RLS Adaptive Filters Research Articles

Image Interpolation Based on 2D-DWT with Novel Regularity-Preserving Algorithm Using RLS Adaptive Filters

This paper proposes a novel method for the image interpolation problem based on two-dimensional discrete wavelet transform (DWT) with the edge preserving approach. The purpose of this method is to consider two contrasting issues of over-smoothing and creation of spurious edges at the same time, and offer a novel solution based on statistical dependencies of image sub-bands, and noise behavior. The offered method has a multi-faceted approach for the problem; by sub-band coding, it handles each 2D-DWT image sub-band with a different solution. For LH and HL sub-bands, two algorithms work together in order to preserve regularity. Area_Check algorithm is a four-phase edge-preserving algorithm that aims to recognize and interpolate separating lines of environments and edgy regions in the best possible way. On the other hand, RLS_AVG algorithm interpolates smooth surfaces of the image by keeping the regularity of the image without over-smoothing. In this regard, the offered algorithm has a great power to counter jaggies and annoying artifacts. In the end, in order to demonstrate the capability, and performance of the proposed method, the final results in various metrics are compared with the results of the most famous and the newest image interpolation methods.

Cascaded RLS Adaptive Filters Based on a Kronecker Product Decomposition

The multilinear system framework allows for the exploitation of the system identification problem from different perspectives in the context of various applications, such as nonlinear acoustic echo cancellation, multi-party audio conferencing, and video conferencing, in which the system could be modeled through parallel or cascaded filters. In this paper, we introduce different memoryless and memory structures that are described from a bilinear perspective. Following the memory structures, we develop the multilinear recursive least-squares algorithm by considering the Kronecker product decomposition concept. We have performed a set of simulations in the context of echo cancellation, aiming both long length impulse responses and the reverberation effect.

Non-Invasive Fetal Monitoring: A Maternal Surface ECG Electrode Placement-Based Novel Approach for Optimization of Adaptive Filter Control Parameters Using the LMS and RLS Algorithms.

This paper is focused on the design, implementation and verification of a novel method for the optimization of the control parameters (such as step size and filter order N) of LMS and RLS adaptive filters used for noninvasive fetal monitoring. The optimization algorithm is driven by considering the ECG electrode positions on the maternal body surface in improving the performance of these adaptive filters. The main criterion for optimal parameter selection was the Signal-to-Noise Ratio (SNR). We conducted experiments using signals supplied by the latest version of our LabVIEW-Based Multi-Channel Non-Invasive Abdominal Maternal-Fetal Electrocardiogram Signal Generator, which provides the flexibility and capability of modeling the principal distribution of maternal/fetal ECGs in the human body. Our novel algorithm enabled us to find the optimal settings of the adaptive filters based on maternal surface ECG electrode placements. The experimental results further confirmed the theoretical assumption that the optimal settings of these adaptive filters are dependent on the ECG electrode positions on the maternal body, and therefore, we were able to achieve far better results than without the use of optimization. These improvements in turn could lead to a more accurate detection of fetal hypoxia. Consequently, our approach could offer the potential to be used in clinical practice to establish recommendations for standard electrode placement and find the optimal adaptive filter settings for extracting high quality fetal ECG signals for further processing. Ultimately, diagnostic-grade fetal ECG signals would ensure the reliable detection of fetal hypoxia.

A Novel Pipelined Adaptive RLS Filter for ECG Noise Cancellation

Filtering the noise present in ECG signals by adaptive signal processing is the aim of the study. Adaptive digital filters are difficult to pipeline due to the presence of long feedback loops, careful calibration of step size and depth of pipelining. DLMS filters are designed to reduce the adaptation delay in the existing method. However with the LMS algorithm, the resulting rate of convergence is typically an order of magnitude slower than the RLS algorithm. The exponentially weighted RLS algorithm which converges in the mean square sense in about 2M iterations, where M is the number of taps in the transversal filter. The fine-grain structure of RLS and RRLS adaptive filters are designed. Signal to Noise Ratio (SNR) analysis for these filters are performed on a preliminary basis with different structures. Pipelined implementation of these adaptive filters yield higher throughput, higher sample rates and low power designs. The filter structures are designed and simulated in MATLAB SIMULINK. These structures are used for the noise cancellation in ECG signals.

DCD-RLS Adaptive Filters With Penalties for Sparse Identification

In this paper, we propose a family of low-complexity adaptive filtering algorithms based on dichotomous coordinate descent (DCD) iterations for identification of sparse systems. The proposed algorithms are appealing for practical designs as they operate at the bit level, resulting in stable hardware implementations. We introduce a general approach for developing adaptive filters with different penalties and specify it for exponential and sliding window RLS. We then propose low-complexity DCD-based RLS adaptive filters with the lasso, ridge-regression, elastic net, and penalties that attract sparsity. We also propose a simple recursive reweighting of the penalties and incorporate the reweighting into the proposed adaptive algorithms to further improve the performance. For general regressors, the proposed algorithms have a complexity of operations per sample, where is the filter length. For transversal adaptive filters, the algorithms require only operations per sample. A unique feature of the proposed algorithms is that they are well suited for implementation in finite precision, e.g., on FPGAs. We demonstrate by simulation that the proposed algorithms have performance close to the oracle RLS performance.

Statistical Approach for Noise Removal in Speech Signals Using LMS, NLMS, Block LMS and RLS Adaptive filters

  Abstract— In this paper the application of different adaptive filters in removing the noise present in the speech signals is presented. To analyze the performance of different adaptive filter family members, the parameters like convergence, output PSNR and CPU consumption time are considered. Results show that NLMS filter shows the better performance in CPU time consumption and output PSNR. Block LMS has the highest Convergence factor among all the members of the adaptive filter family.

