This paper combines compressed sensing (CS) imaging theory and range migration algorithm (RMA), and then proposes a near-field three-dimensional (3-D) imaging approach for joint high-resolution imaging and phase error correction. Firstly, a sparse measurement matrix construction method based on a logistic sequence is proposed, which conducts nonlinear transformation for the determined logistic sequence, making it obey uniform distribution, then conducts sign function mapping, and generates the pseudorandom sequence with Bernoulli distribution, thus leading to good signal recovery under down-sampling and easy availability for engineering realization. Secondly, in combination with the RMA imaging approach, the dictionary with all scene information and phase error correction is constructed for CS signal recovery and error correction. Finally, the non-quadratic solution model jointing imaging and phase error correction based on regularization is built, and it is solved by two steps—the separable surrogate functionals (SSF) iterative shrinkage algorithm is adopted to realize target scattering estimate; the iteration mode is adopted for the correction of the dictionary model, so as to achieve the goal of error correction and highly-focused imaging. The proposed approach proves to be effective through numerical simulation and real measurement in anechoic chamber. The results show that, the proposed approach can realize high-resolution imaging in the case of less data; the designed measurement matrix has better non-coherence and easy availability for engineering realization. The proposed approach can effectively correct the phase error, and achieve highly-focused target image.

Nyquist folding receiver (NYFR) is a typical wideband analog-to-information architecture. Focusing on the noncooperative receiving, the pulse radar signal intercepted by the NYFR in time domain is analyzed. The NYFR outputs under different input conditions are investigated based on the extended Fourier transform (EFT) and the sampling theorem. Combining with the characteristic of the NYFR output in time domain, a new time of arrival (TOA) estimation method based on the energy envelope and the wavelet transform is proposed. The proposed estimation method can be adapted for the non-cooperative situation. It has no requirement for prior information to determine the threshold and is not necessary to transform the signal into baseband. Simulation results prove the correctness of the NYFR output expressions and show the efficacy of the proposed estimation method.

A signal waveform recovery method based on the coprime array is investigated to extract the waveform of the desired signal from spatial interferences in narrowband scenarios. The direction of arrivals (DOAs) of the desired signal and interference signals are estimated with the compressive sensing approach based on angle grids, and the signal power together with the noise power are estimated. Thereafter, a modified steepest descent (SD) method is derived to recover the waveform of the desired signal and interferences utilizing the estimated power and directions. The recovered waveform of the desired signal is the output of the proposed method. The situation in which the signals are not on the predefined angle grids is also considered. The DOAs estimated via the predefined angle grids are corrected based on the maximum likelihood (ML) angle estimation. Compared to the existing beamforming methods on co-prime array, the proposed method can obtain the waveform of the desired signal. Simulation results demonstrate that the proposed method can achieve good performance in signal waveform recovery and output signal to noise ratio.

Blind recognition of convolutional codes is not only essential for cognitive radio, but also for non-cooperative context. This paper is dedicated to the blind identification of rate k/n convolutional encoders in a noisy context based on Walsh-Hadamard transformation and block matrix (WHT-BM). The proposed algorithm constructs a system of noisy linear equations and utilizes all its coefficients to recover parity check matrix. It is able to make use of fault-tolerant feature of WHT, thus providing more accurate results and achieving better error performance in high raw bit error rate (BER) regions. Moreover, it is more computationally efficient with the use of the block matrix (BM) method.

Waveguide invariant is widely used in underwater target passive ranging. In shallow water with uniform sound speed profile, the value of waveguide invariant is approximately a constant, while in shallow water with thermocline, it varies in a wide range. The waveguide invariant distributions and striations in these two conditions are analyzed respectively. On the basis of wavenumber difference between reflected modes and refracted modes, a wavenumber-frequency domain filtering technique is proposed to separate the two groups of modes. The required relationship between array element space, total array length and target azimuth for effective application is discussed. Finally, the simulation results indicate that in shallow water with a thermocline, refracted modes can be effectively filtered out using the wavenumber-frequency domain filtering technique and the target’s range is estimated accurately.

