On adaptive filter structure and performance

SIGLEAvailable from British Library Document Supply Centre- DSC:D75686/87 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Robust RLS Wiener Fixed-Interval Smoother in Linear Discrete-Time Stochastic Systems with Uncertain Parameters

This paper proposes the robust RLS Wiener filter and fixed-interval smoothing algorithms based on the innovation approach. As a result, the robust RLS Wiener filtering algorithm is same as the existing robust RLS Wiener filtering algorithm. The estimation accuracy of the fixed-interval smoother is compared with the robust RLS Wiener filter and the following fixed-interval smoothers. In the proposed robust RLS Wiener fixed-interval smoother, the case, where the observed value is replaced with the robust filtering estimate of the signal, is also simulated. (1) The RLS Wiener fixed-interval smoother in which the filtering estimate of the state is replaced with the robust RLS Wiener filtering estimate. (2) The RTS (Rauch-Tung-Striebel) fixed-interval smoother in which the filtering estimate of the state is replaced with the robust RLS Wiener filtering estimate. (3) The  RLS Wiener fixed-interval smoother and the  RLS Wiener filter. (4) The RLS Wiener fixed-interval smoother in which the filtering estimate of the state is replaced with the robust RLS Wiener filtering estimate and the observed value is replaced with the robust RLS Wiener filtering estimate of the signal. From the simulation results, the most feasible estimation technique for the fixed-interval smoothing estimate is the RLS Wiener fixed-interval smoother. Here, the robust filtering estimate is used and the observed value is replaced with the robust filtering estimate

Blind adaptive constrained reduced-rank parameter estimation based on constant modulus design for CDMA interference suppression

This paper proposes a multistage decomposition for blind adaptive parameter estimation in the Krylov subspace with the code-constrained constant modulus (CCM) design criterion. Based on constrained optimization of the constant modulus cost function and utilizing the Lanczos algorithm and Arnoldi-like iterations, a multistage decomposition is developed for blind parameter estimation. A family of computationally efficient blind adaptive reduced-rank stochastic gradient (SG) and recursive least squares (RLS) type algorithms along with an automatic rank selection procedure are also devised and evaluated against existing methods. An analysis of the convergence properties of the method is carried out and convergence conditions for the reduced-rank adaptive algorithms are established. Simulation results consider the application of the proposed techniques to the suppression of multiaccess and intersymbol interference in DS-CDMA systems

Adaptive interference suppression for DS-CDMA systems based on interpolated FIR filters with adaptive interpolators in multipath channels

In this work we propose an adaptive linear receiver structure based on interpolated finite impulse response (FIR) filters with adaptive interpolators for direct sequence code division multiple access (DS-CDMA) systems in multipath channels. The interpolated minimum mean-squared error (MMSE) and the interpolated constrained minimum variance (CMV) solutions are described for a novel scheme where the interpolator is rendered time-varying in order to mitigate multiple access interference (MAI) and multiple-path propagation effects. Based upon the interpolated MMSE and CMV solutions we present computationally efficient stochastic gradient (SG) and exponentially weighted recursive least squares type (RLS) algorithms for both receiver and interpolator filters in the supervised and blind modes of operation. A convergence analysis of the algorithms and a discussion of the convergence properties of the method are carried out for both modes of operation. Simulation experiments for a downlink scenario show that the proposed structures achieve a superior BER convergence and steady-state performance to previously reported reduced-rank receivers at lower complexity

