel prior a image haze-removal algorithm is proposed in this paper. The algorithm uses maximum-minimum value filter combined together with guided filter to remove haze from the original image and uses wavelet to enhance the visual effect of the de-hazed image. Using maximum-minimum value filter only can cause the problem that the algorithm depending on the value of transmission lower limit excessively, by using maximum-minimum value filter combined together with guided filter the problem can be solved efficiently and the transmission matrix is refined adaptively. The white halos and patchy singularities which exist at the edge of the depth field in the reconstructed image is eliminated. Furthermore the algorithm refine the values of transmission which are estimated too big or too small. Finally wavelet is adopted to enhance the visual effect of the de-hazed image effectively. The objective evaluations of the reconstructed de-hazed image such as reconstructed image entropy, reconstructed image variance, reconstructed image mean square error, the degree of reconstructed image change and reconstructed image clarity are also studied in the paper, but these indicators can not represent the advantages and disadvantages of the performance of the image haze-removal algorithm, so it still needs further study in this field.

Noise cancellation is the primary issue of the theory and practice of signal processing. The Savitzky-Golay (SG) smoothing and differentiation filter is a well studied simple and efficient technique for noise eliminating problems. In spite of all, only few book on signal processing contain this method. The performance of the classical SG-filter depends on the appropriate setting of the windowlength and the polynomial degree. Thus, the main limitations of the performance of this filter are the most conspicious in processing of signals with high rate of change. In order to evade these deficiencies in this paper we present a new adaptive design to smooth signals based on the Savitzky-Golay algorithm. The here provided method ensures high precision noise removal by iterative multi-round smoothing. The signal approximated by linear regression lines and corrections are made in each step. Also, in each round the parameters are dynamically change due to the results of the previous smoothing. The applicability of this strategy has been validated by simulation results.

An automatic gain tuning algorithm is proposed for a recently introduced adaptive notch filter. Theoretical analysis and simulations show that, under Gaussian random-walk type assumptions, the proposed extension is capable of adjusting adaptation gains of the filter so as to minimize the mean-squared frequency tracking error without prior knowledge of the true frequency trajectory. A simplified one degree of freedom version of the filter, recommended for practical applications, is proposed as well.

This paper examines the problem of exponentially-weighted H∞ adaptive filtering and shows that its suboptimal solution reduces to a recursive algorithm which is slightly different from the RL...

This paper introduces an adaptive adjusting FIR filter’s parameters (LMS) method and presents a system recognition model based on the adaptive filter theory. The adaptive filter is directly with adjustable coefficient h (0),h (1),...h (N-1).The unknown system and FIR model have the same input sequence. The simulation result confirms the feasibility of the model.

Acoustic echo degrades the quality of speech in hands-free telephony. The most popular digital signal processing technique to suppress acoustic echo is adaptive filtering. However, adaptive filtering may require the computational cost optimization in particular when adaptive algorithm is implemented on low-cost DSP platforms. We propose a computationally efficient version of the partitioned block frequency domain adaptive filter with multiple iterations on current data block. The algorithm performs as a cascade of two adaptive filters. The first filter minimizes the Least Square (LS) criteria leading to unbiased estimate of a room response. The second filter speeds up the convergence rate using multiple iterations to minimize modified LS criterion. Coefficients updates calculated in a single step substitute for multiple iterations and decrease computational costs. The complexity of the algorithm is o(log"2(R)), where R is a number of iterations. The proposed algorithm was tested in a simulated room and a real reverberant room. Tests proved that our algorithm converges faster compared to algorithms described in literature.

In silicon on chip technology demands high performance and low power Very Large Scale Integrated Circuit (VLSI) digital signal processing (DSP) systems. The aim of this paper explores the power consumption technique for the architecture of Finite Impulse Response (FIR) adaptive filter. An adaptive FIR filter with Block Least Mean Square (BLMS) algorithm was developed to reduce the power. Distributed arithmetic (DA)-based formulation of BLMS algorithm is used to reduce the area where both convolution operation to compute filter output and correlation operation to compute weight-increment term could be performed by using the same LUT. Thus a DA based implementation of adaptive filter is highly computational and area efficient.

