<p>This thesis considers passive localization and tracking. Here, passive refers to passive observations - the type of observations for which the full position estimate of the target cannot be obtained using a single measurement, like those are from a sonar. Hence, localizing or tracking targets based on these measurements calls for the use of multiple sensors. This poses a different set of challenges to tracking with passive observations as opposed to active observations where full target position is available from a single measurement.</p><p>We identify different issues that are related to passive localization and tracking and propose algorithmic solutions to these problems. We consider the angle of arrival (AOA), which is the passive measurement that is often considered in target tracking and time difference of arrival (TDOA) as representative passive measurements to illustrate our algorithms. Whereas, the AOA measurements from different sensors can be considered independent, TDOA measurements, on the other hand, are not independent. That is, they are correlated. We would, however, like to note that the proposed algorithms can be applied with straightforward, but simple, modifications to other types of passive measurements.</p><p>In particular, this thesis provides solutions to the following problems. First, it provides efficient and improved algorithms to the data association problem when tracking with multiple passive synchronous sensors. These solutions are based on the assignment formulation. Whereas one of the algorithms proposed, the gated assignment algorithm, uses the validation gates to reduce the computational cost, the other is a new extension to the multidimensional assignment algorithm that associates the measurements directly to the tracks. This is called the (S + 1)-D assignment-based data association, where S is the number of synchronous sensors available in the tracking system. An approximation to this new (S + 1)-D algorithm is also presented.</p> <p> In literature one finds algorithms to localize a single target using TDOA measurements. None of these algorithms considered the issues that might arise in tracking the localized targets. This thesis provides a framework to localize and track targets based on TDOA measurements. The localization algorithm uses a formulation based on the sensor-emitter geometry. This formulation is considered as a constrained optimization problem and two relaxation-based algorithms are provided to solve this optimization problem. The assignment-based data association provides an additional challenge because the TDOA measurements are correlated. This problem is identified and a solution is provided by modifying the calculation of the association cost.</p> <p> Finally, this thesis also provides an efficient algorithm to form AOA mono tracks using the fast Fourier transform (FFT) and the assignment algorithm. Formation of the mono tracks is very useful in distributed tracking and is the well-known direction of arrival tracking problem in the signal processing community.</p> / Thesis / Doctor of Philosophy (PhD)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/16620 |
| Date | 12 1900 |
| Creators | Sathyan , Thuraiappah |
| Contributors | Kirubarajan, T., Electrical and Computer Engineering |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
Page generated in 0.0035 seconds