Infrared (IR) dim and small target detection in a highly complex background play an important role in many applications, and remain a challenging problem. In this paper, a novel method named stable multisubspace learning is presented to deal with this problem. The new method takes into account the inner structure of actual images so that it overcomes the shortage of the traditional method. First, by analyzing the multisubspace structure of heterogeneous background data, a corresponding image model is proposed using subspace learning strategy. This model is also stable to noise interference. Second, an efficient optimization algorithm is designed to solve the proposed IR image model. By adding the proper postprocessing procedure, we can get the detection result. Experiments on simulation scenes and real scenes show that the proposed method has superior detection ability under heterogeneous background.

Infrared (IR) small-target detection has been a widely studied task in IR search and tracking systems. It remains a challenging problem, especially in heterogeneous scenarios, where it is very difficult to discriminate true targets from sparse residuals in the background. A novel edge and corner awareness-based spatial–temporal tensor (ECA-STT) model is presented in this article. First, we construct an STT based on a spatial–temporal correlation analysis of the IR video background. Then, we propose an indicator to highlight the target through adjustable importance measurements of the edge and corner. The tensor-based nonlocal total variation is also adopted to describe the edges in the background. The target–background separation problem is modeled as a tensor robust principal component analysis (TRPCA) problem with the tensor rank function replaced by the tensor truncated nuclear norm. The proposed model is solved by an effective optimization algorithm derived from the alternating direction method of multipliers (ADMM). Extensive experiments verify the superior abilities of the proposed model in target enhancement and background suppression.

Infrared small target detection is one of the key techniques in the infrared search and track system. Frequency differences among target, background, and noise are often important information for target detection. In this letter, a nonnegativity-constrained variational mode decomposition (NVMD) method is proposed. Unlike the traditional frequency-domain methods, the proposed method can adaptively decompose the input signal into several separated band-limited subsignals, with the nonnegativity constraint. First, a bandpass filter is used as a preprocessing step. Second, by exploring the frequency and nonnegativity properties of the small target, the NVMD model is constructed. The potential target subsignal can be obtained by solving the NVMD model. By performing threshold segmentation on the potential target subsignal, we can obtain the detection result of the infrared small target. Experiments on six real infrared image sequences demonstrate that the proposed method has a good performance in target enhancement and background suppression. Additionally, the proposed method shows strong robustness under various backgrounds.

Radio access network (RAN) technologies continue to evolve, with Open RAN gaining the most recent momentum. In the O-RAN specifications, the RAN intelligent controllers (RICs) are software-defined orchestration and automation functions for the intelligent management of RAN. This article introduces principles for machine learning (ML), in particular, reinforcement learning (RL) applications in the O-RAN stack. Furthermore, we review the state-of-the-art research in wireless networks and cast it onto the RAN framework and the hierarchy of the O-RAN architecture. We provide a taxonomy for the challenges faced by ML/RL models throughout the development life-cycle: from the system specification to production deployment (data acquisition, model design, testing and management, etc.). To address the challenges, we integrate a set of existing MLOps principles with unique characteristics when RL agents are considered. This paper discusses a systematic model development, testing and validation life-cycle, termed: RLOps. We discuss fundamental parts of RLOps, which include: model specification, development, production environment serving, operations monitoring and safety/security. Based on these principles, we propose the best practices for RLOps to achieve an automated and reproducible model development process. At last, a holistic data analytics platform rooted in the O-RAN deployment is designed and implemented, aiming to embrace and fulfil the aforementioned principles and best practices of RLOps.

Robust detection of infrared small target is still a challenge due to the diversity and complexity of the background. In this letter, we propose a novel detection approach based on density peaks searching and maximum-gray region growing. The main idea is that infrared small targets can be described by three features: a relatively high density, a relatively large distance from pixels with higher density, and a relatively large density gap between targets and their neighbors. This idea helps to establish a detection procedure which can detect small targets of different sizes and remove the interference caused by clutters of various complex shapes. A quartile-based technique is introduced to obtain a more robust decision threshold for multiple scenes. Compared with eight state-of-the-art algorithms, the proposed method shows a superior detection performance and an acceptable efficiency in extensive experiments.