Based on the operating principle and the electric property design of the passive hydrogen maser, the technology and test results of its space adaptability are carried out under the special launch conditions and space environment. The various perturbations affecting the output frequency of such a standard used for the navigation satellite system are specified, such as magnetic field change, vibration, thermal vacuum and radiation. Through the adaptability technology in the aspects above, the security and reliability of the space passive hydrogen maser sufficiently fulfill the requirements of space operation. At present, the space passive hydrogen maser is working normally on board, indicating that the space adaptability satisfies the design requirement.

The problem of angle estimation for bistatic multipleinput multiple-output radar in the present of unknown mutual coupling (MC) is investigated, and a three-way compressive sensing (TWCS) estimation algorithm is developed. To exploit the inherent multi-dimensional structure of received data, a trilinear tensor model is firstly formulated. Then the de-coupling operation is followed. Thereafter, the high-order singular value decomposition is applied to compress the high dimensional tensor to a much smaller one. The estimation of the compressed direction matrices are linked to the compressed trilinear model, and finally two overcomplete dictionaries are constructed for angle estimation. Also, Cramer-Rao bounds for angle and MC estimation are derived. The proposed TWCS algorithm is effective from the perspective of estimation accuracy as well as the computational complexity, and it can achieve automatically paired angle estimation. Simulation results show that the proposed method has much better estimation accuracy than the existing algorithms in the low signal-to-noise ratio scenario, and its estimation performance is very close to the parallel factor analysis (PARAFAC) algorithm at the high SNR regions.

The inverse synthetic aperture radar (ISAR) imaging can be converted into a sparse reconstruction problem and solved by the l1-norm minimization algorithm. The basis matrix in sparse ISAR imaging is usually characterized by the unknown rotation rate of a moving target, thus the rotation rate and the sparse signal should be jointly estimated. Especially due to the imperfect coarse motion compensation, we consider the phase error correction problem in the context of the sparse signal reconstruction. To address this issue, we propose an iterative reweighted method, which jointly estimates the rotation rate, corrects the phase error and reconstructs a high resolution ISAR image. The proposed method gives a gradual and interweaved iterative process to refine the unknown parameters to achieve the best sparse representation for the ISAR signals. Particularly, in ISAR image reconstruction, the l1-norm minimization algorithm is sensitive to user parameters.
Setting these user parameters are not trivial and the reconstruction performance depends significantly on their choices. Then, we consider an expansion-compression variance-component (ExCoV) based method, which is automatic and demands no prior knowledge about signal-sparsity or measurement-noise levels. Both numerical and electromagnetic data experiments are implemented to show the effectiveness of the proposed method. It is shown that the proposed method can estimate the rotation rate and correct the phase errors simultaneously, and its superior performance is proved in terms of high resolution ISAR image.

A new method for synthetic aperture radar (SAR) target recognition is proposed. This method is accomplished via the combination of active contour without edges, Hu invariantmoments and support vector machine (SVM) classifier. Image segmentation is performed by using active contour without edges. Then seven Hu moments are extracted and normalized as feature vectors. Finally, the SVM classifier is employed for data training and testing by means of MSTAR SAR images. To verify the performance of the proposed method, the traditional active contour (snakes) is used for comparison. The simulation results confirm the feasibility and accuracy of the proposed method in SAR target recognition.

Based on a simple step-stress accelerated competing risks model from generalized exponential distribution, the statistical prediction problem of unobserved failure times under generalized progressive hybrid censoring is investigated. Using the cumulative exposure model, maximum likelihood predictors, conditional median predictors, approximate prediction intervals and conditional prediction intervals of model parameters and unobserved competing failure times are obtained. To support and illustrate the prediction methods, an available dataset is analyzed and a simulation study is carried out. The numerical results of predictors of model parameters and competing failure times show that the prediction methods have a good performance.

