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A 2 T magnetic field system (second long) based on storage supercapacitor was designed and developed to fulfill the guiding magnetic field requirement of the high-power microwave devices. First, the basic principle of the supercapacitor magnetic field system was briefly introduced. Then the main parameters of the power supply and solenoid were discussed and calculated in detail. In the end, through the simulation and experiment, we verified that the magnetic field system based on supercapacitor could well meet guiding magnetic need. Finally, the roadblock and outlook of this magnetic system were analyzed generally. The method in this research would have certain reference value for the design of similar magnetic systems. [ABSTRACT FROM AUTHOR]

Multimodal image registration is the fundamental technique for scene analysis with series remote sensing images of different spectrum region. Due to the highly nonlinear radiometric relationship, it is quite challenging to find common features between images of different modal types. This paper resorts to the deep neural network, and tries to learn descriptors for multimodal image patch matching, which is the key issue of image registration. A Siamese fully convolutional network is set up and trained with a novel loss function, which adopts the strategy of maximizing the feature distance between positive and hard negative samples. The two branches of the Siamese network are connected by the convolutional operation, resulting in the similarity score between the two input image patches. The similarity score value is used, not only for correspondence point location, but also for outlier identification. A generalized workflow for deep feature based multimodal RS image registration is constructed, including the training data curation, candidate feature point generation, and outlier removal. The proposed network is tested on a variety of optical, near infrared, thermal infrared, SAR, and map images. Experiment results verify the superiority over other state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Automatic target recognition (ATR) of synthetic aperture radar (SAR) images is performed on either global or local features. The global features can be extracted and classified with high efficiency. However, they lack the reasoning capability thus can hardly work well under the extended operation conditions (EOCs). The local features are often more difficult to extract and classify but they provide reasoning capability for EOC target recognition. To combine the efficiency and robustness in an ATR system, a hierarchical fusion of the global and local features is proposed for SAR ATR in this paper. As the global features, the random projection features can be efficiently extracted and effectively classified by sparse representation-based classification (SRC). The physically relevant local descriptors, i.e., attributed scattering centers (ASCs), are employed for local reasoning to handle various EOCs like noise corruption, resolution variance, and partial occlusion. A one-to-one correspondence between the test and template ASC sets is built by the Hungarian algorithm. Then, the local reasoning is performed by evaluating individual matched pairs as well as the false alarms and missing alarms. For the test image to be recognized, it is first classified by the global classifier, i.e., SRC. Once a reliable decision is made, the whole recognition process terminates. When the decision is not reliable enough, it is passed to the local classifier, where a further classification by ASC matching is carried out. Therefore, by the hierarchical fusion strategy, the efficiency of global features and the robustness of local descriptors to various EOCs can be maintained jointly in the ATR system. Extensive experiments on the moving and stationary target acquisition and recognition data set demonstrate that the proposed method achieves superior effectiveness and robustness under both SOC and typical EOCs, i.e., noise corruption, resolution variance, and partial occlusion, compared with some other SAR ATR methods. [ABSTRACT FROM AUTHOR]

This paper presents an approach for attributed scattering center (ASC) matching with application to synthetic aperture radar (SAR) automatic target recognition (ATR). A statistics-based distance measure is designed to evaluate the distance between individual ASCs. Afterwards, the Hungarian algorithm is employed to build a one-to-one correspondence between two ASC sets. Based on the correspondence, a global similarity and a local similarity are designed to comprehensively evaluate the global consistency and structural correlation between those two ASC sets. The two similarities comprehensively exploit the inner correlation between the two ASC sets, thus providing a reliable and robust similarity measure for SAR ATR. The two similarities are then fused based on the Dempster–Shafer evidence theory to determine the target type by the maximum belief rule. Extensive experiments conducted on the moving and stationary target acquisition and recognition dataset and the comparison with several state-of-the-art methods demonstrate the validity and robustness of the proposed method. [ABSTRACT FROM PUBLISHER]

The quality of synthetic aperture radar (SAR) images and the completeness of the template database are two important factors in template-based SAR automatic target recognition. This letter gives a solution to the two factors by multilevel reconstruction of SAR targets using attributed scattering centers (ASCs). The ASCs of original SAR images are extracted to reconstruct the target’s image, which not only reduces the noise and background clutters but also keeps the electromagnetic characteristics of the target. Template database are reconstructed at multilevels to simulate various extents of ASC absence in the extended operation conditions. Therefore, the quality of SAR images as well as the completeness of the template database is augmented. Features are extracted from the augmented SAR images, and the classifier is trained by the augmented database for target recognition. Experimental results on the moving and stationary target acquisition and recognition data set demonstrate the validity of the proposed method. [ABSTRACT FROM PUBLISHER]

The pose of a rotational symmetry target in a wideband radar is the angle between its symmetry axis and the radar line of sight. It is a required parameter for target absolute attitude measurement using the bistatic radar measuring system. In this letter, a 3-D electromagnetic model (3-D EM model)-based method is proposed for estimating the target pose from fully polarimetric wideband radar measurements. The 3-D EM model is established offline based on the target geometric structure. Scattering features at different target poses can be accurately predicted by this model. In the proposed algorithm, a synthetic high-resolution range profile (HRRP), which is proved to be only related to the target pose, is first generated from the sum of echoes in radar copolarization channels. Then, by matching this synthetic HRRP with the synthetic HRRPs recovered by the 3-D EM model, the target pose is obtained. Experiments using both data predicted by a high-frequency electromagnetic code and data measured in an anechoic chamber verify the validity of the method. [ABSTRACT FROM PUBLISHER]

In wideband stepped-frequency radar systems, relative motions between radar and target can induce high distortions of high-resolution range profiles (HRRPs), such as range migration, shape deformation, and signal-to-noise-ratio (SNR) loss. These distortions, if ignored, can lead to unacceptable performance deterioration in detection. To solve this problem, a new algorithm for detecting moving range-spread targets (RSTs) is proposed in this letter. The proposed detector utilizes the waveform contrast of the HRRP to perform both motion compensation and constant false-alarm rate target detection, and it is simple and robust even for low-SNR scenarios. Simulated experiments are carried out to verify the effectiveness and advantages of the proposed detector. [ABSTRACT FROM PUBLISHER]

In this letter, a novel algorithm for attitude measurement based on a 3-D electromagnetic model (3-D em-model) is proposed. The 3-D em-model is established offline based on the geometric structure of the target, and it can be used to predict the scattering features at different target attitudes. In order to measure the attitude of the air target, we design a bistatic step frequency radar system. The directions of the two radars' lines of sight (LOSs) relative to the target are acquired by matching the high-resolution range profiles (HRRPs) from the target echoes to the HRRPs generated from the 3-D em-model. Since the directions of two radars' LOSs relative to the Earth are already known, the absolute attitude of the target can be acquired. The innovative contributions of this letter are as follows: 1) A comprehensive theoretical analysis of air target attitude measurement based on its own 3-D em-model is proposed; 2) the method can be applied to different kinds of air targets such as aircraft, satellite, missile, etc.; 3) the proposed attitude measurement method does not require target motion model in advance; and 4) the proposed algorithm can be applied to any kind of step frequency waveforms. Experiments using both data predicted by a high-frequency electromagnetic code and data measured in the chamber verify the validity of the method. [ABSTRACT FROM PUBLISHER]