Infrared small target detection (ISTD) remains a long-standing challenge due to weak signal contrast, limited spatial extent, and cluttered backgrounds. Despite performance improvements from convolutional neural networks (CNNs) and Vision Transformers (ViTs), current models lack a mechanism to trace how small targets trigger directional, layer-wise perturbations in the feature space, which is an essential cue for distinguishing signal from structured noise in infrared scenes. To address this limitation, we propose the Trajectory-Aware Mamba Propagation Network (TAPM-Net), which explicitly models the spatial diffusion behavior of target-induced feature disturbances. TAPM-Net is built upon two novel components: a Perturbation-guided Path Module (PGM) and a Trajectory-Aware State Block (TASB). The PGM constructs perturbation energy fields from multi-level features and extracts gradient-following feature trajectories that reflect the directionality of local responses. The resulting feature trajectories are fed into the TASB, a Mamba-based state-space unit that models dynamic propagation along each trajectory while incorporating velocity-constrained diffusion and semantically aligned feature fusion from word-level and sentence-level embeddings. Unlike existing attention-based methods, TAPM-Net enables anisotropic, context-sensitive state transitions along spatial trajectories while maintaining global coherence at low computational cost. Experiments on NUAA-SIRST and IRSTD-1K demonstrate that TAPM-Net achieves state-of-the-art performance in ISTD.

翻译：红外小目标检测（ISTD）由于信号对比度弱、空间范围有限以及背景杂乱，一直是一个长期存在的挑战。尽管卷积神经网络（CNN）和视觉Transformer（ViT）带来了性能提升，但现有模型缺乏一种机制来追踪小目标如何在特征空间中引发方向性的、逐层的扰动，而这正是区分红外场景中信号与结构化噪声的关键线索。为弥补这一不足，我们提出了轨迹感知Mamba传播网络（TAPM-Net），它显式地建模了目标诱导特征扰动的空间扩散行为。TAPM-Net建立在两个新颖组件之上：扰动引导路径模块（PGM）和轨迹感知状态块（TASB）。PGM从多层级特征构建扰动能量场，并提取反映局部响应方向性的梯度跟随特征轨迹。生成的特征轨迹被输入到TASB中，这是一个基于Mamba的状态空间单元，它沿着每条轨迹建模动态传播过程，同时结合了速度约束扩散以及来自词级和句级嵌入的语义对齐特征融合。与现有的基于注意力的方法不同，TAPM-Net能够沿着空间轨迹实现各向异性、上下文敏感的状态转移，同时以较低的计算成本保持全局一致性。在NUAA-SIRST和IRSTD-1K数据集上的实验表明，TAPM-Net在ISTD任务中实现了最先进的性能。