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Description
Tracking a time-varying number of targets is a challenging
dynamic state estimation problem whose complexity is intensified
under low signal-to-noise ratio (SNR) or high clutter conditions.
This is important, for example, when tracking
multiple, closely spaced targets moving in the same direction such as a
convoy of low observable vehicles moving through a forest or multiple
targets moving in a crisscross pattern. The SNR in
these applications is usually low as the reflected signals from
the targets are weak or the noise level is very high.
An effective approach for detecting and tracking a single target
under low SNR conditions is the track-before-detect filter (TBDF)
that uses unthresholded measurements. However, the TBDF has only been used to
track a small fixed number of targets at low SNR.
This work proposes a new multiple target TBDF approach to track a
dynamically varying number of targets under the recursive Bayesian framework.
For a given maximum number of
targets, the state estimates are obtained by estimating the joint
multiple target posterior probability density function under all possible
target
existence combinations. The estimation of the corresponding target existence
combination probabilities and the target existence probabilities are also
derived. A feasible sequential Monte Carlo (SMC) based implementation
algorithm is proposed. The approximation accuracy of the SMC
method with a reduced number of particles is improved by an efficient
proposal density function that partitions the multiple target space into a
single target space.
The proposed multiple target TBDF method is extended to track targets in sea
clutter using highly time-varying radar measurements. A generalized
likelihood function for closely spaced multiple targets in compound Gaussian
sea clutter is derived together with the maximum likelihood estimate of
the model parameters using an iterative fixed point algorithm.
The TBDF performance is improved by proposing a computationally feasible
method to estimate the space-time covariance matrix of rapidly-varying sea
clutter. The method applies the Kronecker product approximation to the
covariance matrix and uses particle filtering to solve the resulting dynamic
state space model formulation.
dynamic state estimation problem whose complexity is intensified
under low signal-to-noise ratio (SNR) or high clutter conditions.
This is important, for example, when tracking
multiple, closely spaced targets moving in the same direction such as a
convoy of low observable vehicles moving through a forest or multiple
targets moving in a crisscross pattern. The SNR in
these applications is usually low as the reflected signals from
the targets are weak or the noise level is very high.
An effective approach for detecting and tracking a single target
under low SNR conditions is the track-before-detect filter (TBDF)
that uses unthresholded measurements. However, the TBDF has only been used to
track a small fixed number of targets at low SNR.
This work proposes a new multiple target TBDF approach to track a
dynamically varying number of targets under the recursive Bayesian framework.
For a given maximum number of
targets, the state estimates are obtained by estimating the joint
multiple target posterior probability density function under all possible
target
existence combinations. The estimation of the corresponding target existence
combination probabilities and the target existence probabilities are also
derived. A feasible sequential Monte Carlo (SMC) based implementation
algorithm is proposed. The approximation accuracy of the SMC
method with a reduced number of particles is improved by an efficient
proposal density function that partitions the multiple target space into a
single target space.
The proposed multiple target TBDF method is extended to track targets in sea
clutter using highly time-varying radar measurements. A generalized
likelihood function for closely spaced multiple targets in compound Gaussian
sea clutter is derived together with the maximum likelihood estimate of
the model parameters using an iterative fixed point algorithm.
The TBDF performance is improved by proposing a computationally feasible
method to estimate the space-time covariance matrix of rapidly-varying sea
clutter. The method applies the Kronecker product approximation to the
covariance matrix and uses particle filtering to solve the resulting dynamic
state space model formulation.
ContributorsEbenezer, Samuel P (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Bliss, Daniel (Committee member) / Kovvali, Narayan (Committee member) / Arizona State University (Publisher)
Created2015
Description
The Phoenix CubeSat is a 3U Earth imaging CubeSat which will take infrared (IR) photos of cities in the United Stated to study the Urban Heat Island Effect, (UHI) from low earth orbit (LEO). It has many different components that need to be powered during the life of its mission. The only power source during the mission will be its solar panels. It is difficult to calculate power generation from solar panels by hand because of the different orientations the satellite will be positioned in during orbit; therefore, simulation will be used to produce power generation data. Knowing how much power is generated is integral to balancing the power budget, confirming whether there is enough power for all the components, and knowing whether there will be enough power in the batteries during eclipse. This data will be used to create an optimal design for the Phoenix CubeSat to accomplish its mission.
