Target Detection and Classification via Multi-task Learning of Prediction and Classification

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Detection and classification of target at early stage are critical in modern defense systems. Previous studies have primarily focused on binary classification between targets and non-targets, or single-task learning which is structurally limited in detecting and classifying targets simultaneously. This paper proposes a multi-task learning framework based on LSTM that jointly learns prediction and classification tasks, enabling both detection and classification within a single model. The model captures both the temporal patterns of target sequences and decision boundaries between target classes. To improve detection performance, we introduce a two-dimensional score vector that integrates prediction error and k-NN(k-nearest neighbor) distance, followed by Mahalanobis distance calculation. Experimental results show that the proposed anomaly score outperforms conventional methods on target detection. The model achieves high accuracy and macro F1-scores using full-length sequences and maintains reliable performance even with shorter input segments. These results confirm its potential for early stage classification during tracking.