Application of feature space trajectory classifier to identification of multi-aspect radar signals

Kyung-Tae Kim
<span title="">2005</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/jm6w2xclfzguxnhmnmq5omebpi" style="color: black;">Pattern Recognition</a> </i> &nbsp;
In this paper, a feature space trajectory (FST) classifier is applied to identify an unknown radar target. To improve the identification accuracy, we make use of information at multiple aspects of a radar target, and the FST classifier is combined with two different rules: majority vote and sum vote. In addition, two different algorithms via the simultaneous use of FST concept and line-to-line distance metric are presented to classify multi-aspect radar signals. Experimental results show that
more &raquo; ... e proposed two algorithms significantly outperform the traditional FST classifier combined with majority vote and sum vote.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.patcog.2005.02.003">doi:10.1016/j.patcog.2005.02.003</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gesj7tv75fdmxebmj555eoo52a">fatcat:gesj7tv75fdmxebmj555eoo52a</a> </span>
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