Jian Yang

Accurate estimation of the Doppler centroid is a prerequisite for achieving high-quality Synthetic Aperture Radar imaging. In highly squinted working scenarios, traditional frequency-domain methods depend on antenna pattern fitting. They are easily affected by pattern mismatch and strong scatterer interference. In addition, these methods cannot directly determine the Doppler ambiguity number. The range envelope-based Hough transform can correct linear range walk. It further realizes Doppler centroid estimation without ambiguity. However, range curvature hinders its estimation accuracy. To solve the above problem, this paper proposes a novel squint angle estimation scheme. The scheme organically combines closed-loop iterative range curvature compensation and energy-weighted Hough transform. Within a closed-loop iterative architecture comprising curvature compensation, line feature extraction, direction measurement, and angle refinement, the presented method progressively rectifies curved trajectories, yields robust squint angle estimates, and further derives the unambiguous Doppler centroid indirectly. Both simulated datasets and real airborne SAR measurements demonstrate the effectiveness and robustness of the proposed method.

Model merging combines expert models for multitask performance but faces challenges from parameter interference. This has sparked recent interest in controllable model merging, giving users the ability to explicitly balance performance trade-offs. Existing approaches employ a compile-then-query paradigm, performing a costly offline multi-objective optimization to enable fast, preference-aware model generation. This offline stage typically involves iterative search or dedicated training, with complexity that grows exponentially with the number of tasks. To overcome these limitations, we shift the perspective from parameter-space optimization to a direct correction of the model's final representation. Our approach models this correction as an optimal linear transformation, yielding a closed-form solution that replaces the entire offline optimization process with a single-step, architecture-agnostic computation. This solution directly incorporates user preferences, allowing a Pareto-optimal model to be generated on-the-fly with complexity that scales linearly with the number of tasks. Experimental results show our method generates a superior Pareto front with more precise preference alignment and drastically reduced computational cost.

A sparse antenna array of subarrays can effectively reduce the digital channels of array antennas, system complexities, and hardware cost while simultaneously increasing the antenna aperture. In this study, a new optimal design is proposed for a sparse antenna array of subarrays in the full-phased multiple input multiple output (FPMIMO) operation mode based on genetic algorithm–particle swarm optimization (GA–PSO) and ambiguity functions. The proposed algorithm can adaptively adjust the number of optimization iterations for yielding the optimization results of the PSO algorithm and GA to ensure the global optimization performance of algorithms and combine ambiguity functions to determine the final optimized sparse antenna array of subarrays. The effectiveness of the proposed algorithm is verified via simulation tests.

Many unmanned aerial vehicles (UAVs) require the installation of automatic dependent surveillance‐broadcast (ADS‐B) transponders to facilitate their daily management. However, since ADS‐B transponders do not have a good security mechanism, they introduce problems including impersonation, spoofing, and private changing of the registration number, making UAV surveillance inconvenient. Radio frequency fingerprinting (RFF) recognition is carried out by utilizing the fact that different electronic devices in a given transponder will affect the transmitted signals, resulting in the formation of RFF features that are unique to the transponder and difficult to forge. Therefore, in this work, a deep learning architecture is proposed to classify UAVs based on ADS‐B signals, and a multi‐head self‐attention RFF recognition model is constructed using variational mode decomposition (VMD) of the preamble data and a transformer encoder for validation. The model achieves better results in terms of noise, Doppler shifting, and multipath effect interference. This method demonstrates that the transformer architecture of natural language processing, combined with appropriate data preprocessing methods, can also be used for RFF recognition, and provides advantages in accuracy and robustness (67.83% vs. 64.17%).

Computing offloading strategies have emerged as a pivotal topic in IoT research, serving as a strategic approach to minimising latency and energy consumption in edge computing. Nevertheless, these advancements have also introduced new challenges, especially in the context of computationally intensive tasks and increasing data privacy requirements in IoT applications. Addressing these challenges requires innovative solutions, with blockchain technology emerging as a highly promising tool. Our work explores the integration of blockchain into IoT edge computing, specifically focusing on computing offloading. We have devised a system architecture that seamlessly integrates edge computing with blockchain technologies, aiming to tackle both optimisation and security concerns. By framing the offloading problem as a multi‐objective optimisation issue, we developed a Parallel Double‐Loop Particle Swarm Optimisation algorithm. This algorithm introduces a dual‐loop subpopulation structure based on broadcast feedback, which enhances information exchange between subpopulations and increases population diversity, thereby mitigating the risk of premature convergence to local optima. Moreover, an innovative collaborative mechanism is introduced, allowing multiple generations of updated particles to iterate simultaneously, significantly boosting the search efficiency of the population. Simulation results demonstrate that the PDPSO algorithm not only achieves significantly faster update speeds but also excels at identifying optimal solutions for the objective function. When compared to other existing algorithms, it offers superior overall performance and holds significant potential for addressing multidimensional constraints such as system delay and local energy consumption. Our findings suggest that this approach could be transformative for future IoT edge computing systems, providing a robust framework for efficient and secure task offloading.