Time series analysis is a key technology for medical diagnosis, weather forecasting and financial prediction systems. However, missing data frequently occur during data recording, posing a great challenge to data mining tasks. In this study, we propose a novel time series data representation-based denoising autoencoder (DAE) for the reconstruction of missing values. Two data representation methods, namely, recurrence plot (RP) and Gramian angular field (GAF), are used to transform the raw time series to a 2D matrix for establishing the temporal correlations between different time intervals and extracting the structural patterns from the time series. Then an improved DAE is proposed to reconstruct the missing values from the 2D representation of time series. A comprehensive comparison is conducted amongst the different representations on standard datasets. Results show that the 2D representations have a lower reconstruction error than the raw time series, and the RP representation provides the best outcome. This work provides useful insights into the better reconstruction of missing values in time series analysis to considerably improve the reliability of time-varying system.

Based on the finite element method (FEM) in the frequency domain and particle-in-cell approach in the time domain, a hybrid domain multipactor threshold prediction algorithm is proposed in this paper. The proposed algorithm has the advantages of the frequency domain and the time domain algorithms at the same time in terms of high computational accuracy and considerable computational efficiency. In addition, the compute unified device architecture (CUDA) acceleration technique also can be employed to further enhance its simulation efficiency. Numerical examples are carried out to demonstrate the effectiveness of the proposed algorithm. The results indicate that the multipactor threshold can be accurately predicted and the computational efficiency can be improved.

The hypersonic target detection and recognition system is studied, on the basis of overall planning and design, a multi-agent system (MAS) structure and intelligent⁺ information processing mechanism based on target detection and recognition are proposed, and the multi-agent operation process is analyzed and designed in detail. In the specific agents construction, the information fusion technology is introduced to defining the embedded agents and their interrelations in the system structure, and the intelligent processing ability of complex and uncertain problems is emphatically analyzed from the aspects of autonomy and collaboration. The aim is to optimize the information processing strategy of the hypersonic target detection and recognition system and improve the robustness and rapidity of the system.

In this paper, a sparse nonuniform rectangular array based on spatially spread electromagnetic vector sensor (SNRA-SSEMVS) is introduced, and a method for estimating 2D-direction of arrival (DOA) and polarization is devised. Firstly, according to the special structure of the sparse nonuniform rectangular array (SNRA), a set of accurate but ambiguous direction-cosine estimates can be obtained. Then the steering vector of spatially spread electromagnetic vector sensor (SSEMVS) can be extracted from the array manifold to obtain the coarse but unambiguous direction-cosine estimates. Finally, the disambiguation approach can be used to get the final accurate estimates of 2D-DOA and polarization. Compared with some existing methods, the SNRA configuration extends the spatial aperture and refines the parameters estimation accuracy without adding any redundant antennas, as well as reduces the mutual coupling effect. Moreover, the proposed algorithm resolves multiple sources without the priori knowledge of signal information, suffers no ambiguity in the estimation of the Poynting vector, and pairs the x-axis direction cosine with the y-axis direction cosine automatically. Simulation results are given to verify the effectiveness and superiority of the proposed algorithm.

Three-dimensional (3D) reconstruction based on aerial images has broad prospects, and feature matching is an important step of it. However, for high-resolution aerial images, there are usually problems such as long time, mismatching and sparse feature pairs using traditional algorithms. Therefore, an algorithm is proposed to realize fast, accurate and dense feature matching. The algorithm consists of four steps. Firstly, we achieve a balance between the feature matching time and the number of matching pairs by appropriately reducing the image resolution. Secondly, to realize further screening of the mismatches, a feature screening algorithm based on similarity judgment or local optimization is proposed. Thirdly, to make the algorithm more widely applicable, we combine the results of different algorithms to get dense results. Finally, all matching feature pairs in the low-resolution images are restored to the original images. Comparisons between the original algorithms and our algorithm show that the proposed algorithm can effectively reduce the matching time, screen out the mismatches, and improve the number of matches.

