Ship detection is important for a wide range of applications. In this article, an airborne passive interferometric microwave sensor (PIMS) is proposed as a powerful complementary tool for ship detection. The fundamental of ship detection by the airborne PIMS is introduced. A specification called ``the detection factor'' is addressed to quantitatively assess the performance of the airborne PIMS for ship detection. The detection probability is discussed and addressed based on the constant false-alarm rate (CFAR). Numerical simulations are performed to demonstrate the feasibility of ship detection by the airborne PIMS. The impacts of the atmosphere and the sea wind on the performance of the airborne PIMS for ship detection are also addressed in terms of theoretical analyses and numerical simulations. The airborne experiments are also carried out by an X-band PIMS to verify the practicability of ship detection by the airborne PIMS in a sunny weather and a cloudy weather. The data processing procedure and a ship detection algorithm in the experiments are introduced. The airborne experimental results reveal that the ships can be effectively detected and tracked by the airborne X-band PIMS in a sunny weather and a cloudy weather. It demonstrates the practicability of ship detection by the airborne PIMS. The ship detection probability is also analyzed, which agrees well with the theoretical detection probability.

Passive millimeter-wave (PMMW) imaging is a powerful approach for detecting hidden objects underneath clothing. The theoretical basis of object detection methods is the contrast of brightness temperature (TB) image. TB differences may be caused by the diversity of material, physical temperature, surface structure, and so on. Existing methods are mainly based on single-polarization and single-pixel processing, which usually generate many discrete pixels of false alarms or missing detections. In this article, we present a regional-based method for hidden object detection using polarization and Fisher vector (FV) features. The necessity of polarization averaging for detection is revealed by theoretical simulation and experimental analyses. Based on the superpixel segmentation of polarization mean image, a modified FV, regional mean FV (RMFV), is created to extract concealed object features. Various imaging experimental data of typical security inspection scenarios are applied to verify the proposed method. The robustness and effectiveness are proved by comparing with several state-of-the-art methods.

Passive millimeter-wave and terahertz (MMW/THz) imaging is a significant technique used in personal security inspection and scene surveillance. However, a human body generally generates an obvious reflection on the floor, which may have negative influences on target detection and observer vision. In this article, we present a physically based method using dual-polarization feature and saliency information to solve the problem of reflection removal. The physical principle of a special dual-polarization phenomenon has been revealed by theoretical calculation and simulation analysis. By combining the second Stokes parameter (TQ) and modified linear polarization ratio (mLPR), a new feature parameter has been created to extract reflections of human bodies. By using a saliency analysis, the false positive alarms are eliminated completely. The experimental results demonstrate the robustness and effectiveness of the proposed method using various measured MMW/THz images.

Synthesis of thinned linear array geometry with low redundancy is an important problem for one-dimensional aperture synthesis radiometers (ASRs) with high spatial resolution and satisfactory sensitivity. In this paper, a synthesis method of low-redundancy linear array (LRLA) with large numbers of antennas is proposed. Two LRLA patterns are suggested to significantly reduce the tremendous solution space of large LRLA search problem and a strategy is proposed to speed up exploration. Further, particle swarm optimization (PSO) algorithm exploiting the suggested patterns and strategy is applied to the array synthesis. The advantages of the proposed method, in terms of both generating more new large LRLA configurations and reducing computational cost for the large LRLA configuration synthesis problem, are demonstrated by comparing its performance with that of the previously proposed methods. Finally, a number of newly found large LRLA configurations for 28-37 elements are also presented.

The Microwave Interferometric Radiometer with Aperture Synthesis (MIRAS) is the payload of the Soil Moisture and Ocean Salinity (SMOS) satellite mission led by the European Space Agency. Although the MIRAS operates at the protected L-band, it is perturbed by radio frequency interferences (RFIs) that contaminate the acquired remote sensing data and further deteriorate the total performance of SMOS mission. Accurate location information of these sources is crucial for switching off illegal RFI emitters or mitigating RFI impacts from contaminated data. This article addresses the localization of SMOS RFI sources from a perspective of sparse signal reconstruction (SSR), which exploits the sparsity of RFI sources in the spatial domain. Such an SSR strategy possesses superior (at least comparable) performances over existing RFI localization methods [e.g., discrete Fourier transformation (DFT) inversion and subspace-based direction-of-arrival (DOA) estimation] using only SMOS measurements and even under situations in the presence of data missing due to correlator failures.

The Soil Moisture and Ocean Salinity (SMOS) mission led by the European Space Agency (ESA) is aimed to globally monitor the Earth surface moisture and ocean salinity. As the single payload of SMOS satellite, the Microwave Interferometric Radiometer with Aperture Synthesis (MIRAS) operates in the protected L-band. Nonetheless, the artificial sources emitting close to or/and fully in this band are contaminating the collected remote sensing data and deteriorating the performance of the SMOS mission. Identifying and localizing such sources is crucial to improve the quality of SMOS scientific products. In this article, we propose a method based on matrix completion (MC) for localization of radio frequency interference (RFI) sources. This method mainly exploits the low-rank property of the augmented covariance matrix (ACM) of the sparse array and addresses the ACM incompleteness (i.e., sampling data loss) due to inherent array geometry (e.g., the SMOS Y-shaped array) or potential hardware malfunction (e.g., the correlator failure). Validation results show that, compared with existing RFI localization approaches such as the discrete Fourier transformation (DFT) inversion and covariance-based direction-of-arrival (DOA) estimation, the proposed MC method possesses competitive localization accuracy, superior spatial resolution, and reduced artifacts.