Optimized Estimates Based On Multiple Sensor Configuration Knowledge
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Optimized Estimates Based on Multiple Sensor Configuration Knowledge
There has been an increase in the usage of sensor technology as they are adapt- able for use in di erent environments, many of which are hostile to direct human observation, such as regions a ected by land mines or forest res. The sensors used to monitor developing situations are small inexpensive computing devices with lim- ited processing capabilities, limited power supply and may be destroyed in the event that they are monitoring, without su ering a great nancial cost. Also, since these devices are inexpensive, multiple sensors can be deployed for the same application where the sensors are placed or attached to a mobile platform in a particular con- guration or shape. However, because these devices are inexpensive, they do not possess all the software and hardware necessary to produce accurate observed data from the environment they are deployed in. Moreover, there are additional factors such as atmospheric conditions, network delays, sensor characteristics etc. that may a ect the measures being monitored and therefore, the data observed and produced as output by the sensors may be inaccurate. The issue of obtaining accurate estimates of location and orientation from in- accurate observed sensor data has been subjected to thorough investigation in the literature. However, in the application environment of multiple Global Positioning Satellite (GPS) sensors attached to a mobile robot platform, these previous methods do not take advantage of the sensor con guration information to produce more accu- rate estimates of the measures being observed. In this dissertation the authors will demonstrate that in fact, with the use of the sensor con guration and the inaccu- rate observed sensor data, it is possible to obtain accurate estimates of location and orientation of the deployed sensors. In this dissertation, we propose several concrete issues and their respective so- lutions for the framework of the mobile robot platform with GPS sensors attached in a particular con guration to be operational. We use optimization techniques to t estimates of locations and orientations of the sensors on the mobile platform given observed data that is highly unstable when the platform is stationary or in motion, while taking advantage of the known con guration knowledge. To deal with outliers and missing data, we use statistical and heuristic weighting techniques to favor accu- rate observed sensor data over inaccurate data. Moreover, the production of estimates through the use of optimization has to conform to the real-time constraints when the platform is in motion and therefore, we introduce sliding windows to be able to gen- erate updated estimates. Furthermore, depending on the size of the sliding window, the task of correcting the lag between the estimate over the sliding window and the estimate with respect to the current time-step is also considered to produce more accurate results.
Optimal State Estimation for Process Monitoring, Fault Diagnosis and Control
Optimal State Estimation for Process Monitoring, Fault Diagnosis and Control presents various mechanistic model based state estimators and data-driven model based state estimators with a special emphasis on their development and applications to process monitoring, fault diagnosis and control. The design and analysis of different state estimators are highlighted with a number of applications and case studies concerning to various real chemical and biochemical processes. The book starts with the introduction of basic concepts, extending to classical methods and successively leading to advances in this field. Design and implementation of various classical and advanced state estimation methods to solve a wide variety of problems makes this book immensely useful for the audience working in different disciplines in academics, research and industry in areas concerning to process monitoring, fault diagnosis, control and related disciplines. - Describes various classical and advanced versions of mechanistic model based state estimation algorithms - Describes various data-driven model based state estimation techniques - Highlights a number of real applications of mechanistic model based and data-driven model based state estimators/soft sensors - Beneficial to those associated with process monitoring, fault diagnosis, online optimization, control and related areas
Probabilistic Framework for Sensor Management
A probabilistic sensor management framework is introduced, which maximizes the utility of sensor systems with many different sensing modalities by dynamically configuring the sensor system in the most beneficial way. For this purpose, techniques from stochastic control and Bayesian estimation are combined such that long-term effects of possible sensor configurations and stochastic uncertainties resulting from noisy measurements can be incorporated into the sensor management decisions.