This paper presents a precise analysis of the critical path of the least-mean-square (LMS) adaptive filter for deriving its architectures for high-speed and low-complexity implementation. It is shown that the direct-form LMS adaptive filter has nearly the same critical path as its transpose-form counterpart, but provides much faster convergence and lower register complexity. From the critical-path evaluation, it is further shown that no pipelining is required for implementing a direct-form LMS adaptive filter for most practical cases, and can be realized with a very small adaptation delay in cases where a very high sampling rate is required. Based on these findings, this paper proposes three structures of the LMS adaptive filter: (i) Design 1 having no adaptation delays, (ii) Design 2 with only one adaptation delay, and (iii) Design 3 with two adaptation delays. Design 1 involves the minimum area and the minimum energy per sample (EPS). The best of existing direct-form structures requires 80.4% more area and 41.9% more EPS compared to Design 1. Designs 2 and 3 involve slightly more EPS than the Design 1 but offer nearly twice and thrice the MUF at a cost of 55.0% and 60.6% more area, respectively.

Aiming at the large calculation workload of adaptive algorithm in adaptive filter based on wavelet transform, affecting the filtering speed, a wavelet-based neural network adaptive filter is constructed in this paper. Since the neural network has the ability of distributed storage and fast self-evolution, use Hopfield neural network to implement adaptive filter LMS algorithm in this filter so as to improve the speed of operation. The simulation results prove that, the new filter can achieve rapid real-time denoising.

The Hyper H∞ Filter is an adaptive filtering algorithm, where a forgetting factor ρ is built into the H∞ optimization framework as a function of the robustness parameter γf an...

A high-performance implementation scheme for a least mean square adaptive filter is presented. The architecture is based on distributed arithmetic in which the partial products of filter coefficients are precomputed and stored in lookup tables (LUTs) and the filtering is done by shift-and-accumulate operations on these partial products. In the case of an adaptive filter, it is required that the filter coefficients be updated and, hence, these LUTs are to be recalculated. A new strategy based on the offset binary coding scheme has been proposed in order to update these LUTs from time to time. Simulation results show that the proposed scheme consumes very less chip area and operates at high throughput for large base unit size k ( = N/m) , where m is an integer and N is the number of filter coefficients. For example, a 128-tap finite-impulse-response adaptive filter with the proposed implementation produces 12 times more throughput (for k = 8) and consumes almost 26% less area when compared to the best of existing architectures.

In practical application, signal and noise statistical characteristics are often unknown or not informed, fixed filter is difficult to process these signals and the filtering effect is poor. To address the issue, this paper design the adaptive filter which is based on the algorithm LMS and RLS. By simulation in the MATLAB environment, we can compare the analytical results of the two algorithms and observe the effect of noise elimination, the adaptive filter can be a good noise suppression.

The evolution of multi-feature portable devices with high speed processors and with drastic growth in component density turns the designer attention towards power aware design schemes. In low power VLSI designs an adaptive filter can obtain a reduction in terms of area and power consumption. A system with a linear transfer function controlled by variable parameters and a means to adjust those parameters according to an optimization algorithm is called adaptive filter. The Least Mean Square (LMS) filter is one of adaptive filters type which is used commonly, because of its simplicity and also because of its satisfactory convergence performance. The current IIR adaptive filter uses LMS to reduce area-delay product and energy-delay product. To reduce this delay one can implement filter in pipelined structure. Shift-add tree efficiently minimizes the critical path and silicon area without increasing the number of adaptation delays. The structure of IIR adaptive filter designing is done by using two main blocks: IIR block and new coefficients block (weights block). Weights block consists of series of partial product generators and shift/add tree. Partial product generators has 2 to 3 decoders and AND/OR cells. Weights block performs multiply accumulate operations. Filter block depends upon on the new filter coefficients obtaining from weights block. The proposed filter is designed in MATLAB (2013a) for its performance characteristics and its constraints are verified using XILINX (verl4.7) implemented on FPGA.

