Adaptive Filtering Scheme Research Articles

A novel adaptive nonlinear Kalman filter scheme for DVL-aided SINS alignment in underwater vehicles

The rapid and accurate initial alignment of a strapdown inertial navigation system (SINS) aided by a Doppler velocity logger (DVL) still poses a significant challenge in unmanned underwater vehicle navigation. In this study, a novel adaptive nonlinear filtering alignment scheme is developed for a SINS with a large misalignment angle. In previous studies, the frame-inconsistency problem of the nonlinear SINS error model was neglected. Thus, a modified nonlinear error model of a SINS is derived here rigorously as the process model of the nonlinear Kalman filter. In addition, the noise amplification of applying Sage-Husa adaptive Kalman filter (SHAKF) in alignment is analyzed, and a novel arctangent fading memorial factor is introduced to dynamically adjust the weight of current measurements. Simulations and field experiments indicate that the proposed algorithm is efficient in underwater DVL/SINS in-motion alignment. The alignment accuracy and convergence rate of the modified model are significantly improved compared to those of the traditional model. The arctangent fading memorial SHAKF can reliably estimate the measurement noise and improve the performance of alignment.

Identification and semi-active control of structures with abrupt stiffness degradations

This paper presents for variable stiffness tuned mass dampers (VS-TMDs) a vibration control algorithm combined with a nonlinear system identification method. The objective of the proposed approach is the control of multistory shear frame structures, which exhibit abrupt stiffness degradations. A stiffness variable control (SVC) algorithm is developed to control the stiffness of the VS-TMD, which is based on the mitigation of the potential energy of the structure. For the identification of the system parameters, an adaptive unscented Kalman filter (A-UKF) scheme is utilized. The paper describes the theoretical background of the proposed approach with its governing equations and conducts performance studies on a semi-actively controlled 2-story shear frame structure. Harmonic, white noise and earthquake excitation scenarios including seismic aftershock effects are investigated. Sensitivity studies are conducted. With the proposed approach, the abrupt stiffness changes of the structure are detected and the damper stiffness is adapted successfully. The results show that the SVC together with the A-UKF clearly outperforms the passive TMD control.

Mejora de la cuantificación y armonización cuantitativa en PET mediante filtrado adaptativo

La cuantificación en la tomografía por emisión de positrones (PET) está sujeta a sesgos debido a limitaciones físicas y técnicas. El objetivo de la armonización cuantitativa es lograr mediciones comparables entre diferentes tomógrafos PET, permitiendo así ensayos clínicos multicéntricos. Las guías clínicas, como las de la Asociación Europea de Medicina Nuclear (EANM), recomiendan armonizar las reconstrucciones de PET para llevar los coeficientes de recuperación de contraste (CRC) dentro de las especificaciones. Sin embargo, estas reconstrucciones armonizadas pueden mostrar sesgos cuantitativos. En este trabajo mejoramos la armonización mediante el uso de un novedoso esquema de filtrado adaptativo. Nuestro objetivo fue obtener un sesgo de cuantificación bajo y valores de relación señal / ruido (PSNR) altos al mismo tiempo. Para ello se diseñó e implementó un nuevo filtro de eliminación de ruido adaptativo de tres etapas. Los parámetros del filtro se optimizaron para lograr un PSNR alto en un fantoma cerebral digital y un sesgo cuantitativo bajo de los valores máximos de recuperación de contraste (CRCmax) obtenidos de un fantoma de calidad de imagen de PET de la Asociación Nacional de Fabricantes Eléctricos (NEMA). El fantoma NEMA se escaneó en varios escáneres PET / CT y se reconstruyó sin filtros. La configuración de filtro óptima encontrada para un conjunto de datos de entrenamiento se aplicó luego a las reconstrucciones de prueba de otros escáneres. Los límites de armonización se definieron utilizando los intervalos de confianza del 95% entre reconstrucciones. Como resultado, se lograron valores de CRCmax promedio cercanos a la unidad (± 5%) para esferas con un diámetro igual o superior a 13 mm para el conjunto de datos de entrenamiento. Los valores de PSNR fueron comparables a otros resultados de filtros de última generación. Usando la misma configuración de filtro óptima para los conjuntos de datos de prueba, se encontraron resultados cuantitativos similares. La visibilidad de la lesión mejoró en imágenes clínicas en comparación con las reconstrucciones EANM, sin artefactos visibles. De esta forma, nuestro filtro adaptativo de tres etapas logró un rendimiento cuantitativo de vanguardia para la obtención de imágenes PET. Se lograron tolerancias de armonización con un sesgo y varianza más bajos que las directrices de EANM para una variedad de modelos de escáner. Los valores de CRCmax estaban cerca de la unidad y la variabilidad de cuantificación se redujo en comparación con las reconstrucciones estándar.

