Traditional Least Mean Square Research Articles

Dispersed-sparsity-aware LMS algorithm for scattering-sparse system identification

We develop a least-mean-squares (LMS) algorithm to identify scattering-sparse systems, where the few significantly large coefficients of the unknown impulse response are dispersed along the full length, instead of grouped into clusters. Many wireless communication channels such as the underwater acoustic channels, cellular communication channels and aviation channels are typical scattering-sparse systems. In the proposed strategy, the difference between the ℓ1-norm and ℓ∞,1-norm of the uniformly-divided tap-weight vector of the adaptive filter is utilized as a penalty and is introduced into the mean-square-error cost function, where the ℓ∞,1-norm of the tap-weight vector is used to locate the unknown large coefficients in view of the characteristics of dispersed sparsity. A dispersed-sparsity-aware LMS (DS-LMS) algorithm is then proposed by following the stochastic subgradient method. We study the mean and mean-square behaviors of the proposed algorithm. We also provide theoretical guidelines for the parameter settings that ensure that the proposed DS-LMS algorithm converges to a lower mean-square-deviation (MSD) level than the traditional LMS algorithm. Simulation results verify the effectiveness of the proposed DS-LMS algorithm, and correctness of the theoretical findings.

A novel Discrete Artificial Bee Colony algorithm combined with adaptive filtering to extract Fetal Electrocardiogram signals

The Fetal Electrocardiogram (FECG) signal plays a crucial role in monitoring the health of the fetus, but there are numerous challenges in eliminating the maternal thorax signal and reducing noise interference. This paper proposes a novel objective function that combines a Least Mean Squares (LMS) adaptive filter with a heuristic algorithms to enhance the quality of the extracted FECG signal. To achieve better results, we introduce the Discrete Artificial Bee Colony (DABC) algorithm with a new initialization strategy, a random wavelet basic function strategy, and Gaussian distribution. These improvements enhance global search capabilities and ensure a faster convergence rate. The application of heuristic algorithms can reduce noise signals and provides clearer and more accurate results compared to the traditional LMS filter. Furthermore, the effectiveness of this innovative algorithm is compared with other widely used heuristic algorithms. The experiment results demonstrate that the novel algorithm significantly enhances performance by up to 8% compared to other conventional extraction methods in some indicators.

The RFI Fast Mitigation Algorithm Based on Block LMS Filter

The radio telescope possesses high sensitivity and strong signal collection capabilities. While receiving celestial radiation signals, it also captures Radio Frequency Interferences (RFIs) introduced by human activities. RFI, as signals originating from sources other than the astronomical targets, significantly impacts the quality of astronomical data. This paper presents an RFI fast mitigation algorithm based on block Least Mean Square (LMS) algorithm. It enhances the traditional adaptive LMS filter by grouping L adjacent time-sampled points into one block and applying the same filter coefficients for filtering within each block. This transformation reduces multiplication calculations and enhances algorithm efficiency by leveraging the time-domain convolution theorem. The algorithm is tested using baseband data from the Parkes 64 m radio telescope’s pulsar observations and simulated data. The results confirm the algorithm’s effectiveness, as the pulsar profile after RFI mitigation closely matches the original pulsar profile.

Design of causal preconditioning filters for adaptive filtering algorithms in real-time multi-channel active noise control applications

Least-mean-square (LMS) algorithm has been widely used in the area of active noise control (ANC). One practical concern of LMS-based algorithms is its slow convergence rate in multi-channel broadband noise control applications. To improve the convergence speed of traditional LMS algorithm, preconditioning filters were added to the LMS system in previous studies to remove the correlation between the reference signals and decouple the plant responses. However, the preconditioning filters implemented previously are usually obtained through singular value decomposition of the cross spectral matrix of reference signals and the plant response matrix, which does not lead to causal preconditioning filters and, thus, it can only be applied in spatial audio applications when delay is not an important concern, but cannot be implemented in real-time active noise control applications. In the current work, a method is proposed to obtain a casual multi-channel preconditioning filter for real-time active noise control applications through a numerically robust algorithm to perform a spectral factor decomposition to the reference signal cross spectral matrix and a minimum-phase and all-pass decomposition to the multi-channel secondary path. Simulation results show that, by applying the proposed preconditioning filter, the convergence speed of the LMS algorithm can be significantly improved.

