TY - CONF AB - State estimation when only a partial model of a considered system is available remains a major challenge in many engineering fields. This work proposes a joint, square-root unscented Kalman filter to estimate states and model uncertainties simultaneously by linear combinations of physics-motivated library functions. Using a sparsity promoting approach, a selection of those linear combinations is chosen and thus an interpretable model can be extracted. Results indicate a small estimation error compared to a traditional square-root unscented Kalman filter and exhibit the enhancement of physically meaningful models. AU - Götte, Ricarda-Samantha AU - Timmermann, Julia ID - 34171 IS - 1 KW - joint estimation KW - unscented transform KW - Kalman filter KW - sparsity KW - data-driven KW - compressed sensing T2 - 12th IFAC Symposium on Nonlinear Control Systems (NOLCOS 2022) TI - Estimating States and Model Uncertainties Jointly by a Sparsity Promoting UKF VL - 56 ER - TY - CONF AB - Low-quality models that miss relevant dynamics lead to major challenges in modelbased state estimation. We address this issue by simultaneously estimating the system’s states and its model inaccuracies by a square root unscented Kalman filter (SRUKF). Concretely, we augment the state with the parameter vector of a linear combination containing suitable functions that approximate the lacking dynamics. Presuming that only a few dynamical terms are relevant, the parameter vector is claimed to be sparse. In Bayesian setting, properties like sparsity are expressed by a prior distribution. One common choice for sparsity is a Laplace distribution. However, due to disadvantages of a Laplacian prior in regards to the SRUKF, the regularized horseshoe distribution, a Gaussian that approximately features sparsity, is applied instead. Results exhibit small estimation errors with model improvements detected by an automated model reduction technique. AU - Götte, Ricarda-Samantha AU - Timmermann, Julia ID - 44326 IS - 2 KW - joint estimation KW - unscented Kalman filter KW - sparsity KW - Laplacian prior KW - regularized horseshoe KW - principal component analysis T2 - IFAC-PapersOnLine TI - Approximating a Laplacian Prior for Joint State and Model Estimation within an UKF VL - 56 ER - TY - JOUR AB - While increasing digitalization enables multiple advantages for a reliable operation of technical systems, a remaining challenge in the context of condition monitoring is seen in suitable consideration of uncertainties affecting the monitored system. Therefore, a suitable prognostic approach to predict the remaining useful lifetime of complex technical systems is required. To handle different kinds of uncertainties, a novel Multi-Model-Particle Filtering-based prognostic approach is developed and evaluated by the use case of rubber-metal-elements. These elements are maintained preventively due to the strong influence of uncertainties on their behavior. In this paper, two measurement quantities are compared concerning their ability to establish a prediction of the remaining useful lifetime of the monitored elements and the influence of present uncertainties. Based on three performance indices, the results are evaluated. A comparison with predictions of a classical Particle Filter underlines the superiority of the developed Multi-Model-Particle Filter. Finally, the value of the developed method for enabling condition monitoring of technical systems related to uncertainties is given exemplary by a comparison between the preventive and the predictive maintenance strategy for the use case. AU - Bender, Amelie ID - 25046 IS - 10 JF - Machines KW - prognostics KW - RUL predictions KW - particle filter KW - uncertainty consideration KW - Multi-Model-Particle Filter KW - model-based approach KW - rubber-metal-elements KW - predictive maintenance SN - 2075-1702 TI - A Multi-Model-Particle Filtering-Based Prognostic Approach to Consider Uncertainties in RUL Predictions VL - 9 ER - TY - CONF AU - Polevoy, Gleb AU - Trajanovski, Stojan AU - Grosso, Paola AU - de Laat, Cees ID - 17652 KW - flow KW - filter KW - MMSA KW - set cover KW - approximation KW - local ratio algorithm SN - 978-3-319-71150-8 T2 - Combinatorial Optimization and Applications: 11th International Conference, COCOA 2017, Shanghai, China, December 16-18, 2017, Proceedings, Part I TI - Filtering Undesirable Flows in Networks ER - TY - CONF AB - Application of prognostics and health management (PHM) in the field of Proton Exchange Membrane (PEM) fuel cells is emerging as an important tool in increasing the reliability and availability of these systems. Though a lot of work is currently being conducted to develop PHM systems for fuel cells, various challenges have been encountered including the self-healing effect after characterization as well as accelerated degradation due to dynamic loading, all which make RUL predictions a difficult task. In this study, a prognostic approach based on adaptive particle filter algorithm is proposed. The novelty of the proposed method lies in the introduction of a self-healing factor after each characterization and the adaption of the degradation model parameters to fit to the changing degradation trend. An ensemble of five different state models based on weighted mean is then developed. The results show