<p> In this thesis new joint target tracking and classification techniques for aspect-dependent measurements are developed. Joint target tracking and classification methods can result in better tracking and classification performance than those treating these as two separate problems. Significant improvement in state estimation and classification performance can be achieved by exchanging useful information between the tracker and the classifier. Target classification in many target tracking algorithms is not typically done by taking into consideration the target-to-sensor orientation. However, the feature information extracted from the signal that originated from the target is generally a strong function of the target-to-sensor orientation. Since sensor returns are sensitive to this orientation, classification from a single sensor may not give exact target classes. Better classification results can be obtained by fusing feature measurements from multiple views of a target. In multitarget scenarios, handling the classification becomes more challenging due to the identifying the feature information corresponding to a target. That is, it is difficult to identify the origin of measurements. In this case, feature measurement origin ambiguities can be eliminated by integrating the classifier into multiframe data association. This technique reduces the ambiguity in feature measurements while improving track purity. </p> <p> A closed form expression for multiaspect target classification is not feasible. Then, training based statistical modeling can be used to model the unknown feature measurements of a target. In this thesis, the Observable Operator Model (OOM), a better alternative to the Hidden Markov Model (HMM), is used to capture unknown feature distribution of each target and thus can be used as a classifier. The proposed OOM based classification technique incorporates target-to-sensor orientation with a sequence of feature information from multiple sensors. Further, the multi-aspect classifier can be modeled using the OOM to handle unknown target orientation. The target orientation estimation using OOM can also be used to find improved estimates of the states of highly maneuverable targets with noisy kinematic measurements. One limiting factor in obtaining accurate estimates of highly maneuvering target states is the high level of uncertainty in velocity and acceleration components. The target orientation information is helpful in alleviating this problem to accurately determine the velocity and acceleration components. </p> <p> Various simulation studies based on two-dimensional scenarios are presented in this thesis to demonstrate the merits of the proposed joint target tracking and classification algorithms that use aspect-dependent feature measurements.</p> / Thesis / Doctor of Philosophy (PhD)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/17353 |
| Date | 09 1900 |
| Creators | Sivagnanam, Sutharsan |
| Contributors | Kirubarajan, T., Electrical and Computer Engineering |
| Source Sets | McMaster University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
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