Improved PET quantification and harmonization by adaptive denoising

Purpose: Quantification in positron emission tomography (PET) is subject to bias due to physical and technical limitations. The goal of quantitative harmonization is to achieve comparable measurements between different scanners, thus enabling multicenter clinical trials. Clinical guidelines, such as those from the European Association of Nuclear Medicine (EANM), recommend harmonizing PET reconstructions to bring contrast recovery coefficients (CRCs) within specifications. However, these harmonized reconstructions can show quantitative biases. In this work we improve harmonization by using a novel adaptive filtering scheme. Our goal was to obtain low quantification bias and high peak signal to noise ratio (PSNR) values at the same time. Methods: a novel three-stage adaptive denoising filter was implemented. Filter parameters were optimized to achieve both high PSNR in a digital brain phantom and low quantitative bias of maximum CRC values (CRCmax) obtained from a National Electrical Manufacturers Association (NEMA) PET image quality phantom. The NEMA phantom was scanned on several PET/CT scanners and reconstructed without postfilters. The optimal filter settings found for a training dataset were then applied to testing reconstructions from other scanners. Harmonization limits were defined using the 95% confidence intervals across reconstructions. Results: Average CRCmax values close to unity (± 5%) were achieved for spheres with diameter equal or greater than 13 mm for the training dataset. PSNR values were comparable to other state-of-the-art filter results. Using the same optimal filter settings for the testing datasets, similar quantitative results were found. Lesion conspicuity was improved on clinical scans when compared with EANM reconstructions, with no visible artifacts. Conclusions: Our three-stage adaptive filter achieved state-of-the-art quantitative performance for PET imaging. Harmonization tolerances with lower bias and variance than EANM guidelines were achieved for a variety of scanner models. CRCmax values were close to unity and the quantification variability was reduced when compared with standard reconstructions.

A Machine Learning Framework for Pulse Detection During Out-of-Hospital Cardiac Arrest

The availability of an automatic pulse detection during out-of-hospital cardiac arrest (OHCA) would allow the rapid identification of cardiac arrest and the prompt detection of return of spontaneous circulation. The aim of this study was to develop a reliable pulse detection algorithm using the electrocardiogram (ECG) and thoracic impedance (TI), the signals available in most defibrilators. The dataset used in the study consisted of 1140 ECG and TI segments from 187 OHCA patients, whereof 792 were labelled as pulse-generating rhythm (PR) and 348 as pulseless electrical activity (PEA) by a pool of experts in OHCA. First, an adaptive filtering scheme was used to extract the impedance circulation component and its first derivative from the TI. Then, the wavelet decomposition of the ECG was carried out to obtain the different subband components and the denoised ECG. Pulse/no-pulse (PR/PEA) discrimination features were extracted from those signals and fed into a support vector machine (SVM) classifier that made the pulse/no-pulse decision. A quasi-stratified and patient wise nested cross validation procedure was used to select the best feature subset and to tune the SVM hyperparameters. This procedure was repeated 50 times to estimate the statistical distributions of the performance metrics of the method. The optimal solution consisted in a five feature classifier that yielded a mean (standard deviation) sensitivity, specificity, balanced accuracy and total accuracy of 92.4% (0.7), 93.0% (0.8), 92.7% (0.5) and 92.6% (0.5), respectively. When compared to available methods, our solution presented an improvement in balanced accuracy of at least 2.5 points. A reliable pulse detection algorithm for OHCA using the signals available in defibrillators was acomplished.

An adaptive Kalman filter for extreme polarization effects equalization in coherent optical communication system

The specially designed Kalman filter was proved to be competent to complete the polarization equalization in some extreme environments with the large time-varying polarization mode dispersion (PMD) and the ultrafast rotation of state of polarization (RSOP). But the performance of this kind of Kalman filter is strongly restricted by the initial noise statistics represented by the process noise covariance Q and the measurement noise covariance R which will limit its effectiveness in an actual scene. To solve this problem, this paper proposes an adaptive Kalman filter structure by an adjustable way of estimating the process noise covariance Q and the measurement noise covariance R based on the measurement residual in order to be adaptive to the time-varying environments. The performance of the proposed adaptive Kalman scheme is checked both by the simulation and experiment on the 28 Gbaud PDM-QPSK/16QAM coherent optical communication system platforms. The simulation and experiment results demonstrate that the proposed adaptive Kalman filter scheme has a stable and better performance of the equalization for large PMD combined with ultrafast RSOP, with a greater tolerance the to time-varying extreme polarization environments than that using the extended Kalman filter scheme adopted in our previous work.

