As recently demonstrated, compressive sensing (CS) is potential in exact recovery of an unknown sparse signal from very limited samples. In this paper, sub-Nyquist sampling jamming against inverse synthetic aperture radar (ISAR) imaging is presented, where the CS-based algorithm is applied to the high-resolution range profile (HRRP) reconstruction other than the Doppler profile reconstruction. The reconstruction of moving targets’ HRRP through CS-based matched filtering algorithms shows that the sub-Nyquist sampled jamming signals, formed by the under-sampled radar signals in scatter-wave jamming configuration, provide a capability of deception jamming. The finally reconstructed ISAR images show that the deceptive false-target images retaining the real target information will be induced. Hence, the rational utilization of sub-Nyquist sampling jamming can generate vivid decoys in ISAR countermeasures. Experimental results of the scattering model of the Yak-42 plane are used to verify the correctness of the jamming idea.

Sub-Nyquist Sampling Jamming Against ISAR With CS-Based HRRP Reconstruction

The micromotion dynamics and geometrical shape are considered to be essential characteristics for exoatmospheric targets discrimination. Many methods have been investigated to retrieve the micromotion features using radar signals returned from targets of a given shape. We explore a way of jointly estimating micromotion dynamics and geometrical shape parameters from the infrared (IR) signals of targets in remote detection distance. It is found that the micromotion dynamics of the target would induce a periodic fluctuating variation on the IR irradiance intensity signature. In addition to the micromotion characteristics, the fluctuation could also reflect target structure properties, which offer a possible clue in extracting the features of micromotion dynamics and geometrical shape. Thus, the data model of target IR irradiance intensity signatures induced by micromotion patterns including spinning, coning, and tumbling is developed, and a method of parameters estimation based on joint optimization analysis techniques is proposed. Experimental results demonstrated that the parameters of target micromotion dynamics and geometrical shape can be effectively estimated using the proposed method, if the input signature contains multiple dominant frequency components.

Micromotion dynamics and geometrical shape parameters estimation of exoatmospheric infrared targets

Micro-Doppler (MD) caused by the motion of the ballistic missile can contribute to successful recognition of the ballistic missile. Considering the real observation scenario. This paper proposes a method to derive the MD image of the ballistic missile by applying the range-Doppler algorithm (RDA) based on the real ∞ight scenario and analyzes the factor for the real-time MD imaging. Simulation results using the ∞ight trajectory constructed using the real target parameter demonstrate that we need a new cost function for phase adjustment and a new method for range alignment. In addition, matched-flltering needs to be performed in the baseband, and a su-cient PRF is required to prevent discontinuity of the MD image. Dechirping of MD and flltering of the random movement are also needed for a clear MD image. Among the various weapons used in the modern battlefleld, the ballistic missile in∞icts the biggest threat due to its high maneuvering speed and low radar cross section (RCS), and thus defending against the ballistic missile is a major issue. Recently, a theory has been developed to explain micro-Doppler (MD) efiect caused by the micro motion of the target and applied for radar target recognition purpose (1,2). In the case of the ballistic missile, three motion components | spinning, conning and nutation | cause MD, and they can be utilized for target recognition in combination with the motion parameter (1,3,4). However, very little research has been reported on its application to the real ∞ight scenario and the factor that needs to be considered for the real-time MD imaging. In this paper, considering the real observation scenario by a radar, we propose a method to extract an MD signature of a ballistic missile engaged in the real ∞ight scenario by applying the range-Doppler algorithm (RDA), which is generally used to form the inverse synthetic aperture radar (ISAR) image (5{8), and analyze various factors for the real-time high quality MD image. For this purpose, we constructed the ∞ight trajectory by using the real motion parameters of a 500km range scud missile conducted a translation motion compensation (TMC). Then, the time-varying MD image was formed by applying the time-frequency transform (TFT). Various simulations were performed by using the obtained MD image to study the requirement for the real-time MD imaging. Simulation results obtained by using a target composed of the point scatter demonstrate that the MD signature can be successfully constructed by using the range-Doppler algorithm. However, a new method for TMC is required for real-time high-quality MD imaging. In addition, matched-flltering (MF) in the baseband is required to form a focused image, and a su-cient PRF is needed to remove discontinuity. The re∞ected signal needs to be dechirped to reduce the required PRF, and a fllter needs to be designed to remove the random movement.

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Micro-Doppler Extraction and Analysis of the Ballistic Missile Using RDA Based on the Real Flight Scenario

In the real world, most objects and data have multiple types of attributes and inter-connections. Such data structures are named "Heterogeneous Information Networks" (HIN) and have been widely researched. Biological systems are also considered to be highly complicated HIN. In this work, we review various applications of HIN methods to biological and chemical data, discuss some advanced topics, and describe some future research directions.

