This paper presents a novel video-level RGB-T tracking paradigm based on prompt learning, termed PromptTrack, which establishes dense spatial-temporal associations through cross-modal interactions. The method introduces streaming temporal prompts to capture continuous target dynamics (e.g., appearance changes and motion trajectories), while leveraging multimodal spatial prompts to utilize complementary RGB and thermal-infrared (TIR) features dynamically. By propagating temporal prompts through consecutive frames and integrating bidirectional spatial interactions between modalities, PromptTrack achieves superior tracking performance in complex scenarios such as occlusion, low illumination, and distractors. The proposed framework employs a unified multimodal encoder with spatial-temporal modeling via multimodal spatial prompt blocks, enabling efficient fusion of RGB-TIR features without requiring domain-specific structure modifications. Extensive experiments on three RGB-T benchmarks (LasHeR, RGBT210, RGBT234) demonstrate that PromptTrack achieves new state-of-the-art performance, with 76.2% in precision rate on LasHeR and outperforming existing methods by ++1.9% in precision rate and +0.5% in success rate on RGBT234. Notably, its modality-agnostic design facilitates seamless generalization to RGB-D and RGB-E tracking domains achieving new benchmarks on DepthTrack, VOT-RGBD2022, and VisEvent datasets.

The conventional methods used to estimate the direction of arrival (DOA) of linear frequency modulated (LFM) signals at low signal‐to‐noise ratios (SNRs), such as the echoes reflected by a small unmanned aerial vehicle (UAV), demonstrate major performance deterioration. In order to eliminate the problem and achieve highly accurate DOA estimation for low‐SNR echoes, this paper proposes a novel estimation approach that applies two accumulative methods based on fractional Fourier transform (FrFT). According to the findings of this paper, the algorithm directly accumulates the FrFT result of each echo when it is not possible to determine in advance the speed of the target. When the radial velocity of the small UAV has been estimated ahead of time, the algorithm performs phase compensation to enhance the accumulative effect. Through coherent integration, the algorithm then extracts all the peaks of the target echo waveform, which are then used for the construction of a fractional autocorrelation matrix. Thereafter, multiple signal classification is employed for DOA estimation. Furthermore, with the proposed algorithm, the DOAs of multi‐target echoes can be estimated accurately. The effectiveness of the proposed algorithm was verified using Monte–Carlo simulation trials, and the root‐mean‐square‐error of DOA estimation was close to the Cramer–Rao bound.