2024-03-29T01:47:05Zhttps://keep.lib.asu.edu/oai/requestoai:keep.lib.asu.edu:node-1534202021-08-30T18:30:05Zoai_pmh:all153420
https://hdl.handle.net/2286/R.I.29684
http://rightsstatements.org/vocab/InC/1.0/
All Rights Reserved
2015
xvii, 213 p. : ill. (some col.)
Doctoral Dissertation
Academic theses
Text
eng
Ebenezer, Samuel P
Papandreou-Suppappola, Antonia
Chakrabarti, Chaitali
Bliss, Daniel
Kovvali, Narayan
Arizona State University
Partial requirement for: Ph.D., Arizona State University, 2015
Includes bibliographical references (p. 198-211)
Field of study: Electrical engineering
Tracking a time-varying number of targets is a challenging <br/><br/>dynamic state estimation problem whose complexity is intensified<br/><br/>under low signal-to-noise ratio (SNR) or high clutter conditions. <br/><br/>This is important, for example, when tracking <br/><br/>multiple, closely spaced targets moving in the same direction such as a<br/><br/>convoy of low observable vehicles moving through a forest or multiple<br/><br/>targets moving in a crisscross pattern. The SNR in <br/><br/>these applications is usually low as the reflected signals from <br/><br/>the targets are weak or the noise level is very high. <br/><br/>An effective approach for detecting and tracking a single target<br/><br/>under low SNR conditions is the track-before-detect filter (TBDF)<br/><br/>that uses unthresholded measurements. However, the TBDF has only been used to <br/><br/>track a small fixed number of targets at low SNR.<br/><br/>This work proposes a new multiple target TBDF approach to track a <br/><br/>dynamically varying number of targets under the recursive Bayesian framework. <br/><br/>For a given maximum number of <br/><br/>targets, the state estimates are obtained by estimating the joint <br/><br/>multiple target posterior probability density function under all possible <br/><br/>target <br/><br/>existence combinations. The estimation of the corresponding target existence <br/><br/>combination probabilities and the target existence probabilities are also <br/><br/>derived. A feasible sequential Monte Carlo (SMC) based implementation <br/><br/>algorithm is proposed. The approximation accuracy of the SMC <br/><br/>method with a reduced number of particles is improved by an efficient <br/><br/>proposal density function that partitions the multiple target space into a <br/><br/>single target space.<br/><br/>The proposed multiple target TBDF method is extended to track targets in sea<br/><br/>clutter using highly time-varying radar measurements. A generalized <br/><br/>likelihood function for closely spaced multiple targets in compound Gaussian <br/><br/>sea clutter is derived together with the maximum likelihood estimate of <br/><br/>the model parameters using an iterative fixed point algorithm. <br/><br/>The TBDF performance is improved by proposing a computationally feasible <br/><br/>method to estimate the space-time covariance matrix of rapidly-varying sea<br/><br/>clutter. The method applies the Kronecker product approximation to the <br/><br/>covariance matrix and uses particle filtering to solve the resulting dynamic<br/><br/>state space model formulation.
engineering
Compound Gaussian
Kronecker products
Multiple target tracking
Sea Clutter
Space-Time Covariance matrix
Track-before-detect
Radar targets
Electric noise
Signal processing--Digital techniques.
Multiple radar target tracking in environments with high noise and clutter