that the method is effective in estimating the remaining useful life of PEM fuel cells, with majority of the predictions falling within 5\% error. The method was employed in the IEEE 2014 PHM Data Challenge and led to our team emerging the winner of the RUL category of the challenge. AU - Kimotho, James Kuria AU - Meyer, Tobias AU - Sextro, Walter ID - 9879 KW - ageing KW - particle filtering (numerical methods) KW - proton exchange membrane fuel cells KW - remaining life assessment KW - PEM fuel cell prognostics KW - PHM KW - RUL predictions KW - accelerated degradation KW - adaptive particle filter algorithm KW - dynamic loading KW - model parameter adaptation KW - prognostics and health management KW - proton exchange membrane fuel cells KW - remaining useful life estimation KW - self-healing effect KW - Adaptation models KW - Data models KW - Degradation KW - Estimation KW - Fuel cells KW - Mathematical model KW - Prognostics and health management T2 - Prognostics and Health Management (PHM), 2014 IEEE Conference on TI - PEM fuel cell prognostics using particle filter with model parameter adaptation ER - TY - CONF AB - In this paper we present a robust location estimation algorithm especially focused on the accuracy in vertical position. A loosely-coupled error state space Kalman filter, which fuses sensor data of an Inertial Measurement Unit and the output of a Global Positioning System device, is augmented by height information from an altitude measurement unit. This unit consists of a barometric altimeter whose output is fused with topographic map information by a Kalman filter to provide robust information about the current vertical user position. These data replace the less reliable vertical position information provided the GPS device. It is shown that typical barometric errors like thermal divergences and fluctuations in the pressure due to changing weather conditions can be compensated by the topographic map information and the barometric error Kalman filter. The resulting height information is shown not only to be more reliable than height information provided by GPS. It also turns out that it leads to better attitude and thus better overall localization estimation accuracy due to the coupling of spatial orientations via the Direct Cosine Matrix. Results are presented both for artificially generated and field test data, where the user is moving by car. AU - Bevermeier, Maik AU - Walter, Oliver AU - Peschke, Sven AU - Haeb-Umbach, Reinhold ID - 11726 KW - altitude measurement unit KW - barometers KW - barometric altimeter KW - barometric error Kalman filter KW - barometric height estimation KW - direct cosine matrix KW - global positioning system KW - Global Positioning System KW - GPS device KW - height information KW - height measurement KW - inertial measurement unit KW - Kalman filters KW - loosely-coupled error state space Kalman filter KW - loosely-coupled Kalman-filter KW - map matching KW - robust information KW - robust location estimation KW - sensor fusion KW - topographic map information KW - vertical user position T2 - 7th Workshop on Positioning Navigation and Communication (WPNC 2010) TI - Barometric height estimation combined with map-matching in a loosely-coupled Kalman-filter ER - TY - CONF AB - In this paper we present a novel vehicle tracking algorithm, which is based on multi-level sensor fusion of GPS (global positioning system) with Inertial Measurement Unit sensor data. It is shown that the robustness of the system to temporary dropouts of the GPS signal, which may occur due to limited visibility of satellites in narrow street canyons or tunnels, is greatly improved by sensor fusion. We further demonstrate how the observation and state noise covariances of the employed Kalman filters can be estimated alongside the filtering by an application of the Expectation-Maximization algorithm. The proposed time-variant multi-level Kalman filter is shown to outperform an Interacting Multiple Model approach while at the same time being computationally less demanding. AU - Bevermeier, Maik AU - Peschke, Sven AU - Haeb-Umbach, Reinhold ID - 11723 KW - covariance matrices KW - expectation-maximisation algorithm KW - expectation-maximization algorithm KW - global positioning system KW - Global Positioning System KW - GPS KW - inertial measurement unit KW - interacting multiple model approach KW - Kalman filters KW - multilevel sensor fusion KW - narrow street canyons KW - narrow tunnels KW - online parameter estimation KW - parameter estimation KW - road vehicles KW - robust vehicle localization KW - sensor fusion KW - state noise covariances KW - time-variant multilevel Kalman filter KW - vehicle tracking algorithm T2 - 6th Workshop on Positioning Navigation and Communication (WPNC 2009) TI - Robust vehicle localization based on multi-level sensor fusion and online parameter estimation ER - TY - CONF AB - In this paper we present a novel vehicle tracking method which is based on multi-stage Kalman filtering of GPS and IMU sensor data. After individual Kalman filtering of GPS and IMU measurements the estimates of the orientation of the vehicle are combined in an optimal manner to improve the robustness towards drift errors. The tracking algorithm incorporates the estimation of time-variant covariance parameters by using an iterative