ContributorsBarakat, Raymond John (Author) / White, Daniel (Thesis director) / Kitchen, Jennifer (Committee member) / Electrical Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
Current technology does not allow for the full amount of power produced by solar arrays (PV) on spacecraft to be utilized. The arrays are designed with non-reconfigurable architectures and sent on fifteen to twenty year long missions. They cannot be changed once they are in space, so the arrays are designed for the end of life. Throughout their lifetime, solar arrays can degrade in power producing capabilities anywhere from 20% to 50%. Because there is such a drastic difference in the beginning and end of life power production, and because they cannot be reconfigured, a new design has been found necessary in order to increase power production. Reconfiguration allows the solar arrays to achieve maximum power producing capabilities at both the beginning and end of their lives. With the potential to increase power production by 50%, the reconfiguration design consists of a switching network to be able to utilize any combination of cells. The design for reconfiguration must meet the power requirements of the solar array. This thesis will explore different designs for reconfiguration, as well as possible switches for implementation. It will also review other methods to increase power production, as well as discuss future work in this field.
ContributorsJohnson, Everett Hope (Author) / Kitchen, Jennifer (Thesis director) / Ozev, Sule (Committee member) / School of International Letters and Cultures (Contributor) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
In this paper, we propose an autonomous throwing and catching system to be developed as a preliminary step towards the refinement of a robotic arm capable of improving strength and motor function in the limb. This will be accomplished by first autonomizing simpler movements, such as throwing a ball. In this system, an autonomous thrower will detect a desired target through the use of image processing. The launch angle and direction necessary to hit the target will then be calculated, followed by the launching of the ball. The smart catcher will then detect the ball as it is in the air, calculate its expected landing location based on its initial trajectory, and adjust its position so that the ball lands in the center of the target. The thrower will then proceed to compare the actual landing position with the position where it expected the ball to land, and adjust its calculations accordingly for the next throw. By utilizing this method of feedback, the throwing arm will be able to automatically correct itself. This means that the thrower will ideally be able to hit the target exactly in the center within a few throws, regardless of any additional uncertainty in the system. This project will focus of the controller and image processing components necessary for the autonomous throwing arm to be able to detect the position of the target at which it will be aiming, and for the smart catcher to be able to detect the position of the projectile and estimate its final landing position by tracking its current trajectory.
ContributorsLundberg, Kathie Joy (Co-author) / Thart, Amanda (Co-author) / Rodriguez, Armando (Thesis director) / Berman, Spring (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
This project examines the science of electric field sensing and completes experiments, gathering data to support its utility for various applications. The basic system consists of a transmitter, receiver, and lock-in amplifier. The primary goal of the study was to determine if such a system could detect a human disturbance, due to the capacitance of a human body, and such a thesis was supported. Much different results were obtained when a person disturbed the electric field transmitted by the system than when other types of objects, such as chairs and electronic devices, were placed in the field. In fact, there was a distinct difference between persons of varied sizes as well. This thesis goes through the basic design of the system and the process of experimental design for determining the capabilities of such an electric field sensing system.
ContributorsBranham, Breana Michelle (Author) / Allee, David (Thesis director) / Papandreou-Suppappola, Antonia (Committee member) / Phillips, Stephen (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor) / School of International Letters and Cultures (Contributor)
Created2013-05
Description
A hybrid PV/T module was built, consisting of a thermal liquid heating system and a photovoltaic module system that combine in a hybrid format. This report will discuss the work on the project from Fall 2012 to Spring 2013 and the extended section on the economics for the Honors Thesis. Three stages of experiments were completed. Stage 1 showed our project was functional as we were able to verify our panel produced electricity and increased the temperature of water flowing in the system by 0.65°C. Stage 2 testing included “gluing” the flow system to the back of the panel resulting in an average increase of 4.76°C in the temperature of the water in the system. Stage 3 testing included adding insulating foam to the module which resulted in increasing the average temperature of the water in our flow system by 6.95°C. The economic calculations show the expected energy cost savings for Arizona residents.
