TY - CONF AB - In this paper, we consider the Maximum Likelihood (ML) estimation of the parameters of a GAUSSIAN in the presence of censored, i.e., clipped data. We show that the resulting Expectation Maximization (EM) algorithm delivers virtually biasfree and efficient estimates, and we discuss its convergence properties. We also discuss optimal classification in the presence of censored data. Censored data are frequently encountered in wireless LAN positioning systems based on the fingerprinting method employing signal strength measurements, due to the limited sensitivity of the portable devices. Experiments both on simulated and real-world data demonstrate the effectiveness of the proposed algorithms. AU - Hoang, Manh Kha AU - Haeb-Umbach, Reinhold ID - 11816 KW - Gaussian processes KW - Global Positioning System KW - convergence KW - expectation-maximisation algorithm KW - fingerprint identification KW - indoor radio KW - signal classification KW - wireless LAN KW - EM algorithm KW - ML estimation KW - WiFi indoor positioning KW - censored Gaussian data classification KW - clipped data KW - convergence properties KW - expectation maximization algorithm KW - fingerprinting method KW - maximum likelihood estimation KW - optimal classification KW - parameters estimation KW - portable devices sensitivity KW - signal strength measurements KW - wireless LAN positioning systems KW - Convergence KW - IEEE 802.11 Standards KW - Maximum likelihood estimation KW - Parameter estimation KW - Position measurement KW - Training KW - Indoor positioning KW - censored data KW - expectation maximization KW - signal strength KW - wireless LAN SN - 1520-6149 T2 - 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013) TI - Parameter estimation and classification of censored Gaussian data with application to WiFi indoor positioning ER - TY - CONF AB - In this paper we propose to employ directional statistics in a complex vector space to approach the problem of blind speech separation in the presence of spatially correlated noise. We interpret the values of the short time Fourier transform of the microphone signals to be draws from a mixture of complex Watson distributions, a probabilistic model which naturally accounts for spatial aliasing. The parameters of the density are related to the a priori source probabilities, the power of the sources and the transfer function ratios from sources to sensors. Estimation formulas are derived for these parameters by employing the Expectation Maximization (EM) algorithm. The E-step corresponds to the estimation of the source presence probabilities for each time-frequency bin, while the M-step leads to a maximum signal-to-noise ratio (MaxSNR) beamformer in the presence of uncertainty about the source activity. Experimental results are reported for an implementation in a generalized sidelobe canceller (GSC) like spatial beamforming configuration for 3 speech sources with significant coherent noise in reverberant environments, demonstrating the usefulness of the novel modeling framework. AU - Tran Vu, Dang Hai AU - Haeb-Umbach, Reinhold ID - 11913 KW - array signal processing KW - blind source separation KW - blind speech separation KW - complex vector space KW - complex Watson distribution KW - directional statistics KW - expectation-maximisation algorithm KW - expectation maximization algorithm KW - Fourier transform KW - Fourier transforms KW - generalized sidelobe canceller KW - interference suppression KW - maximum signal-to-noise ratio beamformer KW - microphone signal KW - probabilistic model KW - spatial aliasing KW - spatial beamforming configuration KW - speech enhancement KW - statistical distributions T2 - IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2010) TI - Blind speech separation employing directional statistics in an Expectation Maximization framework 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 -