The target tracking by space-based surveillance systems is difficult due to the long distances, weak energies, fast speeds, high false alarm rates, and low algorithmic efficiencies involved in the process. To mitigate the impact of these difficulties, this article proposes a target tracking algorithm based on image processing and Transformer, which employs a two-dimensional Gaussian soft-thresholding method to reduce the image noise, and combines a Laplace operator-weighted fusion method to augment the image, so as to improve the overall quality of the image and increase the accuracy of target tracking. Based on the SiamCAR framework, the Transformer model in the field of natural language processing is introduced, which can be used to enhance the image features extracted from the backbone network by mining the rich temporal information between the initial and dynamic templates. In order to capture the information of the target’s appearance change in the temporal sequence, a template update branch is introduced at the input of the algorithm, which realizes the dynamic update of the templates by constructing a template memory pool, and selecting the best templates for the candidate templates in the memory pool using the cosine similarity-based selection, thus ensuring the robustness of the tracking algorithm. The experimental results that compared with the SiamCAR algorithm and the mainstream algorithms, the TrD-Siam algorithm proposed in this article effectively improves the tracking success rate and accuracy, addressing poor target tracking performance under space-based conditions, and has a good value of application in the field of optoelectronic detection.

Medical three-dimensional (3D) positioning technology amalgamates traditional medical instrumentation with computational technology and medical imaging to facilitate the real-time visualization of anatomical lesions, surgical implements, and other relevant objects, thereby augmenting the physician’s diagnostic and therapeutic capabilities. Predominantly, optical positioning technologies are employed; however, they inherently suffer from limitations such as optical occlusion, precluding precise positioning. To surmount these challenges, the present manuscript introduces a phase-based 3D electromagnetic positioning technique predicated on spectral interpolation. Initially, four spectral lines exhibiting maximal signal amplitude are designated for the computation of a correction factor. Subsequently, the functional relationship between this correction factor and frequency offset is modeled via polynomial fitting, from which signal frequency and phase correction equations are derived. Experimental analyses executed within the ANSYS software (2021R2) environment substantiate the utility of the phase correction formula in calculating the phase disparity between the signals of the transmitting and receiving electromagnetic coils. This, in turn, enables the acquisition of the linear distance between these coils, which is instrumental in determining their spatial coordinates. The introduced 3D electromagnetic positioning methodology based on spectral interpolation effectively circumvents the issue of multiple solutions engendered by solving nonlinear equations inherent in traditional electromagnetic positioning systems. Simulation analyses corroborate that the proposed technique enhances phase detection accuracy by 1–2 orders of magnitude over conventional methods, achieving a positional accuracy within the effective measurement space of 3 mm—an improvement of at least 70% in comparison with established positioning paradigms.