Low-Altitude Slow Small Target Threat Assessment Algorithm by Exploiting Sequential Multifeature With Long Short-Term Memory
Xiaojie Yu, Song Wei, Yuyuan Fang, Jialian Sheng, Lei Zhang
Abstract
For the preservation of low-airspace security, a comprehensive threat assessment of low-altitude slow small (LSS) flying targets is essential. Threat assessment intrinsically depends on effectively extracting the optimal attributes from tracks, which represent the target’s motion intention and specific characteristics. However, unavoidable measured noise and track jitters during observation impact assessment performance. To address this problem, long short-term memory (LSTM) is introduced due to its good performance on sequential perception. LSTM captures the permanent intent traits and reduces noise interference utilizing temporal correlations within its novel “memory” units. Compared to traditional algorithms, the proposed technique substantially reduces measurement noise, enhancing the accuracy of threat evaluations. Simulation experiments confirm the effectiveness and robustness of the proposed algorithms.