To solve the problem of time difference of arrival (TDOA) positioning and tracking of targets by the unmanned aerial vehicles (UAV) swarm in future air combat, this paper adopts the TDOA positioning method and uses time difference sensors of the UAV swarm to locate target radiation sources. Firstly, a TDOA model for the target is set up for the UAV swarm under the condition that the error variance varies with the received signal-to-noise ratio. The accuracy of the positioning error is analyzed by geometric dilution of precision (GDOP). The D-optimality criterion of the positioning model is theoretically derived. The target is positioned and settled, and the maximum value of the Fisher information matrix determinant is used as the optimization objective function to optimize the track of the UAV in real time. Simulation results show that the track optimization improves the positioning accuracy and stability of the UAV swarm to the target.

When range high-resolution radar is applied to target recognition, it is quite possible for the high-resolution range profiles (HRRPs) of group targets in a beam to overlap, which reduces the target recognition performance of the radar. In this paper, we propose a group target recognition method based on a weighted mean shift (weighted-MS) clustering method. During the training phase, subtarget features are extracted based on the template database, which is established through simulation or data acquisition, and the features are fed to the support vector machine (SVM) classifier to obtain the classifier parameters. In the test phase, the weighted-MS algorithm is exploited to extract the HRRP of each subtarget. Then, the features of the subtarget HRRP are extracted and used as input in the SVM classifier to be recognized. Compared to the traditional group target recognition method, the proposed method has the advantages of requiring only a small amount of computation, setting parameters automatically, and having no requirement for target motion. The experimental results based on the measured data show that the method proposed in this paper has better recognition performance and is more robust against noise than other recognition methods.

Tomographic synthetic aperture radar (TomoSAR) imaging exploits the antenna array measurements taken at different elevation aperture to recover the reflectivity function along the elevation direction. In these years, for the sparse elevation distribution, compressive sensing (CS) is a developed favorable technique for the high-resolution elevation reconstruction in TomoSAR by solving an $L_1 $ regularization problem. However, because the elevation distribution in the forested area is non-sparse, if we want to use CS in the recovery, some basis, such as wavelet, should be exploited in the sparse representation of the elevation reflectivity function. This paper presents a novel wavelet-based $L_{{1}/{2}} $ regularization CS-TomoSAR imaging method of the forested area. In the proposed method, we first construct a wavelet basis, which can sparsely represent the elevation reflectivity function of the forested area, and then reconstruct the elevation distribution by using the $L_{{1}/{2}} $ regularization technique. Compared to the wavelet-based $L_1 $ regularization TomoSAR imaging, the proposed method can improve the elevation recovered quality efficiently.

In target tracking applications, the Doppler measurement contains information of the target range rate, which has the potential capability to improve the tracking performance. However, the nonlinear degree between the measurement and the target state increases with the introduction of the Doppler measurement. Therefore, target tracking in the Doppler radar is a nonlinear filtering problem. In order to handle this problem, the Kalman filter form of best linear unbiased estimation (BLUE) with position measurements is proposed, which is combined with the sequential filtering algorithm to handle the Doppler measurement further, where the statistic characteristic of the converted measurement error is calculated based on the predicted information in the sequential filter. Moreover, the algorithm is extended to the maneuvering target tracking case, where the interacting multiple model (IMM) algorithm is used as the basic framework and the model probabilities are updated according to the BLUE position filter and the sequential filter, and the final estimation is a weighted sum of the outputs from the sequential filters and the model probabilities. Simulation results show that compared with existing approaches, the proposed algorithm can realize target tracking with preferable tracking precision and the extended method can achieve effective maneuvering target tracking.

