Matching Items (6)
Filtering by
- Creators: Arizona State University
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
Voltage Control Oscillator (VCO) is one of the most critical blocks in Phase Lock Loops (PLLs). LC-tank VCOs have a superior phase noise performance, however they require bulky passive resonators and often calibration architectures to overcome their limited tuning range. Ring oscillator (RO) based VCOs are attractive for digital technology applications owing to their ease of integration, small die area and scalability in deep submicron processes. However, due to their supply sensitivity and poor phase noise performance, they have limited use in applications demanding low phase noise floor, such as wireless or optical transceivers. Particularly, out-of-band phase noise of RO-based PLLs is dominated by RO performance, which cannot be suppressed by the loop gain, impairing RF receiver's sensitivity or BER of optical clock-data recovery circuits. Wide loop bandwidth PLLs can overcome RO noise penalty, however, they suffer from increased in-band noise due to reference clock, phase-detector and charge-pump. The RO phase noise is determined by the noise coming from active devices, supply, ground and substrate. The authors adopt an auxiliary circuit with inverse delay sensitivity to supply noise, which compensates for the delay variation of inverter cells. Feed-forward noise-cancelling architecture that improves phase noise characteristic of RO based PLLs is presented. The proposed circuit dynamically attenuates RO phase noise contribution outside the PLL bandwidth, or in a preferred band. The implemented noise-cancelling loop potentially enables application of RO based PLL for demanding frequency synthesizers applications, such as optical links or high-speed serial I/Os.
ContributorsMin, Seungkee (Author) / Kiaei, Sayfe (Thesis advisor) / Bakkaloglu, Bertan (Committee member) / Ozev, Sule (Committee member) / Towe, Bruce (Committee member) / Arizona State University (Publisher)
Created2011
Description
Fluctuations with a power spectral density depending on frequency as $1/f^\alpha$ ($0<\alpha<2$) are found in a wide class of systems. The number of systems exhibiting $1/f$ noise means it has far-reaching practical implications; it also suggests a possibly universal explanation, or at least a set of shared properties. Given this diversity, there are numerous models of $1/f$ noise. In this dissertation, I summarize my research into models based on linking the characteristic times of fluctuations of a quantity to its multiplicity of states. With this condition satisfied, I show that a quantity will undergo $1/f$ fluctuations and exhibit associated properties, such as slow dynamics, divergence of time scales, and ergodicity breaking. I propose that multiplicity-dependent characteristic times come about when a system shares a constant, maximized amount of entropy with a finite bath. This may be the case when systems are imperfectly coupled to their thermal environment and the exchange of conserved quantities is mediated through their local environment. To demonstrate the effects of multiplicity-dependent characteristic times, I present numerical simulations of two models. The first consists of non-interacting spins in $0$-field coupled to an explicit finite bath. This model has the advantage of being degenerate, so that its multiplicity alone determines the dynamics. Fluctuations of the alignment of this model will be compared to voltage fluctuations across a mesoscopic metal-insulator-metal junction. The second model consists of classical, interacting Heisenberg spins with a dynamic constraint that slows fluctuations according to the multiplicity of the system's alignment. Fluctuations in one component of the alignment will be compared to the flux noise in superconducting quantum interference devices (SQUIDs). Finally, I will compare both of these models to each other and some of the most popular models of $1/f$ noise, including those based on a superposition of exponential relaxation processes and those based on power law renewal processes.
ContributorsDavis, Bryce F (Author) / Chamberlin, Ralph V (Thesis advisor) / Mauskopf, Philip (Committee member) / Wolf, George (Committee member) / Beckstein, Oliver (Committee member) / Arizona State University (Publisher)
Created2018
Description
With the growing importance of underground power systems and the need for greater reliability of the power supply, cable monitoring and accurate fault location detection has become an increasingly important issue. The presence of inherent random fluctuations in power system signals can be used to extract valuable information about the condition of system equipment. One such component is the power cable, which is the primary focus of this research.
This thesis investigates a unique methodology that allows online monitoring of an underground power cable. The methodology analyzes conventional power signals in the frequency domain to monitor the condition of a power cable.
First, the proposed approach is analyzed theoretically with the help of mathematical computations. Frequency domain analysis techniques are then used to compute the power spectral density (PSD) of the system signals. The importance of inherent noise in the system, a key requirement of this methodology, is also explained. The behavior of resonant frequencies, which are unique to every system, are then analyzed under different system conditions with the help of mathematical expressions.