The paper proposed the adapter filter design based on the improved LMS algorithm. The paper first introduced the minimum mean square (LMS) algorithm, which is a very useful and very simple estimated gradient method. This algorithm has been widely used since the early 1960s quickly, its advantage is that the small amount of calculation, and the rapid development of the digital signal processor also allows real - time adaptive filter economy realize it possible. For the design of SVC, the adaptive filter design is outstanding performed in the operation. The paper implemented on the DSP LMS adaptive filter algorithm and related technologies for echo cancellation applications do simple research and discussion.

A novel adaptive transversal filter modeling is presented for the hysteresis characteristic in PEA (piezoelectric actuator). First, the hysteresis modeling capability of delay operator based adaptive transversal filter is explored. On this basis, delay operators of adaptive transversal filter are replaced with Backlash operators to compose a new serial structure of adaptive transversal filter model, and LMS (least mean square) algorithm is used to adjust the weight values. And then, as LMS algorithm brings a serious confrontation of the convergence speed and steady-state offset, a new improved adaptive method focus on the convergence factor is brought in. The new filter with this improved algorithm makes the contradiction of convergence speed and steady-state offset could be unified to some extent. At last, the two adaptive transversal filters are applied to model for hysteresis characteristic of PEA, and the modeling effect is verified via a micro-positioning system experiment platform based on PEA. Experimental results show that the proposed Backlash operator based adaptive filter can achieve accurate hysteresis modeling, and the effectiveness of the improved adaptive algorithm is also demonstrated.

Compared with traditional median filter, the filter performance of adaptive median filter has been improved at the cost of high computation complexity. An improved adaptive median filter algorithm is proposed in this paper. First, the filter window size is determined according to the distance between the valid pixels and the center pixels in the proposed algorithm, which can avoid the waste of pixels repeated sort in window expand process. Second, the improved algorithm only to take the median value from valid pixels within the window, effectively weakening the interference with noise point, it will improve the quality of image. Compared with the original algorithm, this proposed algorithm reduced the complexity of algorithm and improved effective of noise reduction, PSNR value average increases 10dB. Finally, this paper applied the algorithm on noise reduction with depth image captured from Kinect.

This paper proposes an adaptation algorithm named Adaptive Step-Size q-Normalized Least Mean Modulus-Newton Algorithm (ASS-qNLMM-NewtonA) in which the normalizing factor is a generalized norm called “q-norm” of the filter input. Two types of impulse noise are considered: one is found in observation noise and another at filter input. Analysis of the ASS-qNLMM-NewtonA is developed to theoretically calculate filter convergence behavior. Through experiments we find that the steady-state excess mean square error takes the minimum value when q is infinity. We also demonstrate that the proposed algorithm is effective in improving the convergence speed, while preserving the robustness against both types of impulse noise. Good agreement between simulated and theoretical convergence curves shows the validity of the analysis.

An adaptive filter is a digital filter that self adjusts its transfer function according to an optimizing algorithm which is most frequently Least Mean Square (LMS) algorithm. Due to the complexity of adaptive filtering most digital filters are FIR filter. There are numerous applications of adaptive filters like noise cancellations, echo cancellation, system identification, inverse system modeling, adaptive beam-forming etc. In this research article, adaptive LMS algorithm has been used for unknown system identification. The system identification is a category of adaptive filtering which find its numerous applications in diverse field like communication, image processing, speech processing etc.

The Modified LMS adaptive filter is used achieving low adaption delay and less area and low power By using error computational block and weight update block in modified Mean Square Filter. Conventional LMS adaptive filter is not suitable for achieving this product values. In delayed LMS adaptive filter we use pipelined architecture. In paper we implement LMS Adaptive filter uses in EEG technology for low adaption delay and low area and compare the values.

