Indiana University-Purdue University Indianapolis (IUPUI) / Moving Horizon Estimation (MHE) is a powerful estimation technique for tackling the estimation problems of the state of dynamic systems in the presence of constraints, nonlinearities and disturbances and measurement noises. In this work, the Moving Horizon Estimation approach is applied in estimating the State of Charge (SOC) and State of Health (SOH) of a battery and the results are compared against those for the traditional estimation method of Extended Kalman Filter (EKF). The comparison of the results show that MHE provides improvement in performance over EKF in terms of different state initial conditions, convergence time, and process and sensor noise variations. An equivalent circuit battery model is used to capture the dynamics of the battery states, experimental data is used to identify the parameters of the battery model. MHE based state estimation technique is applied to estimates the states of the battery model, subjected to various estimated initial conditions, process and measurement noises and the results are compared against the traditional EKF based estimation method. Both experimental data and simulations are used to evaluate the performance of the MHE. The results shows that MHE performs better than EKF estimation even with unknown initial state of the estimator, MHE converges faster to the actual states,and also MHE is found to be robust to measurement and process noises.

No description available.

A research report submitted to the Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, in partial fulfilment of the requirements for the degree of Master of Science in Engineering. Johannesburg, 2016 / It is of great importance to minimize misclassification of ore and waste during grade control for a mine operation. This research report compares two recoverable reserve estimation techniques for ore classification for Kayelekera Uranium Mine. The research was performed on two data sets taken from the pit with different grade distributions. The two techniques evaluated were Sequential Gaussian Simulation and Ordinary Kriging. A comparison of the estimates from these techniques was done to investigate which method gives more accurate estimates. Based on the results from profits and loss, grade tonnage curves the difference between the techniques is very low. It was concluded that similarity in the estimates were due to Sequential Gaussian Simulation estimates were from an average of 100 simulation which turned out to be similar to Ordinary Kriging. Additionally, similarities in the estimates were due to the close spaced intervals of the blast hole/sample data used. Whilst OK generally produced acceptable results like SGS, the local variability of grades was not adequately reproduced by the technique. Subsequently, if variability is not much of a concern, like if large blocks were to be mined, then either technique can be used and yield similar results. / M T 2016

Meta-analyses are conducted to synthesize the quantitative results of related studies. The random-effects meta-analysis model is based on the assumption that a distribution of true effects exists in the population. This distribution is often assumed to be normal with a mean and variance. The population variance, also called heterogeneity, can be estimated numerous ways. Accurate estimation of heterogeneity is necessary as a description of the distribution and for determining weights applied in the estimation of the summary effect when using inverse-variance weighting. To evaluate a wide range of estimators, we compared 16 estimators (Bayesian and non-Bayesian) of heterogeneity with regard to bias and mean square error over conditions based on reviews of educational and psychological meta-analyses. Three simulation conditions were varied: (a) sample size per meta-analysis, (b) true heterogeneity, and (c) sample size per effect size within each meta-analysis. Confidence or highest density intervals can be calculated for heterogeneity. The heterogeneity estimators that performed best over the widest range of conditions were paired with heterogeneity interval estimators. Interval estimators were evaluated based on coverage probability, interval width, and coverage of the estimated value. The combination of the Paule Manel estimator and Q-Profile interval method is recommended when synthesizing standardized mean difference effect sizes.

A long-baseline (LBL) underwater acoustic communication and localization system was developed for the Virginia Tech Underwater Glider (VTUG). Autonomous underwater vehicles, much like terrestrial and aerial robots require an effective positioning system, like GPS to perform a wide variety of guidance, navigation and control operations. Sea and freshwater attenuate electromagnetic waves (sea water is worse due to higher conductivity) within very few meters of striking the water surface. Since radio frequency communications are unavailable, many undersea systems use acoustic communications instead. Underwater acoustic communication is the technique of sending and receiving data below water. Underwater acoustic positioning is the technique of locating an underwater object. Among the various types of acoustic positioning systems, the LBL acoustic positioning method offers the highest accuracy for underwater vehicle navigation. A system consisting of three acoustic 'beacons which are placed on the surface of the water at known locations was developed. Using an acoustic modem to excite an acoustic transducer to send sound waves from an underwater glider, the range measurements to each of the beacons was calculated. These range measurements along with data from the attitude heading and reference system (AHRS) on board the glider were used to estimate the position of the underwater vehicle. Static and dynamic estimators were implemented. The system also allowed for underwater acoustic communication in the form of heartbeat messages from the glider, which were used to monitor the health of the vehicle. / Master of Science

The Burg spectral estimator (BSE) exhibits better peak resolution than conventional linear spectral estimators, particularly for short data records. Based on this property, the quality of the BSE is investigated with the available data record segmented and the relevant parameters or functions associated with each segment averaged. Averaging of autoregressive coefficients, reflection coefficients, or spectral density functions is used with the BSE and the corresponding performances are studied. Approximate expressions for the mean and variance of these modified Burg spectral estimators are derived. Lower bounds for the mean and variance of reflection coefficients are also deduced. Finally, the variance of the estimation errors associated with the modified power spectral density estimators is compared against the theoretical Cramer-Rao lower bound. / M.S.

An optimal estimation strategy is developed for post-experiment estimation of discretely measured dynamic systems which accounts for system model errors in a much more rigorous manner than Kalman filter-smoother type methods. The Kalman filter-smoother type methods, which currently dominate post-experiment estimation practice, treat model errors via “process noise", which essentially shifts emphasis away from the model and onto the measurements. The usefulness of this approach is subject to the measurement frequency and accuracy. The current method treats model errors by use of an estimation strategy based on concepts from optimal control theory. Unknown model error terms are explicitly included in the formulation of the problem and estimated as a part of the solution. In this manner, the estimate is improved; the model is improved; and an estimate of the model error is obtained. Implementation of the current method is straightforward, and the resulting state trajectories do not contain jump discontinuities as do the Kalman filter-smoother type estimates. Results from a number of simple examples, plus some examples from spacecraft attitude estimation, are included. The current method is shown to obtain significantly more accurate estimates than the Kalman filter-smoother type methods in many of the examples. The difference in accuracy is accentuated when the assumed model is relatively poor and when the measurements are relatively sparse in time and/or of low accuracy. Even for some well-modeled, densely measured applications, the current method is shown to be competitive with the Kalman filter-smoother type methods. / Ph. D. / incomplete_metadata

Partial group delay is a spectral parameter, which measures the time lag between two time series in a system after the spurious effects of the other series in the system have been eliminated. For weakly-stationary processes, estimators for partial group delay are proposed based on indirect and direct approaches. Conditions for weak consistency and asymptotic normality of the proposed estimators are obtained. Applications to a multiple test of partial group delay are investigated. The time lag interpretation of partial group delay is justified, which provides insight into the nature of linear relationships among weakly-stationary processes. Extensions are made to group delay estimation and partial group delay estimation for non-stationary "oscillatory" processes. / Ph. D.

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1982. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Bibliography: leaves 336-341. / by David John Rossi. / Ph.D.

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1982. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Vita. / Includes bibliographical references. / by Bruce R. Musicus. / Ph.D.