Another important aspect of this methodology is its ability to accurately estimate cable fault location. The process is online and hence does not require the system to be disconnected from the grid. A single line to ground fault case is considered and the trend followed by the resonant frequencies for different fault positions is observed.
The approach is initially explained using theoretical calculations followed by simulations in MATLAB/Simulink. The validity of this technique is proved by comparing the results obtained from theory and simulation to actual measurement data.
This thesis investigates a unique methodology that allows online monitoring of an underground power cable. The methodology analyzes conventional power signals in the frequency domain to monitor the condition of a power cable.
First, the proposed approach is analyzed theoretically with the help of mathematical computations. Frequency domain analysis techniques are then used to compute the power spectral density (PSD) of the system signals. The importance of inherent noise in the system, a key requirement of this methodology, is also explained. The behavior of resonant frequencies, which are unique to every system, are then analyzed under different system conditions with the help of mathematical expressions.
Another important aspect of this methodology is its ability to accurately estimate cable fault location. The process is online and hence does not require the system to be disconnected from the grid. A single line to ground fault case is considered and the trend followed by the resonant frequencies for different fault positions is observed.
The approach is initially explained using theoretical calculations followed by simulations in MATLAB/Simulink. The validity of this technique is proved by comparing the results obtained from theory and simulation to actual measurement data.
ContributorsGovindarajan, Sudarshan (Author) / Holbert, Keith E. (Thesis advisor) / Heydt, Gerald (Committee member) / Karady, George G. (Committee member) / Arizona State University (Publisher)
Created2016
Description
Many methods have been proposed to estimate power system small signal stability, for either analysis or control, through identification of modal frequencies and their damping levels. Generally, estimation methods have been employed to assess small signal stability from collected field measurements. However, the challenge to using these methods in assessing field measurements is their ability to accurately estimate stability in the presence of noise. In this thesis a new method is developed which estimates the modal content of simulated and actual field measurements using orthogonal polynomials and the results are compared to other commonly used estimators. This new method estimates oscillatory performance by fitting an associate Hermite polynomial to time domain data and extrapolating its spectrum to identify small signal power system frequencies. Once the frequencies are identified, damping assessment is performed using a modified sliding window technique with the use of linear prediction (LP). Once the entire assessment is complete the measurements can be judged to be stable or unstable. Collectively, this new technique is known as the associate Hermite expansion (AHE) algorithm. Validation of the AHE method versus results from four other spectral estimators demonstrates the method's accuracy and modal estimation ability with and without the presence of noise. A Prony analysis, a Yule-Walker autoregressive algorithm, a second sliding window estimator and the Hilbert-Huang Transform method are used in comparative assessments in support of this thesis. Results from simulated and actual field measurements are used in the comparisons, as well as artificially generated simple signals. A search for actual field testing results performed by a utility was undertaken and a request was made to obtain the measurements of a brake insertion test. Comparison results show that the AHE method is accurate as compared to the other commonly used spectral estimators and its predictive capability exceeded the other estimators in the presence of Gaussian noise. As a result, the AHE method could be employed in areas including operations and planning analysis, post-mortem analysis, power system damping scheme design and other analysis areas.
ContributorsKokanos, Barrie Lee (Author) / Karady, George G. (Thesis advisor) / Heydt, Gerald (Committee member) / Farmer, Richard G (Committee member) / Ayyanar, Raja (Committee member) / Karam, Lina (Committee member) / Arizona State University (Publisher)
Created2010
Description
An investigation of phase noise in amplifier and voltage-controller oscillator (VCO) circuits was conducted to show that active direct-current (DC) bias techniques exhibit lower phase noise performance than traditional resistive DC bias techniques. Low-frequency high-gain amplifiers like those found in audio applications exhibit much better 1/f phase noise performance and can be used to bias amplifier or VCO circuits that work at much higher frequencies to reduce the phase modulation caused by higher frequency devices. An improvement in single-side-band (SSB) phase noise of 15 dB at offset frequencies less than 50 KHz was simulated and measured. Residual phase noise of an actively biased amplifier also exhibited significant noise improvements when compared to an equivalent resistive biased amplifier.
ContributorsBaldwin, Jeremy Bart (Author) / Aberle, James T., 1961- (Thesis advisor) / Bakkaloglu, Bertan (Committee member) / Ozev, Sule (Committee member) / Arizona State University (Publisher)
Created2010