Search Results (2)

This research proposes a novel modeling method for integrating IMU arrays into multi-sensor kinematic positioning/navigation systems. This method characterizes sensor errors (biases/scale factor errors) for each IMU in an IMU array, leveraging the novel Generic Multisensor Integration Strategy (GMIS) and the framework for comprehensive error analysis in Discrete Kalman filtering developed through the authors’ previous research. This work enables the time-varying estimation of all individual sensor errors for an IMU array, as well as rigorous fault detection and exclusion for outlying measurements from all constituent sensors. This research explores the feasibility of applying Variance Component Estimation (VCE) to IMU array data, using separate variance components to characterize the performance of each IMU’s gyroscopes and accelerometers. This analysis is only made possible by directly modeling IMU inertial measurements under the GMIS. A real land-vehicle kinematic dataset was used to demonstrate the proposed technique. The a posteriori positioning/attitude standard deviations were compared between multi-IMU and single IMU solutions, with the multi-IMU solution providing an average accuracy improvement of ca. 14–16% in the estimated position, 30% in the estimated roll and pitch, and 40% in the estimated heading. The results of this research demonstrate that IMUs in an array do not generally exhibit homogeneous behavior, even when using the same model of tactical-grade MEMS IMU. Furthermore, VCE was used to compare the performance of three IMU sensors, which is not possible under other IMU array data fusion techniques. This research lays the groundwork for the future evaluation of IMU array sensor configurations. Full article

In the first part of the paper, the positive and negative jumps of NASDAQ daily (log-) returns and three of its stocks are estimated based on the methodology presented by Theodosiadou et al. 2016, where jumps are assumed to be hidden random variables. For that reason, the use of stochastic state space models in discrete time is adopted. The daily return is expressed as the difference between the two-sided jumps under noise inclusion, and the recursive Kalman filter algorithm is used in order to estimate them. Since the estimated jumps have to be non-negative, the associated pdf truncation method, according to the non-negativity constraints, is applied. In order to overcome the resulting underestimation of the empirical time series, a scaling procedure follows the stage of truncation. In the second part of the paper, a nonparametric change point analysis concerning the (variance–) covariance is applied to the NASDAQ return time series, as well as to the estimated bivariate jump time series derived after the scaling procedure and to each jump component separately. A similar change point analysis is applied to the three other stocks of the NASDAQ index. Full article