A detection of static or slowly moving targets in the inhomogeneous clutter of polarimetric radar is researched and a new detector without exploiting the training set is developed, which has a good performance against the inhomogeneous clutter. The decision rule ensures the constant false alarm rate (CFAR) with respect to the disturbance covariance matrix. Numerical results show the robustness of the proposed method against the inhomogeneous clutter.

With a small number of snapshots, performances of uniform circular array (UCA) root-MUSIC based methods for directionof-arrival (DOA) estimation suffer serious degradation, because the beamspace sample covariance matrix (BSCM) severely deviates from the true one. To solve this problem, an iterative technique is proposed. The proposed technique has two key points: transform array data into beamspace data and construct a BSCM; modify the BSCM repeatedly by removing the residual parts. Meanwhile, error and beamspace leakage associated with the BSCM are investigated. The proposed technique can significantly reduce the error and the beamspace leakage which in turn could improve the performance for DOA estimation. Simulation results are given to  illustrate the superiority of the proposed technique for uncorrelated and correlated signals.

The probability hypothesis density (PHD), a well-known scheme for multi-target tracking in clutters, can obtain peaks of possible tracks, and its cluster-indexed method is widely accepted in further track extraction. However, the track extraction may face high risk in the case that the targets are so approached that it is hardly to discern their measurements. The concept of the distance between track sets in two adjacent times is defined and a consistency measure metric between any two peaks in two adjacent times is further proposed based on “global information”, containing spatial information (topology feature) among tracks, along with the temporal information of each track. Then, a global track extraction method is proposed based on the consistency belief and four decision rules. Via the simulation comparison with the cluster-indexed method, the proposed method can avoid the track break and mistake association.

To reach a compromise between short acquisition time and excellent detection probability for the high-dynamic binary offset carrier (BOC) signal, an overall algorithm based on discrete polynomial-phase transform (DPT) and partial matching filter (PMF)-fast Fourier transform (FFT) algorithm is given. The high order items of received signals are removed by the method of DPT, and the PMF-FFT algorithm is redesigned for the BOC signal. The simulation experiments and theoretical analyses verify that the improved algorithm has attained improvement in acquisition performance.

Aiming at solving the azimuth signal aliasing problem in the Doppler domain, the azimuth time aliasing problem after range cell migration correction (RCMC) and the severe range and the azimuth coupling problem in both the phase and envelope for highly squinted terrain observation by progressive scans (TOPS) synthetic aperture radar (SAR), a novel full aperture imaging algorithm is presented. An unaliased two-dimensional (2-D) spectrum is first obtained by the azimuth preprocessing; the modified range migration algorithm (RMA) is then used to complete RCMC; and finally the azimuth signal is focused in the Doppler domain by spectral analysis (SPECAN) and deramping. Simulations and real data processing results validate the effectiveness of the proposed algorithm.

Due to the influence of the platform random motion and electromagnetic propagation in turbulent media, the synthetic aperture radar (SAR) high resolution imaging for the sea scenes where there are large amounts of water returns with some target (land) returns is very difficult. To solve this problem, a SAR imaging method based on the improved phase retrieval (PR) algorithm is proposed. First, a filter is added to the conventional PR algorithm which can reduce the influence of water returns on the reconstruction of the targets and improve the reconstruction result of the targets. Then, the corrupted phase of the Fourier transform of the intensity image in the iterative process, which can improve the stability of the iterative algorithm, is used to reduce the recovery errors, and a better recovery performance is achieved. Finally, several experiments are performed to demonstrate the advantages of the proposed method.

In most multi-function phased array radar applications, multiple missions, including airspace searching and target tracking, are usually performed simultaneously by the digital beam-forming technique and the time dividing method. This paper presents a novel method to classify pulses of different missions from an interleaved pulse sequence emitted by the same radar, which is significant in radar electronic reconnaissance and electronic support measure. Firstly, two hypotheses, i.e., pulse relativity within the same mission and pulse independence among different missions, are proposed by analyzing the antenna pattern and the beam scheduling method of the phased array radar. Based on the above two hypotheses, an optimal model for pulse classification is exploited with pulse amplitude series, where the absolute-value sum of second order difference is taken as the optimal kernel to measure sequence smooth continuity. Finally, several pieces of sequences under different numbers of missions and tracking data rates are simulated for algorithm verification. The simulation results show that the long data length and the high data rate will increase classification efficiency due to the validity of the two hypotheses in sufficient pulse amplitude sequence.

