The increasing prevalence of compact UAVs has introduced significant risks to public safety, while traditional drone detection systems are often bulky and costly. To address these challenges, we present TAME, the Temporal Audio-based Mamba for Enhanced Drone Trajectory Estimation and Classification. This innovative anti-UAV detection model leverages a parallel selective state-space model to simultaneously capture and learn both the temporal and spectral features of audio, effectively analyzing propagation of sound. To further enhance temporal features, we introduce a Temporal Feature Enhancement Module, which integrates spectral features into temporal data using residual cross-attention. This enhanced temporal information is then employed for precise 3D trajectory estimation and classification. Our model sets a new standard of performance on the MMUAD benchmarks, demonstrating superior accuracy and effectiveness. The code and trained models are publicly available on GitHub https://github.com/AmazingDay1/TAME.

翻译：随着小型无人机的日益普及，公共安全面临重大风险，而传统无人机检测系统往往体积庞大且成本高昂。为应对这些挑战，我们提出了TAME——基于时序音频的Mamba增强型无人机轨迹估计与分类模型。这一创新的反无人机检测模型采用并行选择性状态空间架构，能够同步捕获并学习音频的时序与频谱特征，从而有效解析声波传播特性。为进一步增强时序特征，我们设计了时序特征增强模块，通过残差交叉注意力机制将频谱特征融合到时序数据中。经过强化的时序信息随后被用于精确的三维轨迹估计与分类任务。我们的模型在MMUAD基准测试中创造了性能新纪录，展现出卓越的准确性与有效性。相关代码与训练模型已在GitHub开源：https://github.com/AmazingDay1/TAME。