In recent years, convolutional neural networks (CNNs) have been widely used in hyperspectral image (HSI) classification due to their exceptional performance in local feature extraction. However, due to the local join and weight sharing properties of the convolution kernel, CNNs have limitations in long-distance modeling, and deeper networks tend to increase computational costs. To address these issues, this paper proposes a vision Transformer (VIT) based on the light self-Gaussian-attention (LSGA) mechanism, which extracts global deep semantic features. Firstly, the hybrid spatial-spectral Tokenizer module extracts shallow spatial-spectral features and expands image patches to generate Tokens. Next, the light self-attention uses Q (Query), X (Origin input), and X instead of Q, K (Key), and V (Value) to reduce the computation and parameters. Furthermore, to avoid the lack of location information resulting in the aliasing of central and neighborhood features, we devise Gaussian absolute position bias to simulate HSI data distribution and make the attention weight closer to the central query block. Several experiments verify the effectiveness of the proposed method, which outperforms state-of-the-art methods on four datasets. Specifically, we observed a 0.62% accuracy improvement over A2S2K and a 0.11% improvement over SSFTT. In conclusion, the proposed LSGA-VIT method demonstrates promising results in HSI classification and shows potential in addressing the issues of location-aware long-distance modeling and computational cost. Our codes are available at https://github.com/machao132/LSGA-VIT.

Realizing robust infrared small target detection in complex backgrounds is of great essence for infrared search and tracking (IRST) applications. However, the high-intensity structures in background regions, such as the sharp edges, make it a challenging task, especially when the target is with low signal-to-clutter ratio (SCR). To address this issue, we propose an infrared small target detection method using local contrast-weighted multidirectional derivative (LCWMD). It is a robust detector that comprehensively considers the target property, background information, and the relation between them. First, we take into account the approximate isotropy of the infrared small target and present a new multidirectional derivative with penalty factors based on the Facet model to develop the target salience in the local region. Second, a dual local contrast fusion model with the tri-layer design is introduced to amplify the difference between the target and the background, so as to further suppress the high-intensity structural clutters. Finally, the LCWMD map is obtained by weighting the above two filtered maps, after which an adaptive segmentation operation is applied to accomplish the target detection. The results of comparative experiments implemented on real infrared images demonstrate that our method outperforms other state-of-the-art detectors by several times in terms of signal-to-clutter ratio gain (SCRG) and background suppression factor (BSF).

The space-time adaptive processing method have been widely used for both narrowband and wideband interference suppression in GNSS receiver equipped with array antenna. However, the use of space-time filter may cause satellite signal distortion and lead to bias in pseudo-range measurement. In addition, multipath is one of the main error sources that will have serious influence on the performance of GNSS receiver. For precise applications, the error induced by space-time filter and multipath should be reduced. In this paper, a cascaded interference and multipath suppression method using array antenna is proposed. In the first stage, the interference is suppressed by projecting the received array signal into the interference orthogonal subspace using subspace projection method. In the second stage, the directions of arrival (DOA) of the direct signal and the multipath signal are estimated using sparse recovery method. The DOA parameters enable the receiver to construct a space-time distortionless beamformer, which is used to null the multipath signals and maximize signal-to-noise ratio. The method does not require the prior knowledge of the direction of the direct signal, and achieves unbiased measurement while suppressing the interference and multipath. Simulation results demonstrate that the proposed method can achieve interference and multipath suppression effectively, and have a distortionless response for the direct signal.

The development of infrared search and tracking (IRST) systems owes a great deal to the crucial role played by infrared small target detection technology. However, the interferences caused by pixel-sized noises with high brightness (PNHB) and high-intensity structures in background regions make it a tough task to realize robust detection of low-contrast targets. To this end, we propose an infrared small target detection method named sub-maximum filtering and local intensity weighted gradient measure (SF-LIWGM) in this paper. First, a maximum eight-neighborhood sub-maximum (MENS) filter is designed to eliminate the PNHB without damaging the structure of the small target. Besides, we construct a novel local measure framework by aggregating the intensity and gradient information, in which the detection process is split into two branches: target saliency enhancement and high-intensity structural clutters (HISC) suppression. Finally, we adopt an adaptive segmentation operation to implement the target detection. The experimental results on real infrared data demonstrate that our method has satisfactory performance against other state-of-the-art algorithms in terms of signal-to-clutter ratio gain (SCRG) and background suppression factor (BSF), with an average improvement of 5.13 and 4.16, respectively.

Spoofing interference poses a significant challenge to the Global Navigation Satellite System (GNSS). To effectively combat intermediate spoofing signals, this paper presents an enhanced spoofing detection method based on abnormal energy of the quadrature (Q) channel correlators. The detailed principle of this detection method is introduced based on the received signal model under spoofing attack. The normalization parameter used in this method was the estimation of the noise floor. The performance of the proposed Q energy detector is validated through simulations, the Texas Spoofing Test Battery dataset and field tests. The results demonstrate that the proposed detector significantly enhances detection performance compared to signal quality monitoring methods, particularly in overpowered scenarios and dynamic scenarios. By increasing the detection probability in the presence of spoofing signals and decreasing the false alarm probability in the absence of spoofing signals, the proposed detector can better meet the requirements of practical applications.

The adaptive beamforming algorithm can realize interference suppression and navigation signal enhancement, and has been widely used. However, achieving high-precision real-time estimation of the direction of arrival (DOA) parameters of navigation signals in strong-interference scenarios with low complexity is still a challenge. In this paper, a blind adaptive beamforming algorithm for a Global Navigation Satellite System (GNSS) array receiver based on direction lock loop is proposed without using the prior information of the DOA parameter. The direction lock loop is used for DOA tracking and estimation after interference suppression, which uses the spatial correlation of the array beam pattern to construct a closed direction-tracking loop. The DOA estimation value is adjusted in real time based on the loop errors. A blind beamformer is constructed using the DOA estimation results to provide gain by forming a beam in the satellite direction. This method improves the accuracy and dynamic adaptability of DOA estimation while significantly reducing the computational complexity. The theoretical analysis and simulation results verify the effectiveness of the proposed algorithm.