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- All Subjects: Sensor Networks
- Genre: Masters Thesis
Description
In this thesis, an integrated waveform-agile multi-modal tracking-beforedetect sensing system is investigated and the performance is evaluated using an experimental platform. The sensing system of adapting asymmetric multi-modal sensing operation platforms using radio frequency (RF) radar and electro-optical (EO) sensors allows for integration of complementary information from different sensors. However, there are many challenges to overcome, including tracking low signal-to-noise ratio (SNR) targets, waveform configurations that can optimize tracking performance and statistically dependent measurements. Address some of these challenges, a particle filter (PF) based recursive waveformagile track-before-detect (TBD) algorithm is developed to avoid information loss caused by conventional detection under low SNR environments. Furthermore, a waveform-agile selection technique is integrated into the PF-TBD to allow for adaptive waveform configurations. The embedded exponential family (EEF) approach is used to approximate distributions of parameters of dependent RF and EO measurements and to further improve target detection rate and tracking performance. The performance of the integrated algorithm is evaluated using real data from three experimental scenarios.
ContributorsLiu, Shubo (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Duman, Tolga (Committee member) / Kovvali, Narayan (Committee member) / Arizona State University (Publisher)
Created2012
Description
Composite materials are increasingly being used in aircraft, automobiles, and other applications due to their high strength to weight and stiffness to weight ratios. However, the presence of damage, such as delamination or matrix cracks, can significantly compromise the performance of these materials and result in premature failure. Structural components are often manually inspected to detect the presence of damage. This technique, known as schedule based maintenance, however, is expensive, time-consuming, and often limited to easily accessible structural elements. Therefore, there is an increased demand for robust and efficient Structural Health Monitoring (SHM) techniques that can be used for Condition Based Monitoring, which is the method in which structural components are inspected based upon damage metrics as opposed to flight hours. SHM relies on in situ frameworks for detecting early signs of damage in exposed and unexposed structural elements, offering not only reduced number of schedule based inspections, but also providing better useful life estimates. SHM frameworks require the development of different sensing technologies, algorithms, and procedures to detect, localize, quantify, characterize, as well as assess overall damage in aerospace structures so that strong estimations in the remaining useful life can be determined. The use of piezoelectric transducers along with guided Lamb waves is a method that has received considerable attention due to the weight, cost, and function of the systems based on these elements. The research in this thesis investigates the ability of Lamb waves to detect damage in feature dense anisotropic composite panels. Most current research negates the effects of experimental variability by performing tests on structurally simple isotropic plates that are used as a baseline and damaged specimen. However, in actual applications, variability cannot be negated, and therefore there is a need to research the effects of complex sample geometries, environmental operating conditions, and the effects of variability in material properties. This research is based on experiments conducted on a single blade-stiffened anisotropic composite panel that localizes delamination damage caused by impact. The overall goal was to utilize a correlative approach that used only the damage feature produced by the delamination as the damage index. This approach was adopted because it offered a simplistic way to determine the existence and location of damage without having to conduct a more complex wave propagation analysis or having to take into account the geometric complexities of the test specimen. Results showed that even in a complex structure, if the damage feature can be extracted and measured, then an appropriate damage index can be associated to it and the location of the damage can be inferred using a dense sensor array. The second experiment presented in this research studies the effects of temperature on damage detection when using one test specimen for a benchmark data set and another for damage data collection. This expands the previous experiment into exploring not only the effects of variable temperature, but also the effects of high experimental variability. Results from this work show that the damage feature in the data is not only extractable at higher temperatures, but that the data from one panel at one temperature can be directly compared to another panel at another temperature for baseline comparison due to linearity of the collected data.
ContributorsVizzini, Anthony James, II (Author) / Chattopadhyay, Aditi (Thesis advisor) / Fard, Masoud (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Created2012
Description
Continuous underwater observation is a challenging engineering task that could be accomplished by development and deployment of a sensor array that can survive harsh underwater conditions. One approach to this challenge is a swarm of micro underwater robots, known as Sensorbots, that are equipped with biogeochemical sensors that can relay information among themselves in real-time. This innovative method for underwater exploration can contribute to a more comprehensive understanding of the ocean by not limiting sampling to a single point and time. In this thesis, Sensorbot Beta, a low-cost fully enclosed Sensorbot prototype for bench-top characterization and short-term field testing, is presented in a modular format that provides flexibility and the potential for rapid design. Sensorbot Beta is designed around a microcontroller driven platform comprised of commercial off-the-shelf components for all hardware to reduce cost and development time. The primary sensor incorporated into Sensorbot Beta is an in situ fluorescent pH sensor. Design considerations have been made for easy adoption of other fluorescent or phosphorescent sensors, such as dissolved oxygen or temperature. Optical components are designed in a format that enables additional sensors. A real-time data acquisition system, utilizing Bluetooth, allows for characterization of the sensor in bench top experiments. The Sensorbot Beta demonstrates rapid calibration and future work will include deployment for large scale experiments in a lake or ocean.
ContributorsJohansen, John (Civil engineer) (Author) / Meldrum, Deirdre R (Thesis advisor) / Chao, Shih-hui (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Created2012