The issue of small-angle maneuvering targets inverse synthetic aperture radar (ISAR) imaging has been successfully addressed by popular motion compensation algorithms. However, when the target’s rotational velocity is sufficiently high during the dwell time of the radar, such compensation algorithms cannot obtain a high quality image. This paper proposes an ISAR imaging algorithm based on keystone transform and deep learning algorithm. The keystone transform is used to coarsely compensate for the target’s rotational motion and translational motion, and the deep learning algorithm is used to achieve a super-resolution image. The uniformly distributed point target data are used as the data set of the training u-net network. In addition, this method does not require estimating the motion parameters of the target, which simplifies the algorithm steps. Finally, several experiments are performed to demonstrate the effectiveness of the proposed algorithm.

The detection and recognition of radar signals play a critical role in the maintenance of future electronic warfare (EW). So far, however, there are still problems with signal detection and recognition, especially in the low probability of intercept (LPI) radar. This paper explores the usefulness of such an algorithm in the scenario of LPI radar signal detection and recognition based on visibility graphs (VG). More network and feature information can be extracted in the VG two-dimensional space, this algorithm can solve the problem of signal recognition using the autocorrelation function. Wavelet denoising processing is introduced into the signal to be tested, and the denoised signal is converted to the VG domain. Then, the signal detection is performed by using the constant false alarm of the VG average degree. Next, weight the converted graph. Finally, perform feature extraction on the weighted image, and use the feature to complete the recognition. It is testified that the proposed algorithm offers significant improvements, such as robustness to noise, and the detection and recognition accuracy, over the recent researches.

Most existing studies about passive radar systems are based on the already known illuminator of opportunity (IO) states. However, in practice, the receiver generally has little knowledge about the IO states. Little research has studied this problem. This paper analyzes the observability and estimability for passive radar systems with unknown IO states under three typical scenarios. Besides, the directions of high and low estimability with respect to various states are given. Moreover, two types of observations are taken into account. The effects of different observations on both observability and estimability are well analyzed. For the observability test, linear and nonlinear methods are considered, which proves that both tests are applicable to the system. Numerical simulations confirm the correctness of the theoretical analysis.

The noise robustness and parameter estimation performance of the classical three-dimensional estimating signal parameter via rotational invariance techniques (3D-ESPRIT) algorithm are poor when the parameters of the geometric theory of the diffraction (GTD) model are estimated at low signal-to-noise ratio (SNR). To solve this problem, a modified 3D-ESPRIT algorithm is proposed. The modified algorithm improves the parameter estimation accuracy by proposing a novel spatial smoothing technique. Firstly, we make cross-correlation of the auto-correlation matrices; then by averaging the cross-correlation matrices of the forward and backward spatial smoothing, we can obtain a novel equivalent spatial smoothing matrix. The formula of the modified algorithm is derived and the performance of this improved method is also analyzed. Then we compare root-mean-square-errors (RMSEs) of different parameters and the locating accuracy obtained by different algorithms. Furthermore, radar cross section (RCS) of radar targets is extrapolated. Simulation results verify the effectiveness and superiority of the modified 3D-ESPRIT algorithm.

The system portfolio selection is a fundamental frontier issue in the development planning and demonstration of weapon equipment. The scientific and reasonable development of the weapon system portfolio is of great significance for optimizing the design of equipment architecture, realizing effective resource allocation, and increasing the campaign effectiveness of integrated joint operations. From the perspective of system-of-systems, this paper proposes a unified framework called structure-oriented weapon system portfolio selection (SWSPS) to solve the weapon system portfolio selection problem based on structural invulnerability. First, the types of equipment and the relationship between the equipment are sorted out based on the operation loop theory, and a heterogeneous combat network model of the weapon equipment system is established by abstracting the equipment and their relationships into different types of nodes and edges respectively. Then, based on the combat network model, the operation loop comprehensive evaluation index (OLCEI) is introduced to quantitatively describe the structural robustness of the combat network. Next, a weapon system combination selection model is established with the goal of maximizing the operation loop comprehensive evaluation index within the constraints of capability requirements and budget limitations. Finally, our proposed SWSPS is demonstrated through a case study of an armored infantry battalion. The results show that our proposed SWSPS can achieve excellent performance in solving the weapon system portfolio selection problem, which yields many meaningful insights and guidance to the future equipment development planning.

