A Novel 4D Track-before-Detect Approach for Weak Targets Detection in Clutter Regions

A 4D TBD approach is developed here for closely weak extended target tracking and overcoming heterogeneous clutter background and various clutter regions. The 4D measurements in this work are the points containing three positional information in spatial space and corresponding timestamp. The proposed method is mainly designed to address two issues. The first one is the dilemma between the weak target detection and difficult computation originating from the high dimensions of measurement. The second issue is the suppression of inhomogeneous background clutter and various clutter regions. The extension experiment using synthetic data showcases that no false alarm track would be built in the clutter regions, and the detection rate of close targets exceeds 94%. The experiments using real 3D radar also prove that the method works well in tracking closely maneuvering extended targets even if a clutter region exists.

A time-division correction method for adaptive optics system

An adaptive optics system can measure and compensate the wavefront distortion caused by dynamic disturbance in real time. It is usually used for astronomical observation and other occasions. According to the current technology, it is only suitable for small field of view optical system or point target, but not for ground extended target detection. In order to solve this problem, a correction method is proposed: firstly, the sub aperture image of wavefront sensor is divided into several sub regions, each sub region corresponds to a certain light direction or field angle range; secondly, calculate the offset of the image feature points in each sub region, and an image with good correction effect in a field of view in this direction is obtained; the last step is to measure each sub region one by one and combine these images into a full frame image. Through comparison, it is found that the method proposed in this paper is essentially to divide the extended target into multiple point light sources for correction.

Quantitative signal subspace imaging

We develop and analyze a quantitative signal subspace imaging method for single-frequency array imaging. This method is an extension to MUSIC (multiple signal classification) which uses (i) the noise subspace to determine the location and support of targets, and (ii) the signal subspace to recover quantitative information about the targets. For point targets, we are able to recover the complex reflectivity and for an extended target under the Born approximation, we are able to recover a scalar quantity that is related to the product of the volume and relative dielectric permittivity of the target. Our resolution analysis for a point target demonstrates this method is capable of achieving exact recovery of the complex reflectivity at subwavelength resolution. Additionally, this resolution analysis shows that noise in the data effectively acts as a regularization to the imaging functional resulting in a method that is surprisingly more robust and effective with noise than without noise.

EXTENDED TARGET WEIGHING APPROACH (ETWA): IMPACT AND RISK ANALYSIS OF LIGHTWEIGHT CONCEPTS IN THE PRODUCT-PRODUCTION SYSTEM-CO-DESIGN

Lightweight design in interconnected systems becomes more and more complex as the interdependencies cannot be overseen by the product developer. Varying one component might not only influence the interfaces to other components but also the underlying production systems.Therefore, this contribution focuses on the product/production interdependencies and how they can be supported within lightweight design. Based on a functional description of the product it is possible to derive new lightweight design solutions and also to evaluate the change propagation in the production system. For this, a method for the impact and risk analysis is integrated in the lightweight design method Extended Target Weighing Approach (ETWA). By doing so, a risk value for the adapted production system can be calculated and different design concepts can be compared.The application of the developed method on a simplified use-case shows great potentials when evaluating the impact of a newly developed lightweight design solution on an already existing production system supporting the product development in decision making.

Aspect Dependent-based Ghost Suppression for Extended Targets in Through-the-wall Radar Imaging under Compressive Sensing Framework

Several approaches have been proposed to suppress multipath ghost in through-the-wall radar imaging (TWRI). One classical approach, called Aspect Dependent (AD), exploits locations of ghosts in the images without demanding prior knowledge of the reflecting geometry. This operation strategy makes the method superior over multipath exploitation-based approaches. However, the AD method assumes a point target that emulates unreal environment. Therefore, reconstructing extended targets with this method leads to incorrect scene interpretation. This work proposes a ghost suppression method for extended targets based on the AD feature that exploits duo sub-apertures. Firstly, we evaluate the best suppression method using a performance metric called relative clutter peak. Next, the evaluated method is extended to encompass the target extent during sub-images reconstruction. Following this strategy, an effective image fusion method suitable for extended targets is proposed. The method considers pixel neighborhood to effectively recover the given extended target. Simulation results show that the proposed method significantly improves signal-to-clutter ratio and relative clutter peak by 8.8% and 23.8%, respectively, relative to the existing AD based methods under point target assumption.

MACHINE LEARNING TECHNIQUES FOR KNOWLEDGE EXTRACTION FROM SATELLITE IMAGES: APPLICATION TO SPECIFIC AREA TYPES

When we want to extract knowledge form satellite images, several well-known image classification and analysis techniques can be concatenated or combined to gain a more detailed target understanding. In our case, we concentrated on specific extended target areas such as polar ice-covered surfaces, forests shrouded by fire plumes, flooded areas, and shorelines. These image types can be described by characteristic features and statistical relationships. Here, we demonstrate that both multispectral (optical) as well as SAR (Synthetic Aperture Radar) images can be used for knowledge extraction. The free availability of image data provided by the European Sentinel-1 and Sentinel-2 satellites allowed us to conduct a series of experiments that verified our classification approaches. This could already be verified in our recent work by quantitative quality tests.