In order to realize the fact that operational tasks can be decomposed flexibly with new tasks random adding, role-based approaches for operational tasks model and the flexibility decomposition are proposed. Firstly, aiming at the shortcomings of previous task decomposition methods, operational role concepts are introduced to design a computing framework of task flexibility decomposition. Within the framework, task structure matrixes are constructed for tasks decomposition with the mapping relations of information interaction and action process among tasks. On basis of this, via giving operational decomposition granularity, a flexibility approach of operational task decomposition is put forward with three parts about task decomposition granularity adjustment, tasks module partition with different operational role patterns and task decomposition scheme evaluation. Finally, as an application case shown, it is validated that the proposed approaches for tasks modeling and decomposition are available. By comparing with previous decomposition approaches, the proposed approach, within three types of operational roles, has strong environmental adaptability and scheme diversity, which can restrain calculation complexity increment for realizing task flexible decomposition effectively.

In order to adapt to the changes in the states of moving targets and meet the requirements of quality of service in communication among unmanned aerial vehicles (UAVs), the UAVs formation needs to dynamically adjust tracking tasks and tracking paths, when they carry out a mission of tracking multiple moving targets. A multi-targets tracking algorithm based on task allocation consensus is proposed for UAVs to track multiple moving targets  under the distributed control architecture in the limited communication range. This algorithm creates a distributed dynamic task allocation model using intermittent asynchronous communication principle to realize the sharing of observation information gathered by each UAV with fewer communications. In addition, this algorithm also makes it possible that multi-UAVs can plan tracking paths for multiple moving targets. We implement the algorithm on the formation with three UAVs to track three moving targets through simulation. Simulation results show the effectiveness of the proposed algorithm.

Evaluating the node importance correctly is a crucial issue for complex networks research. If a network has multiple or ambiguous morphological features, the evaluation result of an individual index may be unilateral. Meanwhile, the results may be inconsistent if different indexes are adopted simultaneously. To solve the problem, an integrated approach is proposed based on relative entropy. In this approach, a system of multiple indexes is constructed firstly. Then each evaluation result of an individual index is handled into a discrete distribution. Finally an optimal integrated evaluation solution is obtained by linear programming. This approach has a well-formed theoretic basis and an easilycalculated procedure, which can be used in a variety of complex networks. Experimental results show that the proposed approach is more effective than other different methods proposed in some literatures.

A success probability orientated optimization model for resource allocation of the technological innovation multi-project system is studied. Based on the definition of the technological innovation multi-project system, the leveling optimization of cost and success probability is set as the objective of resource allocation. The cost function and the probability function of the optimization model are constructed. Then the objective function of the model is constructed and the solving process is explained. The model is applied to the resource allocation of an enterprise’s technological innovation multi-project system. The results show that the proposed model is more effective in rational resource allocation, and is more applicable in maximizing the utility of the technological innovation multi-project system.

An output-feedback based partial integrated guidance and control (IGC) law for missile interceptor is proposed with the smooth finite time convergence property. Firstly, a novel outputfeedback based smooth sliding mode control is proposed for a class of nonlinear systems in the presence of immeasurable state and disturbance. The finite time convergence property of this control scheme is proved based on the Lyapunov stability and homogeneous principle. Then, the proposed control method is applied to the design of partial IGC law. A kind of scaling methods is also developed to reach the rapid convergence of the system. An important dominance of this control scheme is the smooth characteristic, which may result in chattering free convergence. The simulation results indicate the high precision and smooth elevator deflection of the proposed output feedback base partial IGC law.

Nonlinear dynamic characteristics of a fixed-trim reentry vehicle controlled by an internal moving-mass actuator are analyzed. A traditional dynamic model develops into a five-dimensional nonlinear model using classic Euler angles and their derivatives as state variables. Based on the nonlinear motion equations, by setting the offset distance of the moving-mass as a variation parameter, the curves of the system’s equilibrium points are presented by numerical methods. Then the distributions and approximate analytical solutions of the equilibrium points are obtained by simplifying the model under the condition of small intrinsic angles. The results show that the numbers and values of the equilibrium points are closely connected with the location of the moving-mass. Furthermore, the stabilities of equilibrium points are examined by the Lyapunov’s first method and three groups of stable equilibrium points are obtained. Since only one group of the stable equilibrium points is desired, the angular motion of the system may be unstable or stay in an undesired lock-in state when the offset distance of the moving-mass or the attitude disturbance of the vehicle is too large.

A distributed robust method is developed for cooperative tracking control of unmanned aerial vehicles under unknown wind disturbance and model uncertainty. The communication network among vehicles is a directed graph with switching topology. Each vehicle can only share its states with its neighbors. Dynamics of the vehicles are nonlinear and affected by the wind disturbance and model uncertainty. Feedback linearization is adopted to transform the dynamics of vehicles into linear systems. To account for the wind disturbance and model uncertainty, a robust controlleris designed for each vehicle such that all vehicles ultimately synchronize to the virtual leader in the  three-dimensional path. It is theoretically shown that the position states of the vehicles will converge to that of the virtual leader if the communication network has a directed spanning tree rooted at the virtual leader. Furthermore, the robust controller is extended to address the formation control problem. Simulation examples are also given to illustrate the effectiveness of the proposed method.