Spatio-temporal prediction of wind fields

Short-term wind and wind power forecasts are required for the reliable and economic operation of power systems with significant wind power penetration. This thesis presents new statistical techniques for producing forecasts at multiple locations using spatiotemporal information. Forecast horizons of up to 6 hours are considered for which statistical methods outperform physical models in general. Several methods for producing hourly wind speed and direction forecasts from 1 to 6 hours ahead are presented in addition to a method for producing five-minute-ahead probabilistic wind power forecasts. The former have applications in areas such as energy trading and defining reserve requirements, and the latter in power system balancing and wind farm control. Spatio-temporal information is captured by vector autoregressive (VAR) models that incorporate wind direction by modelling the wind time series using complex numbers. In a further development, the VAR coefficients are replaced with coefficient functions in order to capture the dependence of the predictor on external variables, such as the time of year or wind direction. The complex-valued approach is found to produce accurate speed predictions, and the conditional predictors offer improved performance with little additional computational cost. Two non-linear algorithms have been developed for wind forecasting. In the first, the predictor is derived from an ensemble of particle swarm optimised candidate solutions. This approach is low cost and requires very little training data but fails to capitalise on spatial information. The second approach uses kernelised forms of popular linear algorithms which are shown to produce more accurate forecasts than their linear equivalents for multi-step-ahead prediction. Finally, very-short-term wind power forecasting is considered. Five-minute-ahead parametric probabilistic forecasts are produced by modelling the predictive distribution as logit-normal and forecasting its parameters using a sparse-VAR (sVAR) approach. Development of the sVAR is motivated by the desire to produce forecasts on a large spatial scale, i.e. hundreds of locations, which is critical during periods of high instantaneous wind penetration.Short-term wind and wind power forecasts are required for the reliable and economic operation of power systems with significant wind power penetration. This thesis presents new statistical techniques for producing forecasts at multiple locations using spatiotemporal information. Forecast horizons of up to 6 hours are considered for which statistical methods outperform physical models in general. Several methods for producing hourly wind speed and direction forecasts from 1 to 6 hours ahead are presented in addition to a method for producing five-minute-ahead probabilistic wind power forecasts. The former have applications in areas such as energy trading and defining reserve requirements, and the latter in power system balancing and wind farm control. Spatio-temporal information is captured by vector autoregressive (VAR) models that incorporate wind direction by modelling the wind time series using complex numbers. In a further development, the VAR coefficients are replaced with coefficient functions in order to capture the dependence of the predictor on external variables, such as the time of year or wind direction. The complex-valued approach is found to produce accurate speed predictions, and the conditional predictors offer improved performance with little additional computational cost. Two non-linear algorithms have been developed for wind forecasting. In the first, the predictor is derived from an ensemble of particle swarm optimised candidate solutions. This approach is low cost and requires very little training data but fails to capitalise on spatial information. The second approach uses kernelised forms of popular linear algorithms which are shown to produce more accurate forecasts than their linear equivalents for multi-step-ahead prediction. Finally, very-short-term wind power forecasting is considered. Five-minute-ahead parametric probabilistic forecasts are produced by modelling the predictive distribution as logit-normal and forecasting its parameters using a sparse-VAR (sVAR) approach. Development of the sVAR is motivated by the desire to produce forecasts on a large spatial scale, i.e. hundreds of locations, which is critical during periods of high instantaneous wind penetration

Adaptive estimation and equalisation of the high frequency communications channel

SIGLEAvailable from British Library Document Supply Centre- DSC:D94945 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Motion artifact reduction in PPG signals

The aim of this thesis was to investigate methods for artifact removal in PPG signals and to implement and evaluate a few existing algorithms claiming that the amplitude information is recovered when removing motion artifacts from photoplethysmographic signals (PPG) captured from pulse oximeters. We developed a new proposed method that uses a two-stage based approach with singular value decomposition and fixed fast ICA algorithm in order to generate a PPG-correlated reference signal that is used in adaptive noise cancellation. The results were promising and our proposed method is easy to implement and converges quickly with good extraction performance. It has a few design parameters and only needs the estimated period of the PPG signal. Our method could be used in a clinical routine for prediction of intradialytic hypotension. However it should be mentioned that although our method has great potential the simulations were only conducted on two healthy males. Further studies on a larger dataset might be needed in order to establish a full value of the efficacy of our method.Felaktiga mätresultat vid användning av pulsoximeter under patientövervakning En felaktig diagnos är ju inget kul att få av sin läkare. I sjukhusmiljö samt kliniska omgivningar eller under akuttransport kan pulsoximetern, som bland annat mäter syremättnaden i blodet via fingret, ge felaktiga mätresultat på grund av frivilliga eller ofrivilliga rörelser hos patienten. Under de senaste åren har biomedicinsk teknologi ökat drastiskt för mer effektiva behandlingar samt tillförlitliga diagnoser. För att få kliniskt korrekta mätningar från medicinsk utrustning måste dessa apparater vara optimerade på bästa sätt. Detta kommer att underlätta för sjukvårdspersonalen att dra korrekta slutsatser vid beslut under patient övervakning. En patient med t.ex. njursvikt får problem med rening av restprodukter och avlägsnandet av vatten från blodet, vilket är njurarnas uppgift i huvudsak. Vid hemodialys behandling pumpas blodet ut ur kroppen via nålar för att därefter renas i en dialysator som ska ersätta njurarnas funktion. En vanlig biverkning till följd av behandlingen är blodtrycksfall (intradialytisk hypotoni) vilket sker i 25% av alla behandlingar. Resultat från tidigare forskning visar att man kan prediktera blodtrycksfall i samband med hemodialys behandling med hjälp av amplituden hos fotopletysmografi (PPG) signal. PPG signalen fås av pulseoximetern som har en klämma man kan fästa på fingertoppen. Genom att ljus av två våglängder passerar huden kan man med hjälp av absorptionen i blodet avläsa syremättnad och hjärtpuls. Problemet med PPG signalen är att om patienten rör sig påverkar detta amplituden. Därför är det viktigt att ta bort effekten av rörelser på ett sådant sätt att amplitudinformationen är bevarad. Vi undersökte metoder för borttagning av dessa effekter från rörelser hos patienten och föreslog en ny metod som på ett effektivt sätt estimerar en ren PPG signal med amplitudinformationen bevarad. Metoden har ett fåtal designparameterar och konvergerar snabbt mot lösningen. Vår metod skulle kunna användas i en klinisk rutin för prediktering av intradialytisk hypotoni i samband med hemodialys behandling. Det bör dock nämnas att vår studie utfördes på två friska testpersoner och att mer data hade krävts för en fullskalig utvärdering av metoden