Sparse arrays, such as nested arrays and coprime arrays, can achieve a high number of degrees of freedom (DOFs) for direction of arrival (DOA) estimation with a reduced number of antennas. On the other hand, the compressive measurement method provides an effective way to reduce the number of front-end circuit chains. In this paper, we generalized current works on the two categories of methods to a compressed sparse array (CSA) scheme which combines the compressive measurement method and the sparse array together to significantly reduce the system complexity. After introducing the proposed scheme, the Cramar-Rao bound (CRB) of the proposed CSA scheme is derived. We then determine the corresponding existing conditions of the CRB, based on which the number of DOFs is derived and examined for the first time. It is proved that, for a CSA which compressed the output of an L-element sparse array to M < L chains, a higher number of DOFs is obtained as compared to that of the M-element array with the same sparse structure. Furthermore, the DOA estimation accuracy using the M-chain CSA is higher than that using the M-element sparse array due to the extended array aperture. Numerical simulations verify the superiority of the proposed CSA scheme.

Infrared ship detection technology plays a crucial role in ensuring maritime transportation and navigation safety. However, infrared ship targets at sea exhibit characteristics such as multi-scale, arbitrary orientation, and dense arrangements, with imaging often influenced by complex sea–sky backgrounds. These factors pose significant challenges for the fast and accurate detection of infrared ships. In this paper, we propose a new infrared ship target detection algorithm, YOLO-IRS (YOLO for infrared ship target), based on YOLOv10, which improves detection accuracy while maintaining detection speed. The model introduces the following optimizations: First, to address the difficulty of detecting weak and small targets, the Swin Transformer is introduced to extract features from infrared ship images. By utilizing a shifted window multi-head self-attention mechanism, the window field of view is expanded, enhancing the model’s ability to focus on global features during feature extraction, thereby improving small target detection. Second, the C3KAN module is designed to improve detection accuracy while also addressing issues of false positives and missed detections in complex backgrounds and dense occlusion scenarios. Finally, extensive experiments were conducted on an infrared ship dataset: compared to the baseline model YOLOv10, YOLO-IRS improves precision by 1.3%, mAP₅₀ by 0.5%, and mAP_50–95 by 1.7%. Compared to mainstream detection algorithms, YOLO-IRS achieves higher detection accuracy while requiring relatively fewer computational resources, verifying the superiority of the proposed algorithm and enhancing the detection performance of infrared ship targets.

Co-prime arrays can estimate the directions of arrival (DOAs) of $\mathcal{O}\left(MN\right)$ sources with $\mathcal{O}(M+N)$ sensors, and are convenient to analyze due to their closed-form expression for the locations of virtual lags. However, the number of degrees of freedom is limited due to the existence of holes in difference coarrays if subspace-based algorithms such as the spatial smoothing multiple signal classification (MUSIC) algorithm are utilized. To address this issue, techniques such as positive definite Toeplitz completion and array interpolation have been proposed in the literature. Another factor that compromises the accuracy of DOA estimation is the limitation of the number of snapshots. Coarray-based processing is particularly sensitive to the discrepancy between the sample covariance matrix and the ideal covariance matrix due to the finite number of snapshots. In this paper, coarray interpolation based on matrix completion (MC) followed by a denoising operation is proposed to detect more sources with a higher accuracy. The effectiveness of the proposed method is based on the capability of MC to fill in holes in the virtual sensors and that of MC denoising operation to reduce the perturbation in the sample covariance matrix. The results of numerical simulations verify the superiority of the proposed approach.

In this paper, we propose a novel direction-of-arrival (DOA) estimation scheme, which is named the compressive one-bit measurement scheme. In the proposed scheme, the one-bit quantization technique is used to reduce the system cost in terms of the analog-to-digital converter (ADC). However, the one-bit quantization leads to a serious information loss, thus compromising the estimation accuracy. Inspired by the compressive sensing theory, the compressive measurement method is used to expand the receive array aperture. To be specific, the compressive measurement allows more sensors than the number of front-end circuit chains to be used. Thus, the additional information is obtained, and the estimation performance can be improved from the system structure layer. It is noted that by introducing the compression operation, the relationship between the normalized and the original covariance matrix is different to the conventional one-bit quantization structure. Thus, two DOA estimation algorithms, i.e., the iterative compressive measurement-based multiple signal classification (CM-MUSIC) and the compressive sensing (CS)-based algorithm, are proposed for the compressive one-bit measurement scheme. The iterative CM-MUSIC can achieve a high-resolution estimation, whereas the CS-based algorithm can resolve more sources than the sensors. Numerical simulations validate the effectiveness of the proposed scheme.

Sparse arrays have gained considerable attention in recent years because they can resolve more sources than the number of sensors. The coprime array can resolve $\mathcal{O}\left(MN\right)$ sources with only $\mathcal{O}(M+N)$ sensors, and is a popular sparse array structure due to its closed-form expressions for array configuration and the reduction of the mutual coupling effect. However, because of the existence of holes in its coarray, the performance of subspace-based direction of arrival (DOA) estimation algorithms such as MUSIC and ESPRIT is limited. Several coarray interpolation approaches have been proposed to address this issue. In this paper, a novel DOA estimation approach via direct coarray interpolation is proposed. By using the direct coarray interpolation, the reshaping and spatial smoothing operations in coarray-based DOA estimation are not needed. Compared with existing approaches, the proposed approach can achieve a better accuracy with lower complexity. In addition, an improved angular resolution capability is obtained by using the proposed approach. Numerical simulations are conducted to validate the effectiveness of the proposed approach.

Sparse arrays can achieve a higher number of degrees of freedom (DOFs) compared with uniform linear array (ULA) counterparts. To further reduce the number of physical sensors while keeping a high number of DOFs, a direction of arrival (DOA) estimation algorithm by exploiting coprime frequencies base on a sparse ULA is recently proposed. However, the performance of such approach is not properly analyzed. In this letter, we analyze the Cramer-Rao bound (CRB) as the lower bound of the DOA estimation performance. The difference between the results presented in this letter and the recent CRB results on sparse arrays lies primarily in the additional phases occurred when utilizing different frequencies. It is shown in this letter that the phases affect the covariance matrix of the received data vector and, as a result, change the number of resolvable sources and alter the achieved CRB. We first demonstrate the effect of the additional phases with an example of two closely spaced sources, and the CRB for a sparse ULA exploiting two coprime frequencies is then derived. Numerical simulations are provided to validate the analyses.