Multichannel fast QRD-RLS adaptive filtering: Block-channel and sequential-channel algorithms based on updating backward prediction errors

Fast QR decomposition recursive least-squares (FQRD-RLS) algorithms are well known for their fast convergence and reduced computational complexity. A considerable research effort has been devoted to the investigation of single-channel versions of the FQRD-RLS algorithms, while the multichannel counterparts have not received the same attention. The goal of this paper is to broaden the study of the efficient and low complexity family of multichannel RLS adaptive filters, and to offer new algorithm options. We present a generalized approach for block-type multichannel FQRD-RLS (MC-FQRD-RLS) algorithms that includes both cases of equal and multiple order. We also introduce new versions for block-channel and sequential-channel processing, details of their derivations, and a comparison in terms of computational complexity. The proposed algorithms are based on the updating of backward a priori and a posteriori error vectors, which are known to be numerically robust.

Characterisation of adaptive filters used in the identification process of annoying noises in vehicles

To identify annoying noises (squeaks and rattles) inside vehicle cabins, it is necessary to capture the sounds present inside them and eliminate or attenuate all external noises from the obtained signal by using LMS, RLS or Kalman adaptive filters. This work aims to characterise the performance of these filters and select the one most suitable for this application. Analytical and experimental work was developed to characterise the performance of these filters in terms of filtering capacity and filtering quality. Additionally, a subjective evaluation was performed asking a group of users about the filter that best reproduces well-defined noises when they are recorded from moving vehicles. Results showed that the RLS filter is the most suitable. Additionally, it was found that for low frequency input signals all the filters show the lowest filtering capacity.

Extended rls lattice adaptive filters

This paper solves the problem of developing exact fast weighted RLS lattice adaptive filters for input signals induced by general orthonormal filter models. The resulting algorithm can be viewed as a counterpart of the extended fast fixed-order RLS adaptive filters previously derived.

Extended fast fixed-order RLS adaptive filters

The existing derivations of conventional fast RLS adaptive filters are intrinsically dependent on the shift structure in the input regression vectors. This structure arises when a tapped-delay line (FIR) filter is used as a modeling filter. We show, unlike what original derivations may suggest, that fast fixed-order RLS adaptive algorithms are not limited to FIR filter structures. We show that fast recursions in both explicit and array forms exist for more general data structures, such as orthonormally based models. One of the benefits of working with orthonormal bases is that fewer parameters can be used to model long impulse responses.

Annihilation-reordering look-ahead pipelined CORDIC-based RLS adaptive filters and their application to adaptive beamforming

The novel annihilation-reordering look-ahead technique is proposed as an attractive technique for pipelining of Givens rotation (or CORDIC)-based adaptive filters. Unlike the existing relaxed look-ahead, the annihilation-reordering look-ahead does not depend on the statistical properties of the input samples. It is an exact look-ahead based on CORDIC arithmetic, which is known to be numerically stable. The conventional look-ahead is based on multiply-add arithmetic. The annihilation-reordering look-ahead technique transforms an orthogonal sequential adaptive filtering algorithm into an equivalent orthogonal concurrent one by creating additional concurrency in the algorithm. Parallelism in the transformed algorithm is explored and different implementation styles including pipelining, block processing, and incremental block processing are presented. Their complexities are also studied and compared. The annihilation-reordering look-ahead is employed to develop fine-grain pipelined QR decomposition-based RLS adaptive filters. Both QRD-RLS and inverse QRD-RLS algorithms are considered. The proposed pipelined architectures can be operated at arbitrarily high sample rate without degrading the filter convergence behavior. Stability under finite-precision arithmetic are studied and proved for the proposed architectures. The pipelined CORDIC-based RLS adaptive filters are then employed to develop high-speed linear constraint minimum variance (LCMV) adaptive beamforming algorithms. Both QR decomposition-based minimum variance distortionless response (MVDR) realization and generalized sidelobe canceller (GSC) realization are presented. The complexity of the pipelined architectures are analyzed and compared. The proposed architectures can be operated at arbitrarily high sample rate and consist of only Givens rotations, which can be scheduled onto CORDIC arithmetic-based processors.

Statistical analysis of initialization methods for RLS adaptive filters

Theoretical analysis is used to evaluate the mean and second-moment properties of recursive least squares algorithms incorporating the fast exact initialization and soft constrained initialization methods during the initialization period. It is shown that the weight vector mean and covariance produced by fast exact initialization are undefined for this period. Theoretical results are derived for soft constrained initialization that show that the weight vector mean and covariance are finite, and expressions are given for these quantities. Simulations for various cases are presented to support the accuracy of these theoretical results. >

Convergence of the RLS and LMS adaptive filters

The paper presents new convergence results for two adaptive filters: the RLS and LMS algorithms. Convergence of the exact RLS algorithm is studied when the forgetting factor \lambda is constant, which enables the adaptive filter to track time variations of the optimal filter. It is shown that, in the steady state, the squared deviation of the adaptive filter from the optimal one admits, with probability 1- \epsilon ( \epsilon arbitrarily small), an upper bound that is proportional to the (infinitesimal) quantity \mu = 1 - {\lambda} . This result agrees with the algorithm's practical behavior. The bound increases with the correlation degree of the filter inputs. This paper also provides an almost sure convergence result concerned with the LMS algorithm with decreasing step-size (infinite memory), used only when the optimal filter is asymptotically time-invariant, although the input statistics may be time-varying.