A GRU-Based Method for Predicting Intention of Aerial Targets

<table class="table-group" id="tab4"><tr><td><table class="table"><tr><td class="thead-hr" colspan="3"><hr/></td></tr><tr class="thead"><td class="align_left">Model</td><td class="align_center">Accuracy (%)</td><td class="align_center">Loss</td></tr><tr><td class="thead-hr" colspan="3"><hr/></td></tr><tr><td class="align_left">BiLSTM-Attention</td><td class="align_center">90.5</td><td class="align_center">0.257</td></tr><tr><td class="align_left">LSTM</td><td class="align_center">87.6</td><td class="align_center">0.346</td></tr><tr><td class="align_left">SAE</td><td class="align_center">81.3</td><td class="align_center">0.473</td></tr><tr><td class="align_left">DBP</td><td class="align_center">79.3</td><td class="align_center">0.492</td></tr><tr class="table-tr"><td colspan="3"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>Comparison of different intention recognition models.</div>

Computational Intelligence and Neuroscience

tab4

Table 4

Table 4: A GRU-Based Method for Predicting Intention of Aerial Targets 