Long-time coherent integration, which accumulates target returns across multiple range and Doppler resolution cells, is a powerful focusing technique for enhancing the detectability of a fast-maneuvering target. However, the focusing quality is inevitably degraded by range migration (RM) and Doppler frequency migration (DFM) resulting from target’s unknown maneuvers. Moreover, the irregular sampling induced by nonuniform pulse transmission and velocity ambiguity further complicates the task of motion parameter estimation and target detection. To overcome these obstacles, we propose an extended coherent integration framework featuring a novel phase–decoupled local resampling strategy, which enables accurate reconstruction of the nonuniform sampling signal onto a uniform grid under severe pulse repetition interval (PRI) agility. The proposed strategy first combines ambiguous Doppler phase precompensation with a computationally efficient local quadratic interpolation. Then, keystone transform (KT) is employed to rectify the linear RM, followed by a matched filtering process to eliminate the remaining coupling effects. Finally, with the distortions from RM and DFM effectively compensated, the detected targets achieve fine focusing after performing the target motion compensation.
The majority of radiation field mixed source location methods that are resistant to array manifold errors demonstrate a robust inhibition effect on a single array manifold error and exhibit commendable source location estimation ability. However, multiple array manifold errors are frequently encountered in practical application environments, and the location performance of the aforementioned antiarray manifold error method is observed to decline rapidly. The objective of this paper is to present a study of the location method of a radiation field mixed source with amplitude and phase error and array position error. This paper proposes an auxiliary source and noise subspace power iteration (AS-NSPI) method, which considers the specific impact of array manifold error on radiated far-field sources. Two auxiliary far-field sources with different frequencies are employed to estimate the amplitude and phase error matrix and the array position error. Subsequently, the covariance matrix of the array received data is compensated for by the results of the error estimation. Thereafter, the noise subspace is calculated by noise subspace power iteration (NSPI), and the source location is estimated by space spectrum search. The simulation results demonstrate that the AS-NSPI can circumvent the impact of multiarray manifold errors on source location and exhibits superior estimation accuracy.
In thin-film transistor (TFT) array manufacturing, material inconsistencies and process imperfections frequently induce defects that severely compromise production yield. Traditional manual inspection remains labor-intensive and susceptible to subjective bias, thereby limiting manufacturing capacity. While convolutional neural networks (CNNs) have shown promise in automating anomaly detection, the application of vision transformers (ViTs) in this domain remains relatively unexplored. This study introduces Gaussian Process Kolmogorov–Arnold Transformer (GP-KAT), a structure-aware architecture that combines the nonlinear representation capacity of the KAT with GP-inspired locally periodic kernel modeling and variance-aware anomaly prediction. To address the inherent periodicity of TFT array imagery, we propose a GPKAN layer that incorporates a locally periodic Gaussian kernel to enhance structural feature extraction. Furthermore, a feature variance augmented head is designed to explicitly quantify predictive uncertainty, facilitating more reliable anomaly scoring under ambiguous conditions. Extensive experiments on a newly curated industrial dataset show that GP-KAT outperforms representative ViT-based baselines under a unified training protocol. By improving detection accuracy and providing informative uncertainty estimates under the tested conditions, the proposed approach offers a promising direction for TFT array inspection.
As an emerging coherent jamming technique, interrupted sampling repeater jamming (ISRJ) can effectively disrupt radars employing linear frequency modulated (LFM) signals through mainlobe deception. To counter this critical threat, theoretical analysis of ISRJ principles is conducted, and an analytical model applicable to intrapulse modulated radar waveforms is developed. From multiple perspectives, including signal processing, system operation modes, and system-wide cooperative detection, interference suppression methods incorporating biphase-coded/slope-agile waveform scheduling, slope diversity, and external information support are designed. By integrating these methods, a comprehensive anti-jamming strategy is formulated and validated through simulations and experiments. The proposed approach leverages radar’s preemptive advantage and systemic capabilities without modifying the existing signal processing architecture, offering strong interference suppression, real-time adaptability, independence from prior jamming parameters, and low implementation complexity.
This study proposes an L-shaped super augmented nested array (LSANA) structure for two-dimensional (2-D) direction of arrival (DOA) estimation. The proposed LSANA structure consists of two linear arrays located along the x-axis and y-axis, respectively. Each linear array is a SANA that achieves lower mutual coupling (MC) and higher degrees of freedom (DOF). The azimuth and elevation angles are estimated separately for each part of the array, and then the pairing of these angles is achieved using the cross-covariance matrices of the respective data sections, thus enabling 2-D DOA estimation. Finally, through simulation experiments, the proposed LSANA is compared with other sparse array structures. The results indicate that the proposed LSANA can achieve higher DOF with lower MC, while also providing improved estimation accuracy for DOA.