block Expectation-Maximization algorithm to account for time-variant driving conditions and measurement quality. The proposed system is compared to an interacting multiple model approach (IMM) and achieves improved localization accuracy at lower computational complexity. Furthermore we show how the joint parameter estimation and localizaiton can be conducted with streaming input data to be able to track vehicles in a real driving environment. AU - Bevermeier, Maik AU - Peschke, Sven AU - Haeb-Umbach, Reinhold ID - 11724 KW - computational complexity KW - expectation-maximisation algorithm KW - Global Positioning System KW - inertial measurement unit KW - inertial navigation KW - interacting multiple model KW - iterative block expectation-maximization algorithm KW - Kalman filters KW - multi-stage Kalman filter KW - parameter estimation KW - road vehicles KW - vehicle positioning KW - vehicle tracking T2 - IEEE 69th Vehicular Technology Conference (VTC 2009 Spring) TI - Joint Parameter Estimation and Tracking in a Multi-Stage Kalman Filter for Vehicle Positioning ER - TY - CONF AB - In this paper a switching linear dynamical model (SLDM) approach for speech feature enhancement is improved by employing more accurate models for the dynamics of speech and noise. The model of the clean speech feature trajectory is improved by augmenting the state vector to capture information derived from the delta features. Further a hidden noise state variable is introduced to obtain a more elaborated model for the noise dynamics. Approximate Bayesian inference in the SLDM is carried out by a bank of extended Kalman filters, whose outputs are combined according to the a posteriori probability of the individual state models. Experimental results on the AURORA2 database show improved recognition accuracy. AU - Windmann, Stefan AU - Haeb-Umbach, Reinhold ID - 11939 KW - a posteriori probability KW - AURORA2 database KW - Bayesian inference KW - Bayes methods KW - channel bank filters KW - extended Kalman filter banks KW - hidden noise state variable KW - Kalman filters KW - noise dynamics KW - speech enhancement KW - speech feature enhancement KW - speech feature trajectory KW - switching linear dynamical model approach T2 - IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2008) TI - Modeling the dynamics of speech and noise for speech feature enhancement in ASR ER - TY - CONF AB - In this paper we present a novel channel impulse response estimation technique for block-oriented OFDM transmission based on combining estimators: the estimates provided by a Kalman filter operating in the time domain and a Wiener filter in the frequency domain are optimally combined by taking into account their estimated error covariances. The resulting estimator turns out to be identical to the MAP estimator of correlated jointly Gaussian mean vectors. Different variants of the proposed scheme are experimentally investigated in an EEEE 802.11a-like system setup. They compare favourably with known approaches from the literature resulting in reduced mean square estimation error and bit error rate. Further, robustness and complexity issues are discussed AU - Haeb-Umbach, Reinhold AU - Bevermeier, Maik ID - 11785 KW - bit error rate KW - block-oriented OFDM transmission KW - channel estimation KW - channel impulse response estimation KW - combining estimators KW - error statistics KW - frequency domain estimation KW - Gaussian mean vectors KW - Gaussian processes KW - Kalman filter KW - Kalman filters KW - MAP estimator KW - maximum likelihood estimation KW - OFDM channel estimation KW - OFDM modulation KW - time domain estimation KW - time-frequency analysis KW - Wiener filter KW - Wiener filters T2 - IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2007) TI - OFDM Channel Estimation Based on Combined Estimation in Time and Frequency Domain VL - 3 ER - TY - JOUR AB - Maximizing the output signal-to-noise ratio (SNR) of a sensor array in the presence of spatially colored noise leads to a generalized eigenvalue problem. While this approach has extensively been employed in narrowband (antenna) array beamforming, it is typically not used for broadband (microphone) array beamforming due to the uncontrolled amount of speech distortion introduced by a narrowband SNR criterion. In this paper, we show how the distortion of the desired signal can be controlled by a single-channel post-filter, resulting in a performance comparable to the generalized minimum variance distortionless response beamformer, where arbitrary transfer functions relate the source and the microphones. Results are given both for directional and diffuse noise. A novel gradient ascent adaptation algorithm is presented, and its good convergence properties are experimentally revealed by comparison with alternatives from the literature. A key feature of the proposed beamformer is that it operates blindly, i.e., it neither requires knowledge about the array geometry nor an explicit estimation of the transfer functions from source to sensors or the direction-of-arrival. AU - Warsitz, Ernst AU - Haeb-Umbach, Reinhold ID - 11927 IS - 5 JF - IEEE Transactions on Audio, Speech, and Language Processing KW - acoustic signal processing KW - arbitrary transfer function KW - array signal processing KW - blind acoustic beamforming KW - direction-of-arrival