Kalman Smoother Approach Research Articles

Glottal Airflow Estimation using Neck Surface Acceleration and Low-Order Kalman Smoothing.

The use of non-invasive skin accelerometers placed over the extrathoracic trachea has been proposed in the literature for measuring vocal function. Glottal airflow is estimated using inverse filtering or Bayesian techniques based on a subglottal impedance-based model when utilizing these sensors. However, deviations in glottal airflow estimates can arise due to sensor positioning and model mismatch, and addressing them requires a significant computational load. In this paper, we utilize system identification techniques to obtain a low order state-space representation of the subglottal impedance-based model. We then employ the resulting low order model in a Kalman smoother to estimate the glottal airflow. Our proposed approach reduces the model order by 94% and requires only 1.5% of the computing time compared to previous Bayesian methods in the literature, while achieving slightly better accuracy when correcting for glottal airflow deviations. Additionally, our Kalman smoother approach provides a measure of uncertainty in the airflow estimate, which is valuable when measurements are taken under different conditions. With its comparable accuracy in signal estimation and reduced computational load, the proposed approach has the potential for real-time estimation of glottal airflow and its associated uncertainty in wearable voice ambulatory monitors using neck-surface acceleration.

Application of expectation maximization and Kalman smoothing for prognosis of lumen maintenance life for light emitting diodes

Light emitting diodes (LED) have found widespread use for lighting and displays in recent times due to their high efficiency, favorable form factor, impact resistance, robustness, reliability and prolonged lifetime. The extended times to failure of LEDs make the problem of lumen maintenance life prediction for LED light sources using traditional reliability-based failure data collection and analysis methods quite challenging. Therefore, one has to resort to using prognostic approaches such as Kalman or particle filters applied to real time degradation data to make inferences on the remaining useful life (RUL) of such devices. The standard prognostic approach for predicting lumen maintenance life relies on an underlying degradation model with predetermined parameters from previous stress tests. However, these model parameters may vary quite a bit from device to device and for different manufacturing batch lots, thereby leading to large prediction errors. To address this issue, it would be better to learn the model parameters from the current measurement data directly. This study aims to achieve this by estimating the parameter values using the Expectation Maximization (EM) algorithm. Kalman smoothing is then applied to the identified parameter values for predicting the lumen maintenance life of the LED. The accuracy of the EM – Kalman smoothing approach was tested and compared with the standard nonlinear least square (NLS) approach prescribed by the TM-21 standard as well as the standard particle filter approach (without EM). Our results show that the EM method can give better, if not, similar RUL prediction accuracy with respect to the NLS and standard particle filter (PF) algorithms. Moreover, the accuracy of RUL prediction using our proposed algorithm is insensitive to the choice of the initial model parameter values, which paves way for this algorithm to be used in practice for automated predictive analytics of degrading electromechanical systems.

A BP–MF–EP Based Iterative Receiver for Joint Phase Noise Estimation, Equalization, and Decoding

In this work, with combined belief propagation (BP), mean field (MF) and expectation propagation (EP), an iterative receiver is designed for joint phase noise (PN) estimation, equalization and decoding in a coded communication system. The presence of the PN results in a nonlinear observation model. Conventionally, the nonlinear model is directly linearized by using the first-order Taylor approximation, e.g., in the state-of-the-art soft-input extended Kalman smoothing approach (soft-in EKS). In this work, MF is used to handle the factor due to the nonlinear model, and a second-order Taylor approximation is used to achieve Gaussian approximation to the MF messages, which is crucial to the low-complexity implementation of the receiver with BP and EP. It turns out that our approximation is more effective than the direct linearization in the soft-in EKS with similar complexity, leading to significant performance improvement as demonstrated by simulation results.

Analyzing Precious Metals Returns Using a Kalman Smoother Approach

This study analyzes the sensitivity of different external factors to the returns of the precious metals of gold, silver, platinum and palladium. The goal is to find similarities and differences between the dependencies of every factor to each metal as well as study the sensitivities in a time-varying framework. For that, a brief co-integration test for the precious metals is conducted followed by a Kalman smoother approach to study the sensitivities. All results point to strong time-dependencies, such as a declining relationship of gold to equity volatility. Consistent strong relationships can be identified for the CPI and the dollar while market data such as equity prices or bond yields only selectively relate to the precious metals. A Dynamic Time Warping approach finally compares sensitivities for pairs of precious metals to a specific factor and finds higher similarities between platinum and palladium compared to other pairs.