In the field of weapon system of systems (WSOS) simulation, various indicators are widely used to describe the capability of WSOS, but it is always difficult to describe the comprehensive capability of WSOS quickly and intuitively by visualization of multi-dimensional indicators. A method of machine learning and visualization is proposed, which can display and analyze the capabilities of different WSOS in a two-dimensional plane. The analysis and comparison of the comprehensive capability of different components of WSOS is realized by the method, which consists of six parts: multiple simulations, key indicators mining, three spatial distance calculation, fusion project calculation, calculation of individual capability density, and calculation of multiple capability ranges overlay. Binding a simulation experiment, the collaborative analysis of six indicators and 100 possible kinds of red WSOS are achieved. The experimental results show that this method can effectively improve the quality and speed of capabilities analysis, reveal a large number of potential information, and provide a visual support for the qualitative and quantitative analysis model.

Intuitionistic fuzzy preference relations are powerful techniques used to express uncertain preference information. However, simultaneously providing the exact priority and nonpriority intensities could be difficult in real applications. A credible interval intuitionistic fuzzy number (CIIFN) is introduced and a credible interval intuitionistic fuzzy reciprocal preference relation (CIIFRPR) is developed to solve this issue. Unlike intuitionistic fuzzy preference relations, the new preference relations use the CIIFNs to express the preference information such that the decision makers simply provide the priority intensity with intervalvalued numbers and calculate the non-preference intensity with the transformed method, which avoids a complex evaluation of non-priority information. Furthermore, some basic operations and comparison laws are investigated, based on which three credible interval intuitionistic fuzzy aggregation operators are proposed. Two models are presented to manage the group decision-making. Finally, a practical case is used to demonstrate the feasibility and reasonability of the proposed preference relations and aggregation operators.

Combined with the fact that information asymmetry between two belligerent parties is very common in information-based warfare, the bidirectional influence of asymmetric information on both sides is analyzed. A new damage coefficient is formed based on the analysis, and a generalized Lanchester warfare model characterized by information asymmetry effect is built. According to the staggered transformation of information asymmetric degree resulting from the changing tactic of both sides by turns, a method for approximate solution of the model is presented by translating the information asymmetric degree as a ladder function, and both examples of the solving model and further discussion about the solution are given. The results indicate that the model can serve as a reference to analyze and forecast the course of war quantitatively.

The paper presents the longitudinal control for the carrier-based unmanned aerial vehicle (UAV) system with unmeasured states, actuator faults, control input constraints, and external disturbances. By combining output state observer, adaptive fuzzy control, and constraint backstepping technology, a robust fault tolerant control approach is proposed. An output state observer with fuzzy logic systems is developed to estimate unmeasured states, and command filters rather than differentiations of virtual control law are used to solve the computational complexity problem in traditional backstepping. Additionally, a robust term is introduced to offset the fuzzy adaptive estimation error and external disturbance, and an appropriate fault controller structure with matching conditions obtained from fault compensation is proposed. Based on the Lyapunov theory, the designed control program is illustrated to guarantee that all the closed-loop signals of the given system are bounded, and the output errors converge to a small neighborhood of zero. A carrier-based UAV nonlinear longitudinal model is employed to testify the feasibility and validity of the control scheme. The simulation results show that all the controllers can perform at a satisfactory level of reference tracking despite the existence of unknown aerodynamic parameters and actuator faults.

This paper investigates the problem of designing a fast convergent sliding mode flight controller of a transport aircraft for heavyweight airdrop operations in the presence of bounded uncertainties without the prior knowledge of the bounds. On the basis of feedback linearization of the aircraft-cargo motion system, a novel integral sliding mode flight control law with gains adaptation is proposed. It contains a nominal control law used to achieve finite-time stabilization performance and a compensated control law used to reject the uncertainties. The switching gains of the compensated control law are tuned using adaptation algorithms, and the knowledge of the bounds of the uncertainties is not required to be known in advance. Meanwhile, the severe chattering of the sliding mode control that caused by high switching gains is effectively reduced. The controller and its performance are evaluated on a transport aircraft performing a maximum load airdrop task in a number of simulation scenarios.