ContributorsHaines, Brent Robert (Author) / Roedel, Ronald (Thesis director) / Aberle, James (Committee member) / Rauch, Dawson (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor)
Created2013-05
Description
Resistive Random Access Memory (RRAM) is an emerging type of non-volatile memory technology that seeks to replace FLASH memory. The RRAM crossbar array is advantageous in its relatively small cell area and faster read latency in comparison to NAND and NOR FLASH memory; however, the crossbar array faces design challenges of its own in sneak-path currents that prevent proper reading of memory stored in the RRAM cell. The Current Sensing Amplifier is one method of reading RRAM crossbar arrays. HSpice simulations are used to find the associated reading delays of the Current Sensing Amplifier with respect to various sizes of RRAM crossbar arrays, as well as the largest array size compatible for accurate reading. It is found that up to 1024x1024 arrays are achievable with a worst-case read delay of 815ps, and it is further likely 2048x2048 arrays are able to be read using the Current Sensing Amplifier. In comparing the Current Sensing Amplifier latency results with previously obtained latency results from the Voltage Sensing Amplifier, it is shown that the Voltage Sensing Amplifier reads arrays in sizes up to 256x256 faster while the Current Sensing Amplifier reads larger arrays faster.
ContributorsMoore, Jenna Barber (Author) / Yu, Shimeng (Thesis director) / Liu, Rui (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
This thesis outlines the hand-held memory characterization testing system that is to be created into a PCB (printed circuit board). The circuit is designed to apply voltages diagonally through a RRAM cell (32x32 memory array). The purpose of this sweep across the RRAM is to measure and calculate the high and low resistance state value over a specified amount of testing cycles. With each cell having a unique output of high and low resistance states a unique characterization of each RRAM cell is able to be developed. Once the memory is characterized, the specific RRAM cell that was tested is then able to be used in a varying amount of applications for different things based on its uniqueness. Due to an inability to procure a packaged RRAM cell, a Mock-RRAM was instead designed in order to emulate the same behavior found in a RRAM cell.
The final testing circuit and Mock-RRAM are varied and complex but come together to be able to produce a measured value of the high resistance and low resistance state. This is done by the Arduino autonomously digitizing the anode voltage, cathode voltage, and output voltage. A ramp voltage that sweeps from 1V to -1V is applied to the Mock-RRAM acting as an input. This ramp voltage is then later defined as the anode voltage which is just one of the two nodes connected to the Mock-RRAM. The cathode voltage is defined as the other node at which the voltage drops across the Mock-RRAM. Using these three voltages as input to the Arduino, the Mock-RRAM path resistance is able to be calculated at any given point in time. Conducting many test cycles and calculating the high and low resistance values allows for a graph to be developed of the chaotic variation of resistance state values over time. This chaotic variation can then be analyzed further in the future in order to better predict trends and characterize the RRAM cell that was tested.
Furthermore, the interchangeability of many devices on the PCB allows for the testing system to do more in the future. Ports have been added to the final PCB in order to connect a packaged RRAM cell. This will allow for the characterization of a real RRAM memory cell later down the line rather than a Mock-RRAM as emulation. Due to the autonomous testing, very few human intervention is needed which makes this board a great baseline for others in the future looking to add to it and collect larger pools of data.
