Matching Items (4)
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- All Subjects: particle filter
- Genre: Masters Thesis
Description
Dynamic channel selection in cognitive radio consists of two main phases. The first phase is spectrum sensing, during which the channels that are occupied by the primary users are detected. The second phase is channel selection, during which the state of the channel to be used by the secondary user is estimated. The existing cognitive radio channel selection literature assumes perfect spectrum sensing. However, this assumption becomes problematic as the noise in the channels increases, resulting in high probability of false alarm and high probability of missed detection. This thesis proposes a solution to this problem by incorporating the estimated state of channel occupancy into a selection cost function. The problem of optimal single-channel selection in cognitive radio is considered. A unique approach to the channel selection problem is proposed which consists of first using a particle filter to estimate the state of channel occupancy and then using the estimated state with a cost function to select a single channel for transmission. The selection cost function provides a means of assessing the various combinations of unoccupied channels in terms of desirability. By minimizing the expected selection cost function over all possible channel occupancy combinations, the optimal hypothesis which identifies the optimal single channel is obtained. Several variations of the proposed cost-based channel selection approach are discussed and simulated in a variety of environments, ranging from low to high number of primary user channels, low to high levels of signal-to-noise ratios, and low to high levels of primary user traffic.
ContributorsZapp, Joseph (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Kovvali, Narayan (Committee member) / Reisslein, Martin (Committee member) / Arizona State University (Publisher)
Created2014
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
Research on developing new algorithms to improve information on brain functionality and structure is ongoing. Studying neural activity through dipole source localization with electroencephalography (EEG) and magnetoencephalography (MEG) sensor measurements can lead to diagnosis and treatment of a brain disorder and can also identify the area of the brain from where the disorder has originated. Designing advanced localization algorithms that can adapt to environmental changes is considered a significant shift from manual diagnosis which is based on the knowledge and observation of the doctor, to an adaptive and improved brain disorder diagnosis as these algorithms can track activities that might not be noticed by the human eye. An important consideration of these localization algorithms, however, is to try and minimize the overall power consumption in order to improve the study and treatment of brain disorders. This thesis considers the problem of estimating dynamic parameters of neural dipole sources while minimizing the system's overall power consumption; this is achieved by minimizing the number of EEG/MEG measurements sensors without a loss in estimation performance accuracy. As the EEG/MEG measurements models are related non-linearity to the dipole source locations and moments, these dynamic parameters can be estimated using sequential Monte Carlo methods such as particle filtering. Due to the large number of sensors required to record EEG/MEG Measurements for use in the particle filter, over long period recordings, a large amounts of power is required for storage and transmission. In order to reduce the overall power consumption, two methods are proposed. The first method used the predicted mean square estimation error as the performance metric under the constraint of a maximum power consumption. The performance metric of the second method uses the distance between the location of the sensors and the location estimate of the dipole source at the previous time step; this sensor scheduling scheme results in maximizing the overall signal-to-noise ratio. The performance of both methods is demonstrated using simulated data, and both methods show that they can provide good estimation results with significant reduction in the number of activated sensors at each time step.
ContributorsMichael, Stefanos (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Chakrabarti, Chaitali (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