Typical iterated filters, such as the iterated extended Kalman filter (IEKF), KF (IUKF), and , have been developed to improve the linearization point (or density) ofthe likelihood linearization in the well-known extended KF (EKF) and unscented KF (UKF). A shortcoming of typical iterated filters is thatthey do not treat the linearization of the transition model of the system. To remedy this shortcoming, we introduce dynamically iterated filters (DIFs), a unified framework for iterated linearization-based nonlinear filters that deals with nonlinearities in both the transition modeland the likelihood, thereby constituting a generalization of the afore mentioned iterated filters. We further establish a relationship between the general DIF and the approximate iterated Rauch–Tung–Striebel smoother. This relationship allows for a Gauss–Newton interpretation, which in turn enables explicit step-size correction, leading to dampedversions of the DIFs. The developed algorithms, both damped and non-damped, are numerically demonstrated in three examples, showing superior mean squared error as well as improved parameter tuning robustness as compared to the analogous standard iterated filters.

This paper presents a sliding window estimation method for simultaneous localization and mapping (SLAM) based on the predictionerror method (PEM). The estimation problem considers landmarks as parameters while treating dynamics using state space models. The gradient needed for parameter estimation is computed recursively using an extended kalman filter. Results from experiments and simulations with a monocular camera and inertial sensors are presented and compared to batch PEM and nonlinear least-squares SLAM estimators. The presented method maintains good accuracy, and its parametrization is well-suited for online implementation, as it scales better with the size of the problem than batch methods.

Hearing-impaired listeners have a reduced ability to selectively attend to sounds of interest amid distracting sounds in everyday environments. This ability is not fully regained with modern hearing technology. A better understanding of the brain mechanisms underlying selective attention during speech processing may lead to brain-controlled hearing aids with improved detection and amplification of the attended speech. Prior work has shown that brain responses to speech, measured with magnetoencephalography (MEG) or electroencephalography (EEG), are modulated by selective attention. These responses can be predicted from the speech signal through linear filters called Temporal Response Functions (TRFs). Unfortunately, these sensor-level predictions are often noisy and do not provide much insight into specific brain source locations. Therefore, a novel method called Neuro-Current Response Functions (NCRFs) was recently introduced to directly estimate linear filters at the brain source level from MEG responses to speech from one talker. However, MEG is not well-suited for wearable and realtime hearing technologies. This work aims to adapt the NCRF method for EEG under more realistic listening environments. EEG data was recorded from a hearing-impaired listener while attending to one of two competing talkers embedded in 16-talker babble noise. Preliminary results indicate that source-localized linear filters can be directly estimated from EEG data in such competing-talker scenarios. Future work will focus on evaluating the current method on a larger dataset and on developing novel methods, which may aid in the improvement of next-generation brain-controlled hearing technology.

This letter investigates relationships between iterated filtering algorithms based on statistical linearization, such as the iterated unscented Kalman filter (IUKF), and filtering algorithms based on quasi–Newton (QN) methods, such as the QN iterated extended Kalman filter (QN–IEKF). Firstly, it is shown that the IUKF and the iterated posterior linearization filter (IPLF) can be viewed as QN algorithms, by finding a Hessian correction in the QN –IEKF such that the IPLF iterate updates are identical to that of the QN–IEKF. Secondly, it is shown that the IPLF/ IUKF update can be rewritten such that it is approximately identical to the QN–IEKF, albeit for an additional correction term. This enables a richer understanding of the properties of iterated filtering algorithms based on statistical linearization.

