摘要
We illustrate an approach to statistical model and sequentiM hypothesis designed to the automatic target recognition （ATR） problem for active imaging LADAR. The key to this approach is using multihypothesis sequential tests to reduce the number of target hypotheses under consideration as more observed data are processed. The approach is potentially useful when sensor data are plentiful but computation time and processing capability are constrained. We experimentally demonstrate that the proposed recognition approach can resolve the military ground vehicle recognition problem of active imaging LADAR with a high recognition rate.
We illustrate an approach to statistical model and sequentiM hypothesis designed to the automatic target recognition （ATR） problem for active imaging LADAR. The key to this approach is using multihypothesis sequential tests to reduce the number of target hypotheses under consideration as more observed data are processed. The approach is potentially useful when sensor data are plentiful but computation time and processing capability are constrained. We experimentally demonstrate that the proposed recognition approach can resolve the military ground vehicle recognition problem of active imaging LADAR with a high recognition rate.