A consensus problem of multi-agent system subject to the communication delay with fixed and switching topologies as well as optimal obstacle avoidance is investigated. An innovative non-quadratic obstacle avoidance cost function is designed from an inverse optimal control perspective and a novel dynamic potential field whose vectors are rotating around the obstacle is introduced to avoid obstacles. The asymptotic stability condition of dynamical networks based on the Lyapunov-Krasovskii function is obtained via a linear matrix inequality (LMI) formulation. Finally, numerical examples are presented to show the effectiveness of the proposed theoretical results.

In the field of fault diagnosis, the state equation of nonlinear system, including the actuator and the component, has been established. When the faults in the system appear, it is difficult to observe the fault isolation between the actuator and the component. In order to diagnose the component fault in the nonlinear systems, a novel strategy is proposed. The nonlinear state equation with only the component system is built on mathematical equations. The nonlinearity of the component equation is expanded and estimated with Taylor series. If the actuator is perfect, the anomaly of the state equations reflects the component fault. The fault feature index is defined to detect the component fault and the initial fault. The numerical examples of the component faults are simulated for multiple-input multiple-output (MIMO) nonlinear systems. The results show that the component faults, as well as the incipient faults, can be detected. Furthermore, the effectiveness of the proposed strategy is verified. This method can also provide a foundation for the component fault reconfiguration control.

Appropriate human-machine function allocation benefits the complement between pilots and automation, and makes the whole system more effective and reliable. Thus, the humanmachine function allocation method for aircraft cockpit based on interval 2-tuple linguistic information is proposed. The uncertain expanded weighted arithmetic averaging (UEWAA) operator and uncertain linguistic hybrid aggregation (ULHA) operator are extended into the interval 2-tuple linguistic environment based on uncertain linguistic multiple attribute decision making (ULMADM). And some new aggregation operators are developed, including the interval 2-tuple weighted average aggregation (IT-WAA) operator and the interval 2-tuple weighted hybrid aggregation (IT-WHA) operator. Uniformization of the multiple-granularity linguistic evaluation information is introduced to solve the problem of ULMADM not being able to deal with the multiple-granularity linguistic information from different decision-makers. Then the detailed steps of the proposed method are described. Finally, an illustrative example of the ground proximity warning system (GPWS) is provided to demonstrate the practicality, feasibility and superiority based on the comparison between the proposed method and ULMADM.

Super-resolution (SR) techniques, which are based on
single or multi-frame low-resolution (LR) images, have been extensively
investigated in the last two decades. Mixed-resolution multiview video format plays an important role in three-dimensional television (3DTV) coding scheme. Previous work considers multiview or multi-camera images and videos at the same resolution, which performs well under the planar model without or with little projection error among the videos captured by different cameras. In recent years, several researchers have discussed the SR problem in mixed-resolution multi-view video format, where the superresolved image is created using the up-sampled version of the mLR image and the high frequency components extracted from the warped image in the adjacent high-resolution (HR) views. Unfortunately, the output HR images suffer from artifacts caused by depth error. To obtain the detailed texture and edge information from the HR image as much as possible, while preserving the structure of the LR image, a novel SR reconstruction algorithm is proposed. The algorithm is composed of three components: the structure term, the detail information term, and the regularization term. The first term preserves the structure similarity of the LR image; the second term extracts detailed information from the adjacent HR image; and the last term ensures the uniqueness of the solution. Experimental results show the effectiveness and robustness of the proposed algorithm, which achieves high performance both subjectively and objectively.

Consider the efficiency of p-norm multiple kernel learning (MKL), which is extended to a semi-supervised learning (SSL) scenario by applying the manifold regularization technique. A manifold regularized p-norm multiple kernels model is constructed and applied to a semi-supervised classification task. Solutions are proposed for the case of p = 1, p > 1 and p = ∞, with an analysis of theorems and their proofs. In addition, experiments are conducted on several datasets using state-of-the-art methods to verify the efficiency of the proposed manifold regularized p-norm multiple kernels model in semi-supervised classification.

The Bayesian networks (BNs) provide a robust probabilistic method of reasoning under uncertainty and have been successfully applied to a variety of real-world tasks. Aiming to explore the capabilities of the BN formalism in reliability analysis of monotone coherent multi-state systems, the BNs are compared with a popular tool for reliability analysis of monotone coherent multi-state systems, namely the multi-state fault trees (MFTs). It is shown that any MFT can be directly mapped into BN and the basic inference techniques on the latter may be used to obtain classical parameters computed from the former (i.e. probability distribution of top variable, minimal upper vectors and maximum lower vectors for any performance level, importance measures of components). Furthermore, some additional information can be obtained by using BN, both at the modeling and analysis level. At the modeling level, several restrictive assumptions implicit in the MFT methodology can be removed and various kinds of dependencies among components can be accommodated. At the analysis level, a general diagnostic analysis can be performed. The comparison of these methods is illustrated by an example of the water supply system.