The FloripaSat-I project consists of an initiative from the Federal University of Santa Catarina (UFSC), in Brazil, to train students to design, test and integrate innovative space systems. The group just developed its first open-source CubeSat, the FloripaSat-I, which aims to empower students to develop space systems through a practical approach, where they have full control of the design and test of a real spacecraft. The project has already gone through all the stages of a CubeSat mission prior to the launching and operation stages. A prototype of the satellite, as well the engineering models 1 and 2 (EM-I and EM-II) were built. The expertise provided by the engineering models allows the development of a functional flight model (FM). This paper presents the validation and qualification tests that pass various FloripaSat-I models, from the engineering model to the flight model. All stages of the project are described, the tests performed in each phase, as well as the lessons learned. Thus, this paper serves as a guidance for other university teams that want to test their own CubeSats, as well as teams that want to use the open-source hardware and software left as heritage by this project.

Clustering is one of the unsupervised learning problems. It is a procedure which partitions data objects into groups. Many algorithms could not overcome the problems of morphology, overlapping and the large number of clusters at the same time. Many scientific communities have used the clustering algorithm from the perspective of density, which is one of the best methods in clustering. This study proposes a density-based spatial clustering of applications with noise (DBSCAN) algorithm based on the selected high-density areas by automatic fuzzy-DBSCAN (AFD) which works with the initialization of two parameters. AFD, by using fuzzy and DBSCAN features, is modeled by the selection of high-density areas and generates two parameters for merging and separating automatically. The two generated parameters provide a state of sub-cluster rules in the Cartesian coordinate system for the dataset. The model overcomes the problems of clustering such as morphology, overlapping, and the number of clusters in a dataset simultaneously. In the experiments, all algorithms are performed on eight data sets with 30 times of running. Three of them are related to overlapping real datasets and the rest are morphologic and synthetic datasets. It is demonstrated that the AFD algorithm outperforms other recently developed clustering algorithms.

A method is proposed to deal with the uncertain multiple attribute group decision making problems, where 2-dimension uncertain linguistic variables (2DULVs) are used as the reliable way for the experts to express their fuzzy subjective evaluation information. Firstly, in order to measure the 2DULVs more accurately, a new method is proposed to compare two 2DULVs, called a score function, while a new function is defined to measure the distance between two 2DULVs. Secondly, two optimization models are established to determine the weight of experts and attributes based on the new distance formula and a weighted average operator is used to determine the comprehensive evaluation value of each alternative. Then, a score function is used to determine the ranking of the alternatives. Finally, the effectiveness of the proposed method is proved by an illustrated example.

In the process of the terminal guidance of a kinetic kill vehicle (KKV), it is very important to accurately estimate the line-of-sight (LOS) rate via the measurements of a target seeker onboard the KKV. The strong impact interference caused by the large lateral thrust produced by the thrusters on the KKV is a main factor that affects the measurements on the LOS angle. A method to estimate the impact interference and the LOS rate together via a Kalman filter is proposed to improve the estimation precision of the LOS rate. The observability of the system describing the missile-target relative motion model and the impact interference model is proved, and then a Kalman filter is designed. In the Kalman filter design, the continuous-discrete and two-stage filtering techniques are used because the system model is time-variant and high-order. Numerical simulation results show that by estimating the impact interference, the estimation precision of the LOS rate is increased, and so the miss distance of the KKV under the strong impact interference is reduced. The proposed continuous-discrete two-stage Kalman filter shows higher estimation precision and lower computational cost than the naive discrete augmented state Kalman filter.

To solve the problem of attitude synchronization control for spacecraft formation flying (SFF) suffering from external disturbances under a directed communication topology, a sliding mode disturbance observer (SMDO) based on the finite-time control strategy is developed to observe the time-varying external disturbance via estimating the upper bound of its first derivative. Meanwhile, the rotation matrix is employed to describe the attitude of SFF for the purpose of the avoidance of singularity and unwinding phenomenon. As for the attitude synchronization and the tracking control architecture, a sliding mode surface (SMS) is given such that the control objective can be achieved. The effectiveness and the validity of the proposed method are elaborated via theoretical analysis and numerical simulations.

