Xidian University

About: Xidian University is a education organization based out in Xi'an, China. It is known for research contribution in the topics: Antenna (radio) & Computer science. The organization has 32099 authors who have published 38961 publications receiving 431820 citations. The organization is also known as: University of Electronic Science and Technology at Xi'an & Xīān Diànzǐ Kējì Dàxué.

Radar automatic target recognition using complex high-resolution range profiles

Abstract: Radar high-resolution range profile (HRRP) has received intensive attention from the radar automatic target recognition (RATR) community Usually, since the initial phase of a complex HRRP is strongly sensitive to target position variation, only the amplitude information in complex HRRPs is used for RATR, whereas the phase information is discarded However, the remaining phase information except for initial phases in complex HRRPs may also contain valuable target discriminant information RATR using complex HRRPs is discussed The complex HRRPs' feature subspace within each target-aspect sector is extracted via principal component analysis as the corresponding template during the training phase; while in the test phase we decide that a test sample belongs to the feature subspace which has the test sample's minimum reconstruction error approximation It is shown that the whole process is independent of the initial phases, but exploits the remaining phase information in complex HRRPs Furthermore, to make the proposed recognition method more practical, a fast time-shift compensation algorithm is proposed In the recognition experiments based on measured data, the proposed recognition method using complex HRRPs achieves better recognition results than that using only the amplitude vectors of the complex HRRPs

Coupled Dictionary Learning for Change Detection From Multisource Data

Abstract: With the increase of multisource data available from remote sensing platforms, it is demanding to develop unsupervised techniques for change detection from multisource data. The difference in imaging mechanism makes it difficult to carry out a direct comparison between multisource data in original observation spaces. Different sensors provide different descriptions on the same truth in low-dimension observation spaces, but the same truth indicates the comparability of multisource data in some high-dimensional feature spaces. Inspired by this, we try to solve this problem by transforming multisource data into a common high-dimension feature space. In this paper, an iterative coupled dictionary learning (CDL) model is proposed for multisource image change detection. This model aims to establish a pair of coupled dictionaries, one of which is responsible for the data from one sensor, whereas the other is responsible for the data from another sensor. The atoms from these two coupled dictionaries have a one-to-one correspondence at the same location. Such a property guarantees the transferability of the reconstruction coefficients between bitemporal patch pairs and provides us a desired mechanism to bridge multisource data and highlight changes. The contributions can be summarized as follows: CDL is designed to explore the intrinsic difference of multisource data for change detection in a high-dimension feature space, and an iterative scheme for unsupervised sample selection is proposed to keep the purity of training samples and gradually optimize the current coupled dictionaries. The experimental results have demonstrated the feasibility, effectiveness, and robustness of the proposed framework.

Theory of Secure Network Coding

Abstract: In this tutorial paper, we focus on the basic theory of linear secure network coding. Our goal is to present fundamental results and provide preliminary knowledge for anyone interested in the area. We first present a model for secure network coding and then a necessary and sufficient condition for a linear network code to be secure. Optimal methods to construct linear secure network codes are also provided. For further investigation of the secure properties of linear network codes, we illuminate different secure criteria and requirements, with a few alternative models.

A simple improved velocity estimation for low-speed regions based on position measurements only

Abstract: High-quality low-speed motion control calls for precise position and velocity signals. However, velocity estimation based on simple numerical differentiation from only the position measurement may be very erroneous, especially in the low-speed regions. A simple efficient high-quality instantaneous velocity estimation algorithm is developed in this paper, by using the position measurements only. The proposed estimator is constructed based on the fact that numerical integration can provide more stable and accurate results than numerical differentiation in the presence of noise. The main attraction of the new algorithm is that it is very effective as far as in low-speed ranges, high robustness against noise, and easy implementation with simple computation. Both extensive simulations and experimental tests have been performed to verify the effectiveness and efficiency of the proposed approach

Hyperspectral Anomaly Detection via Background and Potential Anomaly Dictionaries Construction

Abstract: In this paper, we propose a new anomaly detection method for hyperspectral images based on two well-designed dictionaries: background dictionary and potential anomaly dictionary. In order to effectively detect an anomaly and eliminate the influence of noise, the original image is decomposed into three components: background, anomalies, and noise. In this way, the anomaly detection task is regarded as a problem of matrix decomposition. Considering the homogeneity of background and the sparsity of anomalies, the low-rank and sparse constraints are imposed in our model. Then, the background and potential anomaly dictionaries are constructed using the background and anomaly priors. For the background dictionary, a joint sparse representation (JSR)-based dictionary selection strategy is proposed, assuming that the frequently used atoms in the overcomplete dictionary tend to be the background. In order to make full use of the prior information of anomalies hidden in the scene, the potential anomaly dictionary is constructed. We define a criterion, i.e., the anomalous level of a pixel, by using the residual calculated in the JSR model within its local region. Then, it is combined with a weighted term to alleviate the influence of noise and background. Experiments show that our proposed anomaly detection method based on potential anomaly and background dictionaries construction can achieve superior results compared with other state-of-the-art methods.