Locally-adaptive Kalman smoothing approach to identification of nonstationary stochastic systems

The problem of noncausal identification of nonstationary, linear stochastic systems, i.e., identification based on prerecorded input/output data, is considered. It is shown how several competing Kalman-type parameter smoothers, differing in smoothness constraints and memory settings, can be combined together to yield a better and more reliable smoothing algorithm. The resulting parallel estimation scheme automatically adjusts its smoothing bandwidth to the unknown, and possibly time-varying, rate of nonstationarity of the identified system. It also allows one to account for parameter jumps and for the distribution of measurement noise.

Learning using state space kernel machines

AbstractReconstruction of a function from noisy data is often formulated as a regularized optimization problem whose solution closely matches an observed data set and also has a small reproducing kernel Hilbert space norm. The loss functions that measure agreement between the data and the function are often smooth (e.g. the least squares penalty), but non-smooth loss functions are of interest in many applications. Using the least squares penalty, large machine learning problems with kernels amenable to a stochastic state space representation (which we call state space kernel machines) have been solved using a Kalman smoothing approach. In this paper we extend this approach for state space kernel machines to the Vapnik penalty, a particularly important non-smooth penalty that is robust to outlier noise in the measurements and induces a sparse representation of the reconstructed function. We exploit the structure of such models using interior point methods that efficiently solve the functional recovery problem with the Vapnik penalty, and demonstrate the effectiveness of the method on a numerical experiment. The computational effort of our approach scales linearly with the number of data points.

Real-time smoothing of car-following data through sensor-fusion techniques

Observation of vehicles kinematics is an important task for many applications in ITS (Intelligent Transportation Systems). It is at the base of both theoretical analyses and application developments, especially in case of positioning and tracing/tracking of vehicles, car-following analyses and models, navigation and other ATIS (Advanced Traveller Information Systems), ACC (Adaptive Cruise Control) systems, CAS and CWS (Collision Avoidance Systems and Collision Warning Systems) and other ADAS (Advanced Driving Assistance Systems). Modern technologies supply low-cost devices able to collect time series of kinematic and positioning data with medium to very high frequency. Even more data can be (almost continually) collected if vehicle-to-vehicle (V2V) communications come true. However, some of the ITS applications (as well as car-following models, on which many ADAS and ACC are based) require highly accurate measures or, at least, smooth profiles of collected data. Unfortunately, even relatively high-cost devices can collect biased data because of many technical reasons and often this bias could lead to unrealistic kinematics, incorrect absolute positioning and/or inconsistencies between vehicles (e.g. negative spacing). As a consequence, data need filtering in most of the ITS applications. To this aim proper algorithms are required and several sensors and sources of data possibly integrated in order to obtain the maximum quality at the minimal cost. This work addresses the previous issues by developing a specific Kalman smoothing approach. The approach is developed in order to deal with car-following conditions but is conceived to take into account also navigation issues. The performances are analysed with respect to real-world car-following data, voluntarily biased for evaluation purposes. Assessment is carried out with reference to different mixtures of sensors and different sensors accuracies.

Error sources and algorithms for white-light fringe estimation at low light levels.

We address the problem of highly accurate phase estimation at low light levels, as required by the Space Interferometry Mission (SIM). The most stringent SIM requirement in this regard is that the average phase error over a 30-s integration time correspond to a path-length error of approximately 30 pm. Most conventional phase-estimation algorithms exhibit significant enough bias at the signal levels at which the SIM will be operating so that some correction is necessary. Several algorithms are analyzed, and methods of compensating for their bias are developed. Another source of error in phase estimation occurs because the phase is not constant over the integration period. Errors that are due to spacecraft motion, the motion of compensating optical elements, and modulation errors are analyzed and simulated. A Kalman smoothing approach for compensating for these errors is introduced.