KW - direction-of-arrival estimation KW - eigenvalues and eigenfunctions KW - generalized eigenvalue decomposition KW - gradient ascent adaptation algorithm KW - microphone arrays KW - microphones KW - narrowband array beamforming KW - sensor array KW - single-channel post-filter KW - spatially colored noise KW - transfer functions TI - Blind Acoustic Beamforming Based on Generalized Eigenvalue Decomposition VL - 15 ER - TY - CONF AB - A marginalized particle filter is proposed for performing single channel speech enhancement with a non-linear dynamic state model. The system consists of a particle filter for tracking line spectral pair (LSP) parameters and a Kalman filter per particle for speech enhancement. The state model for the LSPs has been learnt on clean speech training data. In our approach parameters and speech samples are processed at different time scales by assuming the parameters to be constant for small blocks of data. Further enhancement is obtained by an iteration which can be applied on these small blocks. The experiments show that similar SNR gains are obtained as with the Kalman-LM-iterative algorithm. However better values of the noise level and the log-spectral distance are achieved AU - Windmann, Stefan AU - Haeb-Umbach, Reinhold ID - 11943 KW - clean speech training data KW - iterative methods KW - iterative speech enhancement KW - Kalman filter KW - Kalman filters KW - Kalman-LM-iterative algorithm KW - line spectral pair parameters KW - log-spectral distance KW - marginalized particle filter KW - noise level KW - nonlinear dynamic state speech model KW - particle filtering (numerical methods) KW - single channel speech enhancement KW - SNR gains KW - speech enhancement KW - speech samples T2 - IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006) TI - Iterative Speech Enhancement using a Non-Linear Dynamic State Model of Speech and its Parameters VL - 1 ER - TY - CONF AB - For human-machine interfaces in distant-talking environments multichannel signal processing is often employed to obtain an enhanced signal for subsequent processing. In this paper we propose a novel adaptation algorithm for a filter-and-sum beamformer to adjust the coefficients of FIR filters to changing acoustic room impulses, e.g. due to speaker movement. A deterministic and a stochastic gradient ascent algorithm are derived from a constrained optimization problem, which iteratively estimates the eigenvector corresponding to the largest eigenvalue of the cross power spectral density of the microphone signals. The method does not require an explicit estimation of the speaker location. The experimental results show fast adaptation and excellent robustness of the proposed algorithm. AU - Warsitz, Ernst AU - Haeb-Umbach, Reinhold ID - 11930 KW - acoustic filter-and-sum beamforming KW - acoustic room impulses KW - acoustic signal processing KW - adaptive principal component analysis KW - adaptive signal processing KW - architectural acoustics KW - constrained optimization problem KW - cross power spectral density KW - deterministic algorithm KW - deterministic algorithms KW - distant-talking environments KW - eigenvalues and eigenfunctions KW - eigenvector KW - enhanced signal KW - filter-and-sum beamformer KW - FIR filter coefficients KW - FIR filter coefficients KW - FIR filters KW - gradient methods KW - human-machine interfaces KW - iterative estimation KW - iterative methods KW - largest eigenvalue KW - microphone signals KW - multichannel signal processing KW - optimisation KW - principal component analysis KW - spectral analysis KW - stochastic gradient ascent algorithm KW - stochastic processes T2 - IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2005) TI - Acoustic filter-and-sum beamforming by adaptive principal component analysis VL - 4 ER - TY - CONF AB - The paper is concerned with binaural signal processing for a bimodal human-robot interface with hearing and vision. The two microphone signals are processed to obtain an enhanced single-channel input signal for the subsequent speech recognizer and to localize the acoustic source, an important information for establishing a natural human-robot communication. We utilize a robust adaptive algorithm for filter-and-sum beamforming (FSB) and extract speaker direction information from the resulting FIR filter coefficients. Further, particle filtering is applied which conducts a nonlinear Bayesian tracking of speaker movement. Good location accuracy can be achieved even in highly reverberant environments. The results obtained outperform the conventional generalized cross correlation (GCC) method. AU - Warsitz, Ernst AU - Haeb-Umbach, Reinhold ID - 11931 KW - bimodal human-robot interface KW - binaural signal processing KW - enhanced single-channel input signal KW - filter-and-sum beamforming KW - filtering theory KW - FIR filter coefficient KW - generalized cross correlation method KW - microphones KW - microphone signal KW - nonlinear Bayesian tracking KW - particle filtering KW - robust adaptive algorithm KW - robust speaker direction estimation KW - signal processing KW - speech enhancement KW - speech recognition KW - speech recognizer KW - user interfaces T2 - IEEE Workshop on Multimedia Signal Processing (MMSP 2004) TI - Robust speaker direction estimation with particle filtering ER -