用声音识别无人机轨迹与类型，精度更高且无需昂贵设备。
TAME: Temporal Audio-based Mamba for Enhanced Drone Trajectory Estimation and Classification
- 基于音频时序特征的Mamba模型，同时捕捉声音的时间与频谱信息。
- 在MMUAD数据集上表现优异，3D轨迹估计准确率显著提升。
- 适合反无人机系统研发者，尤其关注低成本部署的场景。
随着小型无人机日益普及，公共安全面临严峻挑战，传统反无人机检测系统通常体积庞大且成本高昂。为此，本文提出TAME（Temporal Audio-based Mamba for Enhanced Drone Trajectory Estimation and Classification），一种创新的反无人机检测模型。该模型采用并行选择性状态空间机制，同步捕获音频信号的时间与频谱特征，有效分析声音传播特性。为进一步增强时序特征，引入时序特征增强模块，通过残差交叉注意力将频谱信息融入时序数据中。利用增强后的时序信息，实现高精度三维轨迹估计与分类。在MMUAD基准测试中，TAME展现出卓越的性能，达到新标准。代码与训练好的模型已公开发布于GitHub：https://github.com/AmazingDay1/TAME。
原文摘要 · Abstract (English)
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.
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