This paper proposes a cooperative guidance law for attacking a ground target with the impact angle constraint based on the motion camouflage strategy in the line-of-sight (LOS) frame. A dynamic model with the impact angle constraint is established according to the relative motion between multiple missiles and the target. The process of cooperative guidance law design is divided into two stages. Firstly, based on the undirected graph theory, a new finite-time consensus protocol on the LOS direction is derived to guarantee relative distances reach consensus. And the value of acceleration command is positive, which is beneficial for engineering realization. Secondly, the acceleration command on the normal direction of the LOS is designed based on motion camouflage and finite-time convergence, which can ensure the missiles reach the target with the desired angle and satisfy the motion camouflage state. The finite-time stability analysis is proved by the Lyapunov theory. Numerical simulations for stationary and maneuver targets have demonstrated the effectiveness of the cooperative guidance law proposed.

This paper addresses the cooperative control problem of multiple unmanned aerial vehicles (multi-UAV) systems. First, a new distributed consensus algorithm for second-order nonlinear multi-agent systems (MAS) is formulated under the leader-following approach. The algorithm provides smooth input signals to the agents’ control channels, which avoids the chattering effect generated by the conventional sliding mode-based control protocols. Second, a new formation control scheme is developed by integrating smooth distributed consensus control protocols into the geometric pattern model to achieve three-dimensional formation tracking. The Lyapunov theory is used to prove the stability and convergence of both distributed consensus and formation controllers. The effectiveness of the proposed algorithms is demonstrated through simulation results.

In the equatorial region, deep amplitude fading in global positioning system (GPS) signals frequently occurs during the strong ionospheric scintillation, it can lead to the loss of lock in GPS carrier tracking loops, and result in increased positioning error and even navigation interruption. The relationships between amplitude scintillation indices and detrended carrier frequency are investigated, based on GPS L1 C/A signals during the last peak of the solar cycle at the low latitude site of S?o José dos Campos, Brazil (23.2S, 45.9W) from 2013 to 2015. Corresponding mathematic model of the probability distribution function is built for the first time to provide statistical analysis on the above relationships. The results show that the standard carrier frequencies reveal an almost linear relation with the amplitude scintillation indices. Moreover, the frequency widths of detrended frequency are proportional to levels of amplitude scintillation when the value of the peak probability is lower than the corresponding boundary. A conclusion can be drawn that different levels of amplitude scintillation will influence the fluctuation of the carrier frequency. The analysis will provide useful guidance to set the receiver’s bandwidth with respect to the different scintillation levels and design the advanced tracking algorithms to improve the robustness and precision of the GPS receiver.

An airship model is made-up of aerostatic, aerodynamic, dynamic, and propulsive forces and torques. Besides others, the computation of aerodynamic forces and torques is difficult. Usually, wind tunnel experimentation and potential flow theory are used for their calculations. However, the limitations of these methods pose difficulties in their accurate calculation. In this work, an online estimation scheme based on unscented Kalman filter (UKF) is proposed for their calculation. The proposed method introduces six auxiliary states for the complete aerodynamic model. UKF uses an extended model and provides an estimate of a complete state vector along with auxiliary states. The proposed method uses the minimum auxiliary state variables for the approximation of the complete aerodynamic model that makes it computationally less intensive. UKF estimation performance is evaluated by developing a nonlinear simulation environment for University of Engineering and Technology, Taxila (UETT) airship. Estimator performance is validated by performing the error analysis based on estimation error and 2- $ \sigma $ uncertainty bound. For the same problem, the extended Kalman filter (EKF) is also implemented and its results are compared with UKF. The simulation results show that UKF successfully estimates the forces and torques due to the aerodynamic model with small estimation error and the comparative analysis with EKF shows that UKF improves the estimation results and also it is more suitable for the under-consideration problem.