The vortex electromagnetic (EM) wave carrying orbital angular momentum (OAM) offers a new degree of freedom for radar techniques, although to date its application to synthetic aperture radar (SAR) imaging has not yet been widely reported. In this paper, a SAR imaging model based on vortex EM waves is derived. Ku-band vehicle-borne vortex EM waves SAR experiment using phased uniform circular array (UCA) antennas is reported for the first time. A detailed comparison with conventional plane EM waves SAR imaging is given in terms of signal-to-noise ratio, and azimuth resolution. The experimental results demonstrate the effectiveness of the proposed imaging model, and indicate that vortex EM waves can be exploited in SAR imaging with higher azimuth resolution than plane EM waves. OAM, which is completely independent of frequency and polarization, is a new degree of freedom in SAR imaging. This work shows the new applications of vortex EM waves, which paves the way for the development of novel SAR systems.

Accurate localization and reliable mapping is essential for autonomous navigation of robots. As one of the core technologies for autonomous navigation, Simultaneous Localization and Mapping (SLAM) has attracted widespread attention in recent decades. Based on vision or LiDAR sensors, great efforts have been devoted to achieving real-time SLAM that can support a robot’s state estimation. However, most of the mature SLAM methods generally work under the assumption that the environment is static, while in dynamic environments they will yield degenerate performance or even fail. In this paper, first we quantitatively evaluate the performance of the state-of-the-art LiDAR-based SLAMs taking into account different pattens of moving objects in the environment. Through semi-physical simulation, we observed that the shape, size, and distribution of moving objects all can impact the performance of SLAM significantly, and obtained instructive investigation results by quantitative comparison between LOAM and LeGO-LOAM. Secondly, based on the above investigation, a novel approach named EMO to eliminating the moving objects for SLAM fusing LiDAR and mmW-radar is proposed, towards improving the accuracy and robustness of state estimation. The method fully uses the advantages of different characteristics of two sensors to realize the fusion of sensor information with two different resolutions. The moving objects can be efficiently detected based on Doppler effect by radar, accurately segmented and localized by LiDAR, then filtered out from the point clouds through data association and accurate synchronized in time and space. Finally, the point clouds representing the static environment are used as the input of SLAM. The proposed approach is evaluated through experiments using both semi-physical simulation and real-world datasets. The results demonstrate the effectiveness of the method at improving SLAM performance in accuracy (decrease by 30% at least in absolute position error) and robustness in dynamic environments.

Acoustic detection is of great significance because of its wide applications. This paper reports a Micro-Electro-Mechanical System (MEMS) acoustic sensor based on grating interferometer. In the MEMS structure, a diaphragm and a micro-grating made up the interference cavity. A short-cavity structure was designed and fabricated to reduce the impact of temperature on the cavity length in order to improve its stability against environment temperature variations. Besides this, through holes were designed in the substrate of the grating to reduce the air damping of the short-cavity structure. A silicon diaphragm with a 16.919 µm deep cavity and 2.4 µm period grating were fabricated by an improved MEMS process. The fabricated sensor chip was packaged on a conditioning circuit with a laser diode and a photodetector for acoustic detection. The output voltage signal in response to an acoustic wave is of high quality. The sensitivity of the acoustic sensor is up to −15.14 dB re 1 V/Pa @ 1 kHz. The output signal of the high-stability acoustic sensor almost unchanged as the environment temperature ranged from 5 °C to 55 °C.

Recent advances in very high-resolution tomographic synthetic aperture radar (SAR) inversion using multiple data stacks from different viewing angles enable us to reconstruct the reflectivity function along the elevation direction by means of spectral analysis for every azimuth-range pixel. They can be potentially used for facade reconstruction and deformation monitoring in an urban area. When SAR tomographic processing is used to form 3-D point clouds, some points that we simply omitted in the traditional 3-D reconstruction are difficult to understand. It comes to our attention that this special detail which seems violated to a common practice is due to the mechanism of multipath (MP) scattering. Dominated by SAR tomography process, MP scattering reveals many interesting phenomena in 3-D point clouds. In this paper, a theoretical model of the MP scattering of the structure of dihedral corner which often appears in the urban area is given. There are two groups of points distributed symmetrically and compactly around the base angle of the building. The positions of these two groups of the points can be predicted by our model. Corresponding experiments are carried out to show the validity. The position is relative to the height of the building which indicates a direct utilization of our model to estimate the height of the building directly without 3-D reconstruction.

Using millimeter-wave radar to scan and detect small foreign object debris (FOD) on an airport runway surface is a popular solution in civil aviation safety. Since it is impossible to completely eliminate the interference reflections arising from strongly scattering targets or non-homogeneous clutter after clutter cancellation processing, the consequent high false alarm probability has become a key problem to be solved. In this article, we propose a new FOD detection method for interference suppression and false alarm reduction based on an iterative adaptive approach (IAA) algorithm, which is a non-parametric, weighted least squares-based iterative adaptive processing approach that can provide super-resolution capability. Specifically, we first obtain coarse FOD target information by data preprocessing in a conventional detection method. Then, a refined data processing step is conducted based on the IAA algorithm in the azimuth direction. Finally, multiple pieces of information from the two steps above are used to comprehensively distinguish false alarms by fusion processing; thus, we can acquire accurate FOD target information. Real airport data measured by a 93 GHz radar are used to validate the proposed method. Experimental results of the test scene, which include golf balls with a diameter of 43 mm, were placed about 300 m away from radar, which show that the proposed method can effectively reduce the number of false alarms when compared with a traditional FOD detection method. Although metal balls with a diameter of 50 mm were placed about 660 m away from radar, they also can obtain up to 2.2 times azimuth super-resolution capability.

It has become a field of intensive research to exploit SAR tomography to reconstruct a 3-D model of the buildings. However, in multiple-input multiple-output (MIMO) SAR tomography, the double-bounce reflections of the building corner will cause symmetric virtual scatterers, affecting both the scattering coefficient and structure of the 3-D model. To solve this problem, the building corner reflection is discussed and compressive sensing (CS)-based corner reflection suppression (CSCRS) is proposed. In the final part of this letter, the effectiveness of the proposed method is validated using array InSAR data. It is found that the proposed method leads to considerable improvements with regard to suppression ratio and reconstruction accuracy.