The final testing circuit and Mock-RRAM are varied and complex but come together to be able to produce a measured value of the high resistance and low resistance state. This is done by the Arduino autonomously digitizing the anode voltage, cathode voltage, and output voltage. A ramp voltage that sweeps from 1V to -1V is applied to the Mock-RRAM acting as an input. This ramp voltage is then later defined as the anode voltage which is just one of the two nodes connected to the Mock-RRAM. The cathode voltage is defined as the other node at which the voltage drops across the Mock-RRAM. Using these three voltages as input to the Arduino, the Mock-RRAM path resistance is able to be calculated at any given point in time. Conducting many test cycles and calculating the high and low resistance values allows for a graph to be developed of the chaotic variation of resistance state values over time. This chaotic variation can then be analyzed further in the future in order to better predict trends and characterize the RRAM cell that was tested.
Furthermore, the interchangeability of many devices on the PCB allows for the testing system to do more in the future. Ports have been added to the final PCB in order to connect a packaged RRAM cell. This will allow for the characterization of a real RRAM memory cell later down the line rather than a Mock-RRAM as emulation. Due to the autonomous testing, very few human intervention is needed which makes this board a great baseline for others in the future looking to add to it and collect larger pools of data.
ContributorsDobrin, Ryan Christopher (Co-author) / Halden, Matthew (Co-author) / Hall, Tanner (Co-author) / Barnaby, Hugh (Thesis director) / Kitchen, Jennifer (Committee member) / Electrical Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
With the progression of different industries moving away from employing secretaries for business professionals and professors, there exists a void in the area of personal assistance. This problem has existing solutions readily available to replace this service, i.e. secretary or personal assistant, tend to range from expensive and useful to inexpensive and not efficient. This leaves a low cost niche into the market of a virtual office assistant or manager to display messages and to help direct people in obtaining contact information. The development of a low cost solution revolves around the software needed to solve the various problems an accessible and user friendly Virtual Interface in which the owner of the Virtual Office Manager/Assistant can communicate to colleagues who are at standby outside of the owner's office and vice versa. This interface will be allowing the owner to describe the status pertaining to their absence or any other message sent to the interface. For example, the status of the owner's work commute can be described with a simple "Running Late" phrase or a message like "Busy come back in 10 minutes". In addition, any individual with an interest to these entries will have the opportunity to respond back because the device will provide contact information. When idle, the device will show supplemental information such as the owner's calendar and name. The scope of this will be the development and testing of solutions to achieve these goals.
ContributorsOffenberger, Spencer Eliot (Author) / Kozicki, Michael (Thesis director) / Goryll, Michael (Committee member) / Electrical Engineering Program (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
Information divergence functions, such as the Kullback-Leibler divergence or the Hellinger distance, play a critical role in statistical signal processing and information theory; however estimating them can be challenge. Most often, parametric assumptions are made about the two distributions to estimate the divergence of interest. In cases where no parametric model fits the data, non-parametric density estimation is used. In statistical signal processing applications, Gaussianity is usually assumed since closed-form expressions for common divergence measures have been derived for this family of distributions. Parametric assumptions are preferred when it is known that the data follows the model, however this is rarely the case in real-word scenarios. Non-parametric density estimators are characterized by a very large number of parameters that have to be tuned with costly cross-validation. In this dissertation we focus on a specific family of non-parametric estimators, called direct estimators, that bypass density estimation completely and directly estimate the quantity of interest from the data. We introduce a new divergence measure, the $D_p$-divergence, that can be estimated directly from samples without parametric assumptions on the distribution. We show that the $D_p$-divergence bounds the binary, cross-domain, and multi-class Bayes error rates and, in certain cases, provides provably tighter bounds than the Hellinger divergence. In addition, we also propose a new methodology that allows the experimenter to construct direct estimators for existing divergence measures or to construct new divergence measures with custom properties that are tailored to the application. To examine the practical efficacy of these new methods, we evaluate them in a statistical learning framework on a series of real-world data science problems involving speech-based monitoring of neuro-motor disorders.
ContributorsWisler, Alan (Author) / Berisha, Visar (Thesis advisor) / Spanias, Andreas (Thesis advisor) / Liss, Julie (Committee member) / Bliss, Daniel (Committee member) / Arizona State University (Publisher)
Created2017