A fast adaptive resonance suppression scheme for high-damping system using R-SDFT detection

ABSTRACTAdaptive notch filter resonance suppression scheme is widely used in high-performance servo drivers. For high-damping system, the deviation between resonant and oscillation frequency will cause a long sustained vibration in traditional suppression schemes, which may damage the precision machine irreparably. To reduce the harm of sustained vibration, a fast adaptive notch filter scheme based on range-shifted discrete Fourier transform (R-SDFT) is proposed. Different from the traditional scheme, the new one eliminates the deviation between resonant and oscillation frequency by notch filter other than the low-pass filter, which massively simplifies the suppression process and reduces the number of oscillation frequency detection required. Meanwhile, an oscillation detection method based on R-SDFT is presented to shorten the time of the detective process through interval detection and prediction strategy. Compared with the traditional strategy, the experimental results demonstrate that the proposed adaptive suppression scheme can reduce the oscillation time by more than half.

Space-time multichannel adaptive filtering scheme for VLC color cross-talk equalization.

In the emerging framework of visible light communications, the limitation of modulation bandwidth of light emitting diodes (LEDs) transmitters is counterbalanced by the use of red-green-blue (RGB) LEDs since they exhibit higher modulation bandwidth. Using RGB LEDs allows to exploit the spatial multiplexing aspects, that is obtaining parallel channels for transmission. In this context, we used at the receiver RGB-tuned photodiodes (PDs). Hence each PD can, in principle, detect only the information its tuning is matched to. The drawback of increasing spectral efficiency is the presence of cross-talk at the single PD due to the signals emitted by the remaining colors when a perfect color filtering is not applied. In this paper we consider amplitude shift keying (ASK) modulation, investigate the issue of the spatial (color) interference and we present a blind multichannel adaptive equalizer able to mitigate the temporary intersymbol interference (ISI) and also the spatial (color) ISI, this latter partially suppressed using suitable color filters. We analyze also the convergence of the adaptive algorithm and compare its performance with the literature.

Adaptive Kalman filter for polarization and phase recovery in wide ranges of optical signal-to-noise ratio, polarization rotation frequency, and laser linewidth

An adaptive Kalman filter (AKF) scheme for joint polarization and phase recovery is proposed and experimentally demonstrated. In this scheme, the noise covariance is estimated adaptively along with the recursive of KF. The simulation results show that the tuning parameter Q can adaptively converge to an optimal value in wide ranges of optical signal-to-noise ratio (OSNR), polarization rotation frequency, and laser linewidth. As a result, the proposed AKF has less OSNR sensitivity penalty, better polarization tracking capability, and laser linewidth tolerance compared with extended Kalman filter (EKF) with fixed Q value. Finally, in the experiment of 16 quadrature amplitude modulation system, AKF also shows a better polarization tracking capability than the EKF.

Gradient-Based Parameter Estimation in Pairwise Linear Gaussian System

This technical note addresses the gradient-based parameter estimation problem for pairwise linear Gaussian systems. The new adaptive filtering scheme is based on gradient-based optimization methods for estimating the uncertain system parameters and a robust square-root variant of the pairwise Kalman filter used for estimating the unknown states of the pairwise linear Gaussian systems. Hence, in the adaptive filtering techniques, the dynamic state and system parameters are estimated simultaneously. The method of the filter sensitivities computation required in gradient evaluation is derived in terms of square-root factors of covariance matrices.

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.

Adaptive Receive Processing of Spatially Modulated Physical Radar Emissions

Inspired by the fixational movements of the human eye, fast-time spatial modulation was recently demonstrated as a particular physically realizable form of a multiple-input multiple-output (MIMO) radar emission. The attendant coupling of the delay and angle dimensions has been shown to provide a modest improvement in spatial separation, even when using non-adaptive pulse compression and beamforming. Here this continuous emission paradigm is appropriately discretized and a joint delay-angle adaptive filtering strategy is developed that exploits the physical waveform-diverse emission structure to realize significant enhancement in target separability.