Performance of Beamforming for Smart Antenna using Traditional LMS Algorithm for Various Parameters

Adaptive signal processing sensor arrays, known also as smart antennas .The smart antenna adaptive algorithms achieve the best weight vector for beam forming by iterative means. The Least Mean Square (LMS) algorithm, is an adaptive algorithm .LMS incorporates an iterative procedure that makes successive corrections to the weight vector in the direction of the negative of the gradient vector which eventually leads to the minimum mean square error. Beam forming is directly determined by the two factors. The performance of the traditional LMS algorithm for different parameters is analysed in this paper. This algorithm can be applied to beam forming with the software Matlab. The result obtain can achieve faster convergence and lower steady state error. The algorithms can be simulated in MATLAB 7.10 version.

Special cascade LMS equalization scheme suitable for 60-GHz RoF transmission system.

We design a specific cascade least mean square (LMS) equalizer and to the best of our knowledge, it is the first time this kind of equalizer has been employed for 60-GHz millimeter-wave (mm-wave) radio over fiber (RoF) system. The proposed cascade LMS equalizer consists of two sub-equalizers which are designated for optical and wireless channel compensations, respectively. We control the linear and nonlinear factors originated from optical link and wireless link separately. The cascade equalization scheme can keep the nonlinear distortions of the RoF system in a low degree. We theoretically and experimentally investigate the parameters of the two sub-equalizers to reach their best performances. The experiment results show that the cascade equalization scheme has a faster convergence speed. It needs a training sequence with a length of 10000 to reach its stable status, which is only half as long as the traditional LMS equalizer needs. With the utility of a proposed equalizer, the 60-GHz RoF system can successfully transmit 5-Gbps BPSK signal over 10-km fiber and 1.2-m wireless link under forward error correction (FEC) limit 10-3. An improvement of 4dBm and 1dBm in power sensitivity at BER 10-3 over traditional LMS equalizer can be observed when the signals are transmitted through Back-to-Back (BTB) and 10-km fiber 1.2-m wireless links, respectively.

Robust modeling of acoustic paths using a sparse adaptive algorithm

Acoustic impulse response functions are generally sparse in nature and traditionally these are modeled by adaptive finite impulse response (FIR) filters trained using a least mean square (LMS) algorithm. The conventional LMS algorithm is not effective in modeling sparse systems and sparse LMS algorithms have been recently developed to improve the modeling in such scenarios. However, the traditional sparse LMS algorithms are not robust to disturbances at the error sensor and may diverge in some scenarios. With an objective to overcome this limitation of conventional sparse adaptive algorithm, this paper presents a robust sparse adaptive algorithm. The new algorithm has been shown to effectively model sparse systems in a robust manner. In addition, the algorithm has been successfully applied in modeling the acoustic feedback path in a behind the ear digital hearing aid.

Block-Sparsity-Induced Adaptive Filter for Multi-Clustering System Identification

In order to improve the performance of least mean square (LMS)-based adaptive filtering for identifying block-sparse systems, a new adaptive algorithm called block-sparse LMS (BS-LMS) is proposed in this paper. The basis of the proposed algorithm is to insert a penalty of block-sparsity, which is a mixed \$l_{2, 0}\$ norm of adaptive tap-weights with equal group partition sizes, into the cost function of traditional LMS algorithm. To describe a block-sparse system response, we first propose a Markov-Gaussian model, which can generate a kind of system responses of arbitrary average sparsity and arbitrary average block length using given parameters. Then we present theoretical expressions of the steady-state misadjustment and transient convergence behavior of BS-LMS with an appropriate group partition size for white Gaussian input data. Based on the above results, we theoretically demonstrate that BS-LMS has much better convergence behavior than \$l_0\$-LMS with the same small level of misadjustment. Finally, numerical experiments verify that all of the theoretical analysis agrees well with simulation results in a large range of parameters.