International audience; This work aims at implementing an asynchronous FIR adaptive filter, based on the Recursive Inverse (RI) adaptive algorithm. Previous work has presented the proposed adaptive filter algorithm and has shown that the algorithm's performance is similar to that of the Recursive Least Squares (RLS) algorithm. Moreover, it offers better performance than the Transform Domain (TD) algorithms, i.e. the TD LMS with Variable Step-Size (TDVSS) in stationary environments. The asynchronous logic has been chosen because of its unique low-power characteristic towards stationary events. The asynchronous-based architecture has been designed to be fast enough to accommodate the iterative computation of the filter coefficients while being accurate to ensure a minimum number of iteration, and a fast convergence. This paper presents an overview of the proposed architecture, as well as performance comparison between the RI and the RLS algorithm. Preliminary test shows promising results, nevertheless some optimization is required to reduce the complexity of the design and to increase the accuracy of the computation.

It is well known that the volterra filter is an efficient adaptive filter or the nonlinear system, but its computational complexity is very high, especially as the orders of the filter increase. To overcome the computational complexity of the Volterra filter, a novel adaptive filter using layered bilinear architecture is proposed in this paper. Compared with the conventional second-order Volterra filter, the layered bilinear adaptive filter exhibits a slightly better performance in terms of convergence speed and steady-state error.

In this paper, an adaptive temporal and spatial filter (ATSF) based on mean square error (MSE) estimation is proposed. ATSF is a block based de-noising algorithm. Each noisy block is selectively filtered by a temporal filter or a spatial filter. Multi-hypothesis motion compensated filter (MHMCF) and bilateral filter are chosen as the temporal filter and the spatial filter, respectively. Although there is no original video, we mathematically derivate a formular to estimate the real MSE between a block de-noised by MHMCF and its original block and a linear model is proposed to estimate the real MSE between a block de-noised by bilateral filter and its original block. Finally, each noisy block is processed by the filter with a smaller estimated MSE. Simulation results show that our proposed algorithm achieves substantial improvements in terms of both visual quality and PSNR as compared with the conventional de-noising algorithms.

Frequency-domain (FD) adaptive filter algorithms are able to achieve a low computational complexity by using the overlap-and-save implementation means compared to time-domain (TD) ones. In this article, we propose a new FD least-mean-square (FD-LMS) algorithm which dynamically selects frequency bins in order to reduce the computational complexity and maintain the convergence performance of the conventional FD-LMS. The optimal selection of frequency bins is derived by the largest decrease between the successive FD mean square deviations (MSDs) at every data block. Simulation results show that the proposed algorithm provides a low steady-state normalized MSD (NMSD) and similar convergence rate compared to the conventional FD-LMS algorithm. In addition, it gains a low computational complexity.

This paper describes a Filter Window Characteristics based fast adaptive median filter algorithm. Experimental results from the point of view, this adaptive median filter algorithm to determine the degree of pollution on the image to adjust adaptive filtering process, while maintaining the average speed median filter algorithm based on filtering noise effectively, and better protection of the image the edge of the details.

In this letter, we propose a new prediction block filter that can reduce errors between the original and prediction blocks. The proposed filter adaptively adjusts filter coefficients by using the previously reconstructed adjacent blocks and their prediction blocks. Then, the filter is selectively applied to the current prediction block according to the rate-distortion optimization. Moreover, since the same filter coefficients can be derived in the decoder, they are not encoded into the bit-stream. The proposed method achieves a 4.65% bitrate saving on average compared with H.264/AVC.