Constructing numerically stable Kalman filter‐based algorithms for gradient‐based adaptive filtering

SummaryThis paper addresses the numerical aspects of adaptive filtering (AF) techniques for simultaneous state and parameters estimation arising in the design of dynamic positioning systems in many areas of research. The AF schemes consist of a recursive optimization procedure to identify the uncertain system parameters by minimizing an appropriate defined performance index and the application of the Kalman filter (KF) for dynamic positioning purpose. The use of gradient‐based optimization methods in the AF computational schemes yields to a set of the filter sensitivity equations and a set of matrix Riccati‐type sensitivity equations. The filter sensitivities evaluation is usually carried out by the conventional KF, which is known to be numerically unstable, and its derivatives with respect to unknown system parameters. Recently, a novel square‐root approach for the gradient‐based AF by the method of the maximum likelihood has been proposed. In this paper, we show that various square‐root AF schemes can be derived from only two main theoretical results. This elegant and simple computational technique replaces the standard methodology based on direct differentiation of the conventional KF equations (with their inherent numerical instability) by advanced square‐root filters (and its derivatives as well). As a result, it improves the robustness of the computations against round off errors and leads to accurate variants of the gradient‐based AFs. Additionally, such methods are ideal for simultaneous state estimation and parameter identification because all values are computed in parallel. The numerical experiments are given. Copyright © 2015 John Wiley & Sons, Ltd.

An adaptive unscented Kalman filter for tracking sudden stiffness changes

The paper presents an approach to track sudden changes in stiffness of structural systems exposed to earthquake induced base excitations. Such sudden changes in the stiffness could be caused by abrupt damage of one or more structural members. To track such changes through a Kalman filter approach, the stiffness and damping coefficients of structural members to be tracked need to be a part of the state vector of a state space model. However, such state equations become nonlinear even for an otherwise linear system. The use of the unscented transform-based Kalman filter approach has been considered to effectively deal with such nonlinearities in state estimation. But this approach not intended to track sudden changes is unable to achieve this. Herein, an adaptive Kalman filter scheme is proposed for efficient detection as well as tracking of sudden changes in stiffness values. The approach first identifies the instant of a sudden change, followed by appropriate adjustment of the state covariance matrix for efficient tracking of the states. Numerical examples of structural models with several earthquake inputs with different characteristics are used to show that the proposed scheme can effectively track multiple events of sudden stiffness changes in several structural members occurring at different time instances.

Filtro Híbrido Adaptativo en Cascada para Arreglos Lineales Uniformes de Antenas

This paper proposes a new adaptive hybrid filter scheme based on the least mean square and the recursive least square algorithms for uniform linear antenna arrays to generate radiation beam patterns. The individual adaptive blocks used to form the new filter are interconnected by the linear array factor to simulate a virtual antenna array and to set the desired direction of the main beam pattern. The completed formulation of the new filter is detailed and its capability to minimize interference signals and adaptive white gaussian noise is evaluated by simulation and compared with other filters. The simulation results showed that the convergence speed and the filtering characteristics of the new model are better or similar to those that make it. In conclusion, the new scheme can generate the main beam pattern to the desired signal direction.

An Efficient Adaptive Filtering Scheme Based on Combining Multiple Metrics

SUMMARY We propose a novel adaptive filtering scheme named metric-combining normalized least mean square (MC-NLMS). The proposed scheme is based on iterative metric projections with a metric designed by combining multiple metric-matrices convexly in an adaptive manner, thereby taking advantages of the metrics which rely on multiple pieces of information. We compare the improved PNLMS (IPNLMS) algorithm with the natural proportionate NLMS (NPNLMS) algorithm, which is a special case of MC-NLMS, and it is shown that the performance of NPNLMS is controllable with the combination coefficient as opposed to IPNLMS. We also present an application to an acoustic echo cancellation problem and show the efficacy of the proposed scheme.

