An Extended Kalman Filtering Technique for Geodetic First Order Data Assimilation

Data assimilation allows merging of different sources of data to estimate possible states of a system as it evolves in time. This therefore supports the idea of combining classical observations with Global Positioning System (GPS) observations to improve the integrity of first order geodetic controls in Nigeria. Given that these geodetic controls, which were established using traditional techniques and whose algorithms are still in use, the task of optimizing the coordinate values of these monuments to improve efficiency and accuracy in conventional geodetic operations around Nigeria is still a challenge. This study introduces the Extended Kalman Filter (EKF) technique for the modeling of these observations and their uncertainties in addition to exogenous noise, which is handled by an approximate set-valued state estimator. The proposed EKF provides a feasible linearization process in merging classical and GPS data collection modes as shown in our study. For each discrete time in the analysis step, it employs the Kalman gain computation, which attempts to weigh and balance uncertainties between the estimate and observation before proceeding to the analysis step. In this setup, the EKF constrains the system state in order to balance and strengthen the integrity of these first order monuments. The relationship of the derived system state with GPS coordinates (R2 = 0.85) and classical observations (R2 = 0.92) over Nigeria using a multi linear regression analysis is considerably strong. This outcome provides insight to the performance of the test algorithm and builds on the usefulness of data assimilation techniques in geodetic operations.

Dual-finite distributed H∞ filtering in sensor networks

Generally, distributed H∞ filtering approach achieves a certain disturbance attenuation level in the full frequency range. However, the energy of system noise or reference input usually limits in a specified frequency range. To reduce such a design conservatism, this article develops a distributed filtering approach based on dual scale, that is, filtering over a finite-time interval from time scale and also on a specified finite-frequency region from the frequency scale. Our target is to make the filtering error under sensor networks monitoring be relaxed into an ellipsoid bound rather than asymptotically converging to zero for exogenous noise in a specified frequency range. Finally, two illustrative examples demonstrate the strength of the developed filtering approach.

Variations of low temperature fumaroles as a tool for detecting changes in volcanic activity state: a brief overview

Fumarolic fields, especially those with near-surface soil temperature <100 ∘C, are very common features of active or quiescent volcanoes, with both open or closed conduits. Their spatial extent, as well as the time variability of their temperature, are conditioned by three main factors: (1) Local hydro-meteorological conditions; (2) Vapor flow from the underlying volcanic-hydrothermal system; (3) Permeability variation induced by stress field changes and/or deposition dissolution cycles of hydrothermal alteration minerals. Once depurated from the exogenous noise, time variations of the thermal signal, in term of both short-lasting transients and medium/long term trends, reflect changes in the activity state of the related volcanic system, and/or of seismic activity, also of tectonic origin, affecting volcanoes. Theoretical models of heat transfer processes are discussed, highlighting how it is very difficult distinguish between conductive and convective mechanisms or calculating heat fluxes: as a consequence, thermal data from low temperature fumaroles should be used as qualitative proxies of volcano-tectonic phenomena acting on the monitored volcanoes. Following the description of the measuring systems and of the criteria for designing a performing network for thermal monitoring of fumaroles, some case histories from Italian volcanoes (Vulcano, Stromboli, Mt. Etna, Mt. Vesuvius) are presented, illustrating how in the last years the monitoring of low temperature fumaroles have given useful insights on the evolution of the activity state of these volcanoes.

Cycle-to-cycle combustion variability modelling in spark ignited engines for control purposes

A control-oriented model of spark ignition combustion is presented. The model makes use of avaliable signals, such as spark advance, air mass, intake pressure, and lambda, to characterize not only the average combustion evolution but also the cycle-to-cycle variability. The conventional turbulent flame propagation model with two states, namely entrained mass and burnt mass, is improved by look-up tables at some parameters, and the cycle-to-cycle variability is estimated by propagation of an exogenous noise with a normal probabilistic distribution at the turbulent and laminar flame speed, which intends to simulate the unknowns at turbulent flow, temperature distribution, or initial kernel distribution. The model is able to estimate which is the expected variability during the combustion evolution and might be used online for characterizing the time response of closed-loop control actions or it can be used offline to improve the control strategies without large experimental test campaigns. Experimental data from a four-stroke commercial engine was used for calibration and validation purposes, demonstrating the capabilities of the model in steady and transient conditions.

ESTIMATING THE QUADRATIC VARIATION SPECTRUM OF NOISY ASSET PRICES USING GENERALIZED FLAT-TOP REALIZED KERNELS

This paper analyzes a generalized class of flat-top realized kernel estimators for the quadratic variation spectrum, that is, the decomposition of quadratic variation into integrated variance and jump variation. The underlying log-price process is contaminated by additive noise, which consists of two orthogonal components to accommodate α-mixing dependent exogenous noise and an asymptotically non-degenerate endogenous correlation structure. In the absence of jumps, the class of estimators is shown to be consistent, asymptotically unbiased, and mixed Gaussian at the optimal rate of convergence, n1/4. Exact bounds on lower-order terms are obtained, and these are used to propose a selection rule for the flat-top shrinkage. Bounds on the optimal bandwidth for noise models of varying complexity are also provided. In theoretical and numerical comparisons with alternative estimators, including the realized kernel, the two-scale realized kernel, and a bias-corrected pre-averaging estimator, the flat-top realized kernel enjoys a higher-order advantage in terms of bias reduction, in addition to good efficiency properties. The analysis is extended to jump-diffusions where the asymptotic properties of a flat-top realized kernel estimate of the total quadratic variation are established. Apart from a larger asymptotic variance, they are similar to the no-jump case. Finally, the estimators are used to design two classes of (medium) blocked realized kernels, which produce consistent, non-negative estimates of integrated variance. The blocked estimators are shown to have no loss either of asymptotic efficiency or in the rate of consistency relative to the flat-top realized kernels when jumps are absent. However, only the medium blocked realized kernels achieve the optimal rate of convergence under the jump alternative.

32-Channel Omni-Directional Microphone Array Design and Implementation

This paper presents the design and evaluation of a microphone array. The proposed evaluation index is the directional characteristic of delay and sum beamforming, which is used to optimize the microphone array design. Using beamforming simulation, a microphone arrangement that minimizes sidelobes and improves the basic performance of beamforming is selected. The new hardware has omni-directional directivity and high tolerance for exogenous noise. It has 32 microphones on a 335-mm diameter disk designed to be mounted on a mobile robot. The microphone array performance is verified in different real environments. Experimental results in indoor/outdoor sound localization show the effectiveness of the array in reverberant environments and its robustness against different pressure sound sources for covering larger areas.