In this paper, we proposed an image noise canceller achieved by an adaptive filter in two-dimensional block processing based on the least-mean-square algorithm. In this adaptive filter, each image is processed in two phases. In the initial weight matrix decision phase, the block-by-block operations with the smaller block size of 4 × 4 are applied to the original noisy image for getting the suitable weight matrix that will be used as the initial one for the block-adaptation phase such that a higher signal-to-noise ratio can be achieved. To verify the feasibility of this approach, the simulations in the block-adaptation phase with the block sizes of 4 × 4, 8 × 8, 16 × 16, and 32 × 32 are performed. The simulation results show that this approach achieves a higher signal-to-noise ratio in each case of block size. Index Terms—Adaptive filter, noise cancellation, least- mean-square

Decision feedback equalizers are commonly employed to reduce the intersymbol interference that is caused by the time dispersive channel.In this paper a block based partial update normalized LMS algorithm is proposed,which significantly reduces the computational complexity over the other LMS based algorithms.The important characteristic of this algorithm is that only a part of the filter coefficients are updated in every iteration.The frequency domain representation facilitates, easier to choose step size with which the proposed algorithm convergent in the mean squared sense, whereas in the time domain it requires the information on the largest eigen value of the correlation matrix of the input sequence. Simulation studies shows that the proposed realization gives good performance characteristic in terms of convergence rate.

The Complex Block Least Mean Square (LMS) technique is widely used in adaptive filtering applications because of its simplicity and efficiency from a theoretical and implementation standpoint. However, the limitations of the Complex Block LMS technique are slow convergence and dependence on the proper choice of the stepsize or convergence factor. Moreover, its performance degrades significantly in time-varying environments. In this paper, a novel adaptive LMS technique named the Complex Block Conjugate LMS algorithm, CBC-LMS, is presented. Based on the Conjugate Gradient Principle, the proposed technique searches orthogonal directions to update the filter coefficients instead of the negative gradient directions used in the Complex Block LMS algorithm. In addition, the CBC-LMS algorithm derives optimal stepsizes to adjust the adaptive system coefficients at each iteration. As a result, the developed method overcomes the inherent limitations of the existing Complex Block LMS algorithm. The performance of the CBC-LMS technique is tested in wireless channel estimation and equalization applications, using both computer simulations and laboratory experiments. Furthermore, the developed technique is compared to the Complex Block LMS method and a recently proposed method, which is called Complex Optimal Block Adaptive LMS (OBA-LMS). The experimental and simulation results confirm that the proposed CBC-LMS technique achieves faster convergence with comparable accuracy and reduced computational complexity, relative to the existing techniques.

The active power filter has two key link for harmonic current detection and respectively compensation current tracking. This article mainly aims at harmonic current detection link. Firstly, Analysis adaptive harmonic detection methods, then based on over to LMS (Least Mean Square) adaptive algorithm as the research object, in discussing the adaptive algorithm criteria. By comparing several simulation-based LMS algorithm to achieve the results of the filter,analyse the causes of the results.

In this paper the principle of adaptive filter and various least mean square (LMS) adaptive filter algorithm is studied, based on the related hyperbolic tangent function LMS algorithm is presented, referred to as CTanh-LMS algorithm. Simulation results show that, compared with other adaptive filter algorithm, this method has better denoising ability, and the algorithm is simple, fast convergence rate, and can satisfy the gearbox vibration signal denoising requirements. The proposed algorithm can not only solve the gearbox fault feature extraction, and give adaptive filter algorithm research provides a new means, has important theoretical significance and practical value.

The paper introduces the principle and structure of adaptive filter based on LMS algorithm, studies a design scheme of a single frequency adaptive notch filter, and simulates its working procedure by using the Simulink simulation tool. The simulation results show that the adaptive notch filter based on LMS algorithm has the better convergence and the smaller steady-state error than traditional notch filter in the appropriate parameter.

An improved adaptive Huber filter algorithm is presented to model error and measurement noise uncertainty. The adaptive algorithm for model error is obtained using an upper bound for the state prediction covariance matrix. The measurement noise is estimated at each filter step by minimizing a criterion function which was original from adaptive Huber filter. A recursive algorithm is provided for solving the criterion function. The proposed adaptive filter algorithm was successfully implemented in relative navigation using global position system for spacecraft formation flying in high earth orbits with real orbit perturbations and non-Gaussian random measurement error. Simulation results indicated that the proposed adaptive filter performed better in robustness and accuracy compared with previous adaptive algorithms.