Biological Systems as Heterogeneous Information Networks: A Mini-review and Perspectives

Point-of-interest (POI) recommendations are a popular form of personalized service in which users share their POI location and related content with their contacts in location-based social networks (LBSNs). The similarity and relatedness between users of the same POI type are frequently used for trajectory retrieval, but most of the existing works rely on the explicit characteristics from all users’ check-in records without considering individual activities. We propose a POI recommendation method that attempts to optimally recommend POI types to serve multiple users. The proposed method aims to predict destination POIs of a user and search for similar users of the same regions of interest, thus optimizing the user acceptance rate for each recommendation. The proposed method also employs the variable-order Markov model to determine the distribution of a user’s POIs based on his or her travel histories in LBSNs. To further enhance the user’s experience, we also apply linear discriminant analysis to cluster the topics related to “Travel” and connect to users with social links or similar interests. The probability of POIs based on users’ historical trip data and interests in the same topics can be calculated. The system then provides a list of the recommended destination POIs ranked by their probabilities. We demonstrate that our work outperforms collaborative-filtering-based and other methods using two real-world datasets from New York City. Experimental results show that the proposed method is better than other models in terms of both accuracy and recall. The proposed POI recommendation algorithms can be deployed in certain online transportation systems and can serve over 100,000 users.

/pdf/discovering-travel-community-for-poi-recommendation-on-4gtogu89zr.pdf

Discovering Travel Community for POI Recommendation on Location-Based Social Networks

Clustering analysis is a basic and essential method for mining heterogeneous information networks, which consist of multiple types of objects and rich semantic relations among different object types. Heterogeneous information networks are ubiquitous in the real-world applications, such as bibliographic networks and social media networks. Unfortunately, most existing approaches, such as spectral clustering, are designed to analyze homogeneous information networks, which are composed of only one type of objects and links. Some recent studies focused on heterogeneous information networks and yielded some research fruits, such as RankClus and NetClus. However, they often assumed that the heterogeneous information networks usually follow some simple schemas, such as bityped network schema or star network schema. To overcome the above limitations, we model the heterogeneous information network as a tensor without the restriction of network schema. Then, a tensor CP decomposition method is adapted to formulate the clustering problem in heterogeneous information networks. Further, we develop two stochastic gradient descent algorithms, namely, SGDClus and SOSClus, which lead to effective clustering multityped objects simultaneously. The experimental results on both synthetic datasets and real-world dataset have demonstrated that our proposed clustering framework can model heterogeneous information networks efficiently and outperform state-of-the-art clustering methods.

/pdf/a-tensor-cp-decomposition-method-for-clustering-50wk7gh6nq.pdf

A Tensor CP Decomposition Method for Clustering Heterogeneous Information Networks via Stochastic Gradient Descent Algorithms

According to the working mode of the ballistic missile warning radar (BMWR), the radar return from the BMWR is usually sparse. To recognize and identify the warhead, it is necessary to extract the precession frequency and the locations of the scattering centers of the missile. This article first analyzes the radar signal model of the precessing conical missile during flight and develops the sparse dictionary which is parameterized by the unknown precession frequency. Based on the sparse dictionary, the sparse signal model is then established. A nonlinear least square estimation is first applied to roughly extract the precession frequency in the sparse dictionary. Based on the time segmented radar signal, a sparse component analysis method using the orthogonal matching pursuit algorithm is then proposed to jointly estimate the precession frequency and the scattering centers of the missile. Simulation results illustrate the validity of the proposed method.

/pdf/precession-missile-feature-extraction-using-sparse-component-3xipa36lf5.pdf

Precession missile feature extraction using sparse component analysis of radar measurements

The heterogeneous information networks are omnipresent in real-world applications, which consist of multiple types of objects with various rich semantic meaningful links among them. Community discovery is an effective method to extract the hidden structures in networks. Usually, heterogeneous information networks are time-evolving, whose objects and links are dynamic and varying gradually. In such time-evolving heterogeneous information networks, community discovery is a challenging topic and quite more difficult than that in traditional static homogeneous information networks. In contrast to communities in traditional approaches, which only contain one type of objects and links, communities in heterogeneous information networks contain multiple types of dynamic objects and links. Recently, some studies focus on dynamic heterogeneous information networks and achieve some satisfactory results. However, they assume that heterogeneous information networks usually follow some simple schemas, such as bityped network and star network schema. In this paper, we propose a multityped community discovery method for time-evolving heterogeneous information networks with general network schemas. A tensor decomposition framework, which integrates tensor CP factorization with a temporal evolution regularization term, is designed to model the multityped communities and address their evolution. Experimental results on both synthetic and real-world datasets demonstrate the efficiency of our framework.

/pdf/multityped-community-discovery-in-time-evolving-3qitxemg44.pdf

Multityped Community Discovery in Time-Evolving Heterogeneous Information Networks Based on Tensor Decomposition

At present, natural scene text detection methods based on deep learning have achieved outstanding results in many applications. Hull number belongs to the text object, detecting the hull number successfully plays an important role in maritime military and shipping. However, the hull number occupies a relatively small area in the ship image as well as it could be blurred or deformed due to the reason of the photo environment, which made the accuracy of detecting the hull number directly on ship images has great room for improvement. Therefore, this article proposes a hull number detection method based on image super-resolution(SR), which first performs SR on a single ship image, then implements hull number detection on the SR ship image. To reduce the time consumption of the SR process, the original image is divided into multiple grids that perform SR in parallel. Finally, these multiple SR grids are synthesized into a new SR image. Experiments proved that the detection accuracy of the hull number is significantly improved on SR ship images.

Liu Lihua

Papers

A Tensor CP Decomposition Method for Clustering Heterogeneous Information Networks via Stochastic Gradient Descent Algorithms

Precession missile feature extraction using sparse component analysis of radar measurements

Multityped Community Discovery in Time-Evolving Heterogeneous Information Networks Based on Tensor Decomposition

Hull Number Detection for Ship Images Based on Image Super-Resolution