Weijia Cui

The rapid acquisition of burst direct sequence spread spectrum (DSSS) signals remains critically challenged by large Doppler offsets, especially in high-dynamic scenarios, such as low Earth orbit (LEO) satellite communications. Traditional solutions, including the partial matched filter fast Fourier transform (PMF-FFT) algorithm, incur high computational complexity when searching over wide frequency-uncertainty ranges and large code phase ranges, creating a bottleneck for systems that must handle massive volumes of burst signals. This article proposes an efficient acquisition algorithm that integrates a frequency-compensation strategy with a novel fast correlation method based on m-sequence properties. The core idea is to exploit the relationship between the Hadamard matrix and the m-sequence matrix to convert m-sequence parallel-correlation operations into fast Walsh–Hadamard transform (FWHT) computations. By combining frequency compensation, constructing augmented chip vectors, and implementing parallel code phase search via FWHT, the proposed approach markedly reduces the processing burden. Both theoretical analysis and simulation results demonstrate that the proposed algorithm achieves robust acquisition performance and high computational efficiency under high-dynamic conditions. This leads to a substantial reduction in computational overhead and significantly enhances the practicality of real-time burst communication systems.

Aiming at the weak signal enhancement, target detection and anti-jamming, and reduction of energy consumption faced by array radar technology in complex electromagnetic environments, this paper proposes a low sidelobe level high-gain adaptive beamforming method for uniform subarrays. The proposed method utilizes the virtual interference signals iteration method to greatly reduce the computational dimension on the basis of ensuring the output performance. Firstly the proposed algorithm compensates the delay of the array elements within the subarray with respect to the reference array element by element-level weighting and derives the array output after element-level weighting. Next the subarray-level interference plus noise covariance matrix was derived. Then the Lagrange method is applied to solve the weight vector for each iteration. Finally, the algorithm is made to iterate until convergence by adding virtual interference signals. The theoretical derivation and simulation experiments demonstrate the effectiveness and reliability of the proposed method.

The challenge of achieving high-precision direction-of-arrival (DOA) estimation with enhanced degrees of freedom (DOFs) under a limited number of physical array elements remains a critical issue in array signal processing. To address this limitation, this paper makes the following three key contributions: (1) a novel moving sparse array synthesis model incorporating time-frequency-spatial joint processing for coprime frequencies signal sources; (2) an optimized coprime frequencies-based unmanned aerial vehicle array (CF-UAVA) configuration with derived closed-form expressions for the distribution of synthesized array; and (3) two DOA estimation methods: a group sparsity-based approach universally applicable to the proposed aperture synthesis model and a joint group sparsity and virtual array interpolation tailored for the proposed CF-UAVA configuration. Comprehensive simulation results demonstrate the superior DOA estimation accuracy and increased DOFs achieved by our proposed aperture synthesis model and DOA estimation algorithms compared to conventional approaches.

Aiming at the problem of high computational complexity of adaptive beamforming techniques in array radar systems, a two-level weighted low-complexity adaptive beamforming method is proposed in this letter. First, the uniform linear array is divided into subarrays and each subarray has the same elements. The desired signal received by each array element in the subarray is then superimposed by compensating for the delay using a first level weighting. Finally, the interference signals and noise are suppressed using a second level weighting to obtain the ideal output signal to interference plus noise ratio. Simulation results verify the effectiveness and reliability of the proposed method.

Unmanned Aerial Vehicle (UAV) technology is increasingly gaining attention in localization systems due to its flexibility and mobility. However, traditional localization techniques often fail in complex environments where line-of-sight paths are obstructed. To address this challenge, this paper presents an innovative UAV-assisted high-precision multi-target localization system. The system utilizes UAVs equipped with Reconfigurable Intelligent Surfaces to create a reflective signal path, allowing a receiver sensor to capture these signals, creating favorable conditions for multi-target localization. Exploiting the sparsity of signals, we introduce a direct positioning algorithm that leverages Atomic Norm Minimization (ANM) to estimate the target’s location. To address the high complexity of traditional ANM methods, we propose a novel Coyote-ANM-based direct localization (CADL) approach. This method combines the coyote optimization algorithm with the alternating direction method of multipliers to achieve high-accuracy positioning with reduced computational complexity. Simulation results across various signal-to-noise ratio scenarios demonstrate that the proposed algorithm significantly improves localization accuracy, achieving lower root mean square error values and faster execution times compared to traditional methods.

In satellite communication, the high-speed moving source will cause a large frequency offset when the transmission signal arrives at the receiver, and the existence of frequency offset will seriously interfere with the demodulation of the signal, making the receiver unable to obtain the information it carries from the transmission signal. In this paper, the influence of frequency offset and phase offset on modulation signal and constellation is analyzed. In order to eliminate the frequency offset in the process of signal transmission, a carrier frequency offset estimation algorithm for APSK modulated signals is proposed. By setting one or more decision thresholds, the algorithm enlarges the signal points on the inner ring to the outermost ring in equal proportion, and thus realizes the data combination of the signal points on the APSK signal constellation, and then removes the modulation information. Finally, the FFT operation is used to extract the peak value of the signal points, and the estimated value of the carrier frequency offset can be obtained. Through simulation experiments, this algorithm has low complexity, short running time, high accuracy of estimation results, and has a large range of frequency offset estimation compared with kay algorithm, Fitz algorithm, M&M algorithm, L&R algorithm, etc., which is more suitable for satellite communication and other applications with significant Doppler effect.

In the area of direction-of-arrival (DOA) estimate research, Unknown mutual coupling (UMC) causes significant interference with the array’s localization performance. Although the sparsity of the structure of the sparse array can weaken the mutual coupling (MC) to some extent, the residual MC still has a substantial effect on the localization performance. In this paper, a sparsity-based localization method for mixed near-field (NF) and far-field (FF) sources with UMC is developed. On the basis of the rank reduction (RARE) principle, multiple parameters of the source are decoupled. Therefore the one-dimensional (1-D) search is necessary to estimate the aforementioned factors. The sparse array localization performance is greatly improved by the offsetting of MC parameters using proposed method. Simulation experiments show that this method significantly improves the performance of mixed NF and FF sources localization under the UMC environment.

Deep learning-based automatic modulation recognition networks are susceptible to adversarial attacks, posing significant performance vulnerabilities. In response, we introduce a defense framework enriched by tailored autoencoder (AE) techniques. Our design features a detection AE that harnesses reconstruction errors and convolutional neural networks to discern deep features, employing thresholds from reconstruction error and Kullback–Leibler divergence to identify adversarial samples and their origin mechanisms. Additionally, a restoration AE with a multi-layered structure effectively restores adversarial samples generated via optimization methods, ensuring accurate classification. Tested rigorously on the RML2016.10a dataset, our framework proves robust against adversarial threats, presenting a versatile defense solution compatible with various deep learning models.

In passive localization techniques, as the scale of the array of the sensors used increases, the source distribution may be a coexistence of near-field (NF) and far-field (FF) sources. Most of the existing algorithms dedicated to the localization of mixed-field sources are based on a simplified model, which has model errors and cannot make good use of non-circular properties when non-circular signals are present in the sources. In this paper, we present a mixed-field circular and non-circular source localization algorithm based on exact spatial propagation geometry. First, we make an initial estimate of the source parameters using exact spatial geometry relations. The MUSIC algorithm is then used in combination with the non-circular properties of the signal to achieve an accurate estimate. The algorithm does not lose performance due to model mismatch and is able to make good use of the non-circular properties of the sources to improve the estimation accuracy. The simulation results show that the proposed algorithm can effectively distinguish between sources and that the algorithm performs satisfactorily.