Hybrid Filters based Denoising of Medical Images using Adaptive Wavelet Thresholding Algorithm

new techniques are developed within the previous couple of years that convalesce results on spacial filters by take away the noise additional with success whereas protective the sides within the information. a trifle of those techniques used the background from partial differential equations and process fluid dynamics like level set strategies, non-linear isotropous and anisotropic diffusion. A little range of techniques pooled impulse removal filters with native adaptive filtering within the rework domain to require out not solely white and mixed noise, however additionally their mixtures. so as to diminish the noise gift in medical pictures several techniques area unit procurable like digital filters (FIR or IIR), adaptive filtering strategies etc. nonetheless, digital filters and adaptive strategies are often applied to signals whose applied math characteristics area unit stationary in several cases. currently the moving ridge rework has been incontestable to be great tool for non-stationary signal analysis. we have a tendency to take PSNR and MSE as a potency issue to envision the effectiveness of planned denoising

Efficient edge-directed deblocking algorithm for KOMPSAT-2 satellite images

In this paper, we present a deblocking algorithm that removes grid and staircase artefacts, which result from the compression of images. The KORea Multi-Purpose SATellite-2 images are efficiently compressed using an algorithm that assigns quantisation coefficients, by rows, according to local region complexity, in such a way that more complicated regions are more compressed. However, this approach is problematic in that it produces blocking artefacts in those relatively less complicated areas within the same row as the complicated regions. Removing these artefacts with a general deblocking algorithm can, undesirably, blur the complicated regions as well as the artefacts; additionally, the general filter is poor at preserving curved edges. Therefore, the algorithm proposed herein presents an adaptive filtering scheme for removing blocking artefacts while preserving image details, including curved edges, using a content analysis. Particularly, edge adaptive filtering has been introduced to remove staircase artefacts around the big image edges. Experimental analyses revealed that the proposed method outperforms several state-of-the-art approaches in terms of subjective or objective image quality, and computational speed.

A Reliable Multi-Sensor Navigation System for an Unmanned Aerial Vehicle Realized through Integration of SINS with GPS, CNS and Doppler Radar

Contemporary importance of the unmanned aerial vehicle (UAV) both for military and civilian applications has prompted vigorous research related with guidance, navigation and control of these vehicles. The potential civilian uses for small low-cost UAVs are various like reconnaissance, surveillance, rescue and search, remote sensing, traffic monitoring, destruction appraisal of natural disasters, etc. One of the most crucial parts of UAVs missions is accurate navigation of the vehicle, i.e. the real time determination of its position, velocity and attitude. Generally highly accurate Strap down Inertial Navigation Systems (SINS) are too heavy to be flown on UAVs. Moreover highly accurate SINS are also highly expensive. Therefore the low-cost and low weight MEMS based SINS with a compromised precision are the viable option for navigation of UAVs. The errors in position, velocity, and attitude solutions provided by the MEMS based SINS grow unboundedly with the passage of time. To contain these growing errors, integrated navigation is the resolution. Complementary characteristics SINS and external non-inertial navigation aids like Global Positioning System (GPS), Celestial Navigation System (CNS) and Doppler radar make the integrated navigation system an appealing and cost effective solution. The non-inertial sensors providing navigation fixes must have low weight and volume to be suitable for UAV application. In this research work GPS, CNS and Doppler radar are used as external navigation aids for SINS. The navigation solutions of all contributing systems are fused using Federated Kalman Filter (FKF). Three local filters are employed for SINS/GPS, SINS/CNS and SINS/Doppler integration and subsequently information from all three local filters is fused to acquire a global solution. Moreover adaptive and fault tolerant filtering scheme has also been implemented in each local filter to isolate or accommodate any undesirable error or noise. Simulation for the presented architecture has validated the effectiveness of the scheme, by showing a substantial precision improvement in the solutions of position, velocity and attitude.

Mode dependent loop filter for intra prediction coding in H.264/AVC

In this paper, a high performance and low complexity loop filter is proposed for intra prediction coding. Although the deblocking loop filter (DLF) has achieved outstanding performance on suppressing quantization noise, it also induces details information loss because of the smoothing operation. To achieve better restoration performance, we propose a filter set named mode dependent loop filter (MDLF) which adaptively select the filter coefficients according to various local characteristics. In the homogeneous areas, the task of the filter emphasizes on smoothing the noise. In the heterogeneous areas, the proposed filter concentrates on preserving the details. Based on the spatial correlation assumption and statistical analysis, the intra mode combination is used to classify the training samples with different local characteristics. Then the classical least mean square error framework is employed to solve the coefficients for the proposed filter set. In this way, a more efficient adaptive loop filter scheme can be achieved for specific intra mode combination. Experiment results show that the proposed loop filter achieves superior coding gains compared to the H.264/AVC High Profile. Furthermore, relative to QALF+DLF, a comparable performance also can be achieved by the proposed MDLF with far less complexity increase.