Three-Dimensional Imaging Method for Array ISAR Based on Sparse Bayesian Inference

The problem of synthesis scatterers in inverse synthetic aperture radar (ISAR) make it difficult to realize high-resolution three-dimensional (3D) imaging. Radar array provides an available solution to this problem, but the resolution is restricted by limited aperture size and number of antennas, leading to deterioration of the 3D imaging performance. To solve these problems, we propose a novel 3D imaging method with an array ISAR system based on sparse Bayesian inference. First, the 3D imaging model using a sparse linear array is introduced. Then the elastic net estimation and Bayesian information criterion are introduced to fulfill model order selection automatically. Finally, the sparse Bayesian inference is adopted to realize super-resolution imaging and to get the 3D image of target of interest. The proposed method is used to process real radar data of a Ku band array ISAR system. The results show that the proposed method can effectively solve the problem of synthesis scatterers and realize super-resolution 3D imaging, which verify the practicality of our proposed method

Three-Dimensional Imaging Method for Array ISAR Based on Sparse Bayesian Inference

The problem of synthesis scatterers in inverse synthetic aperture radar (ISAR) make it difficult to realize high-resolution three-dimensional (3D) imaging. Radar array provides an available solution to this problem, but the resolution is restricted by limited aperture size and number of antennas, leading to deterioration of the 3D imaging performance. To solve these problems, we propose a novel 3D imaging method with an array ISAR system based on sparse Bayesian inference. First, the 3D imaging model using a sparse linear array is introduced. Then the elastic net estimation and Bayesian information criterion are introduced to fulfill model order selection automatically. Finally, the sparse Bayesian inference is adopted to realize super-resolution imaging and to get the 3D image of target of interest. The proposed method is used to process real radar data of a Ku band array ISAR system. The results show that the proposed method can effectively solve the problem of synthesis scatterers and realize super-resolution 3D imaging, which verify the practicality of our proposed method

Sparse Bayesian Learning Based Three-Dimensional Imaging Algorithm for Off-Grid Air Targets in MIMO Radar Array

In recent years, the development of compressed sensing (CS) and array signal processing provides us with a broader perspective of 3D imaging. The CS-based imaging algorithms have a better performance than traditional methods. In addition, the sparse array can overcome the limitation of aperture size and number of antennas. Since the signal to be reconstructed is sparse for air targets, many CS-based imaging algorithms using a sparse array are proposed. However, most of those algorithms assume that the scatterers are exactly located at the pre-discretized grids, which will not hold in real scene. Aiming at finding an accurate solution to off-grid target imaging, we propose an off-grid 3D imaging method based on improved sparse Bayesian learning (SBL). Besides, the Bayesian Cramér-Rao Bound (BCRB) for off-grid bias estimator is provided. Different from previous algorithms, the proposed algorithm adopts a three-stage hierarchical sparse prior to introduce more degrees of freedom. Then variational expectation maximization method is applied to solve the sparse recovery problem through iteration, during each iteration joint sparsity is used to improve efficiency. Experimental results not only validate that the proposed method outperforms the existing off-grid imaging methods in terms of accuracy and resolution, but have compared the root mean square error with corresponding BCRB, proving effectiveness of the proposed method

Comparison between Different TomoSAR Imaging Models for Airborne Platform Flying at Low Altitude

The classical planar-wavefront-based TomoSAR imaging model suffers from the problem that the effective integration interval is not enough to cover the target distribution region in the low-altitude airborne case. It will lead to a deterioration of the performance of tomogram reconstruction and inaccuracy of estimated scatterers. This paper reviews the exact and approximate forms of the aforementioned inaccurate model based on planar wavefront and points out the problem with the conventional model. To solve this problem, we propose spherical wavefront models with the exact form or an approximate form of the slant range formula. The estimated variable for the scatterer’s location is converted from elevation to off-nadir angle, and the effective integration interval has been extended. In addition, we explore relationships between the exact form of the conventional model and the exact form of the proposed model, and the relationship between the approximate form of the conventional model and the approximate form of the proposed model. This provides a basis for modifying the inversion algorithm that is designed based on the conventional model to adapt to the low-altitude airborne case. Eventually, through experiments based on simulated data and measured data, the imprecise reconstructions obtained with the conventional model are demonstrated, and the correctness of spherical wavefront models and the effectiveness of transformation between models are proved

A Novel Filtering Method of 3D Reconstruction Point Cloud from Tomographic SAR

With the development of airborne synthetic aperture radar (SAR) technology, the 3D SAR point cloud reconstruction has emerged as a crucial development trend in the current SAR community. However, due to measurement errors, environmental interference, radar decoherence, and other noises associated with the SAR system, the reconstructed tomogram is often deteriorated by numerous noisy scatterers. As a result, it becomes challenging to obtain high-quality 3D point clouds of the observed object, making it difficult to further process the point cloud and realize target identification. To address these issues, we propose a K nearest neighbor comprehensive weighted filtering algorithm. The filtered point cloud is evaluated quantitatively using three-dimensional entropy. In this study, we adopted various filtering methods for simulated data, P-band data of Genhe, and Ku-band data of Yuncheng to refine the tomogram and compare their performances. Both qualitative and quantitative analyses demonstrate the superiority of the filtering algorithm proposed in this paper

Formation of SiO₂‑Encapsulated Ag Nanoparticles on SiO₂ Nanofibers and Their Application as Robust, Flexible Pressure Sensor Working under High Temperatures

Lightweight, flexible pressure sensors working under high temperatures have intrigued great research interest owing to their potential in firefighting, aerospace technology, and automotive and petroleum industries. Here, we propose a strategy to prepare SiO2 shell-coated Ag nanoparticles on SiO2 nanofiber membranes (SNFs), which prohibit nanoparticle migration and fusion. The reduction treatment of the nanofiber membrane promotes the in situ reduction of AgNO3 into Ag seeds, which further grow as Ag nanoparticles in the following wet-chemical treatment. Then, a protective silica layer is fabricated on the Ag nanoparticles, which effectively prevents the nanoparticles’ migration and fusion; we propose that metal–support interaction (MSI) may help to form the silica coatings on the Ag nanoparticles. The as-formed Ag nanoparticles with a SiO2 coating layer are sinter-resistant and show much higher thermal stability than bare Ag nanoparticles on the nanofibers. The as-assembled pressure sensors using these SNFs as active materials can continuously work at 350 °C without performance decay. After annealing the SNF at 600 °C for 2 h, the SNF can still maintain its performance as a pressure sensor. The high-sensitivity, high-temperature-resistant, and robust SNF may provide a platform for unconventional pressure sensor construction. The strategies used in this study may bring insights into both the design and application of these sinter-resistant nanostructures