Introduction: By means of adding more sensor technology, modern hearing aids (HAs) strive to become better, more personalized, and self-adaptive devices that can handle environmental changes and cope with the day-to-day fitness of the users. The latest HA technology available in the market already combines sound analysis with motion activity classification based on accelerometers to adjust settings. While there is a lot of research in activity tracking using accelerometers in sports applications and consumer electronics, there is not yet much in hearing research. Objective: This study investigates the feasibility of activity tracking with ear-level accelerometers and how it compares to waist-mounted accelerometers, which is a more common measurement location. Method: The activity classification methods in this study are based on supervised learning. The experimental set up consisted of 21 subjects, equipped with two XSens MTw Awinda at ear-level and one at waist-level, performing nine different activities. Results: The highest accuracy on our experimental data as obtained with the combination of Bagging and Classification tree techniques. The total accuracy over all activities and users was 84% (ear-level), 90% (waist-level), and 91% (ear-level + waist-level). Most prominently, the classes, namely, standing, jogging, laying (on one side), laying (face-down), and walking all have an accuracy of above 90%. Furthermore, estimated ear-level step-detection accuracy was 95% in walking and 90% in jogging. Conclusion: It is demonstrated that several activities can be classified, using ear-level accelerometers, with an accuracy that is on par with waist-level. It is indicated that step-detection accuracy is comparable to a high-performance wrist device. These findings are encouraging for the development of activity applications in hearing healthcare.

A wearable microphone array platform is used tolocalize stationary sound sources and amplify the sound inthe desired directions using several beamforming methods. Theplatform is equipped with inertial sensors and a magnetometerallowing predictions of source locations during orientationchanges and compensation for the displacement in the arrayconfiguration. The platform is modular, open and 3D printedto allow for easy reconfiguration of the array and for reuse inother applications, e.g., mobile robotics. The software componentsare based on open source. A new method for source localizationand signal reconstruction using Taylor expansion of the signals isproposed. This and various standard and non-standard Directionof Arrival (DOA) methods are evaluated in simulation andexperiments with the platform to track and reconstruct multipleand single sources. Results show that sound sources can belocalized and tracked robustly and accurately while rotating theplatform and that the proposed method outperforms standardmethods at reconstructing the signals.

The general Simultaneous Localisation and Mapping (SLAM) problem aims at estimating the state of a moving platform simultaneously with building a map of the local environment. There are essentially three classes of algorithms. EKF- SLAM and FastSLAM solve the problem on-line, while Nonlinear Least Squares (NLS) is a batch method. All of them scales badly with either the state dimension, the map dimension or the batch length. We investigate the EM algorithm for solving a generalized version of the NLS problem. This EM-SLAM algorithm solves two simpler problems iteratively, hence it scales much better with dimensions. The iterations switch between state estimation, where we propose an Extended Rauch-Tung-Striebel smoother, and map estimation, where a quasi-Newton method is suggested. The proposed method is evaluated in real experiments and also in simulations on a platform with a monocular camera attached to an inertial measurement unit. It is demonstrated to produce lower RMSE than with a standard Levenberg-Marquardt solver of NLS problem, at a computational cost that increases considerably slower. 

This paper investigates the usefulness of multi-frequency received signal strength (RSS) for indoor localization. Acollected set of data from four sites containing 7 frequencies fromdual receivers and a high accuracy reference positioning systemis presented. The collected data is also made publicly availablethrough ResearchGate. The data is analyzed with respect tospatial variations using Gaussian processes ( GP ). The resultsshow that there are more rapid signal variations across corridorsthan along them. The uniqueness of RSS fingerprints is analyzedsuggesting that sequences of measurements in smoothing, orsmoothing-like, algorithms that can handle temporary positionambiguities are likely the best choice for localization applications.

This paper presents a batch estimation method for Simultaneous Localization and Mapping (SLAM) using the Prediction Error Method (PEM). The estimation problem considers landmarks as parameter while treating dynamics using state space models. The gradient needed for parameter estimation is computed recursively using an Extended Kalman Filter (EKF). Results using simulations with a monocular camera and inertial sensors are presented and compared to a Nonlinear Least- Squares (NLS) estimator. The presented method produce both lower RMSE’s and scale better to the batch length. 

The extended Kalman filter (EKF) has been animportant tool for state estimation of nonlinear systems sinceits introduction. However, the EKF does not possess the same optimality properties as the Kalman filter, and may perform poorly. By viewing the EKF as an optimization problem it is possible to, in many cases, improve its performance and robustness. The paper derives three variations of the EKF by applying different optimisation algorithms to the EKF costfunction and relate these to the iterated EKF. The derived filters are evaluated in two simulation studies which exemplify the presented filters.