FIR System Identification Using Feedback

This paper describes a new approach to finite-impulse (FIR) system identification. The method differs from the traditional stochastic approximation method as used in the traditional least-mean squares (LMS) family of algorithms, in which we use deconvolution as a means of separating the impulse-response from the system input data. The technique can be used as a substitute for ordinary LMS but it has the added advantages that can be used for constant input data (i.e. data which are not persistently exciting) and the stability limit is far simpler to calculate. Furthermore, the convergence properties are much faster than LMS under certain types of noise input.

Gradient optimization p-norm-like constraint LMS algorithm for sparse system estimation

In order to improve the sparsity exploitation performance of norm constraint least mean square (LMS) algorithms, a novel adaptive algorithm is proposed by introducing a variable p-norm-like constraint into the cost function of the LMS algorithm, which exerts a zero attraction to the weight updating iterations. The parameter p of the p-norm-like constraint is adjusted iteratively along the negative gradient direction of the cost function. Numerical simulations show that the proposed algorithm has better performance than traditional l0 and l1 norm constraint LMS algorithms.

Adaptive inverse control of random vibration based on the filtered-X LMS algorithm

Random vibration control is aimed at reproducing the power spectral density (PSD) at specified control points. The classical frequency-spectrum equalization algorithm needs to compute the average of the multiple frequency response functions (FRFs), which lengthens the control loop time in the equalization process. Likewise, the feedback control algorithm has a very slow convergence rate due to the small value of the feedback gain parameter to ensure stability of the system. To overcome these limitations, an adaptive inverse control of random vibrations based on the filtered-X least mean-square (LMS) algorithm is proposed. Furthermore, according to the description and iteration characteristics of random vibration tests in the frequency domain, the frequency domain LMS algorithm is adopted to refine the inverse characteristics of the FRF instead of the traditional time domain LMS algorithm. This inverse characteristic, which is called the impedance function of the system under control, is used to update the drive PSD directly. The test results indicated that in addition to successfully avoiding the instability problem that occurs during the iteration process, the adaptive control strategy minimizes the amount of time needed to obtain a short control loop and achieve equalization.

Digital LMS adaptation of analog filters without gradient information

The least mean square (LMS) algorithm has practical problems in the analog domain mainly due to DC offset effects. If digital LMS adaptation is used, a digitizer (analog-to-digital converter or comparator) is required for each gradient signal as well as the filter output. Furthermore, in some cases the state signals are not available anywhere in the analog signal path necessitating additional analog filters. Here, techniques for digitally estimating the gradient signals required for the LMS adaptation of analog filters are described. The techniques are free from DC offset effects and do not require access to the filter's internal state signals. Digitizers are required only on the input and error signal. The convergence rate and misadjustment are identical to traditional LMS adaptation, but an additional matrix multiplication is required for each iteration. Hence, analog circuit complexity is reduced but digital circuit complexity is increased with no change in overall performance making it an attractive option for mixed-signal integrated systems in digital CMOS. Signed and subsampled variations of the adaptive algorithm can provide a further reduction in analog and digital circuit complexity, but with a slower convergence rate. Theoretical analyses, behavioral simulations, and experimental results from an integrated filter are all presented.

Functional Languages in Signal Processing Applied to Prosthetic Limb Control

This article describes how one can use functional languages to develop a dedicated system for controlling a prosthetic arm. It shows the prototype artificial limb along with the development of the various algorithms and software used to process electromyographic (EMG) signals, to be used as inputs for the control mechanism. Great emphasis is also laid on the parametric modelling used to extract the necessary features from the EMG signals. An iterative least mean square (LMS) algorithm has been used to improve the efficiency of traditional LMS algorithms, which greatly enhanced the performance of the system. The use of the functional paradigm lead to a fast developing stage and a very compact set of programs that will run as fast as (sometimes even faster than) traditional C/C++ programs.

Sign-sign LMS convergence with independent stochastic inputs

The sign-sign adaptive least-mean-square (LMS) identifier filter is a computationally efficient variant of the LMS identifier filter. It involves the introduction of signum functions in the traditional LMS update term. Consideration is given to global convergence of parameter estimates offered by this algorithm, to a ball with radius proportional to the algorithm step size for white input sequences, specially from Gaussian and uniform distributions. >