A uniform stability analysis is developed for a type of neutral delays differential equations which depend on more general nonlinear integral inequalities. Many original investigations and results are obtained. Firstly, generations of two integral nonlinear inequalities are presented, which are very effective in dealing with the complicated integro-differential inequalities whose variable exponents are greater than zero. Compared with existed integral inequalities, those proposed here can be applied to more complicated differential equations, such as time-varying delay neutral differential equations. Secondly, the notions of (ω,Ω) uniform stable and (ω,Ω) uniform asymptotically stable, especially (c₁, c₁) uniform stable and (c₁, c₁) uniform asymptotically stable, are presented. Sufficient conditions on about (c₁, c₁) uniform stable and (c₁, c₁) uniform asymptotically stable of time-varying delay neutral differential equations are established by the proposed integral inequalities. Finally, a complex numerical example is presented to illustrate the main results effectively. The above work allows to provide further applications on the proposed stability analysis and control system design for different nonlinear systems.

This paper presents a method of computing a 2.1D sketch (i.e., layered image representation) from a single image with mixed Markov random field (MRF) under the Bayesian framework. Our model consists of three layers: the input image layer, the graphical representation layer of the computed 2D atomic regions and 3-degree junctions (such as T or arrow junctions), and the 2.1D sketch layer. There are two types of vertices in the graphical representation of the 2D entities: (i) regions, which act as the vertices found in traditional MRF, and (ii) address variables assigned to the terminators decomposed from the 3-degree junctions, which are a new type of vertices for the mixed MRF. We formulate the inference problem as computing the 2.1D sketch from the 2D graphical representation under the Bayesian framework, which consists of two components: (i) region layering/coloring based on the Swendsen-Wang cuts algorithm, which infers partial occluding order of regions, and (ii) address variable assignments based on Gibbs sampling, which completes the open bonds of the terminators of the 3-degree junctions. The proposed method is tested on the D-Order dataset, the Berkeley segmentation dataset and the Stanford 3D dataset. The experimental results show the efficiency
and robustness of our approach.

With the wide application of Web 2.0 and social software, there are more and more tag-related studies and applications. Because of the randomness and the personalization in users’ tagging, tag research continues to encounter data space and semantics obstacles. With the min-max similarity (MMS) to establish the initial centroids, the traditional K-means clustering algorithm is firstly improved to the MMSK-means clustering algorithm, the superiority of which has been tested; based on MMSK-means and combined with latent semantic analysis (LSA), here secondly emerges a new tag clustering algorithm, LMMSK. Finally, three algorithms for tag clustering, MMSK-means, tag clustering based on LSA (LSA-based algorithm) and LMMSK, have been run on Matlab, using a real tag-resource dataset obtained from the Delicious Social Bookmarking System from 2004 to 2009. LMMSK’s clustering result turns out to be the most effective and the most accurate. Thus, a better tag-clustering algorithm is found for greater application of social tags in personalized search, topic identification or knowledge community discovery. In addition, for a better comparison of the clustering results, the clustering corresponding results matrix (CCR matrix) is proposed, which is promisingly expected to be an effective tool to capture the evolutions of the social tagging system.

The ever-increasing complexity of industry facilities has made the reliability analysis and assessment an imperative yet tough work. Motivated by practical engineering requirement, this paper develops a Bayesian-based information extraction and aggregation (BIEA) approach for system level reliability estimation of a complex system. It takes both subjective judgments and objective field outputs into consideration. Novel features of this approach is a unique information content based aggregation process, which allows a flexible application of this framework in separated modules on account for purpose. The coherency of which is guaranteed by the objective information content calculation. This work goes beyond the alternatives that deal with solely attributed data under ideal information circumstance, and investigates a more generic tool for real engineering application. Limitations embedded in traditional statistical modeling methods have been eliminated in a nature manner by information transition and integration. In addition, a double axis driving mechanism (DADM) for erecting the antenna of a satellite is demonstrated as case study for benefit illustration and effectiveness verification.

With the development of multi-autonomous underwater vehicle (AUV) cooperative systems, the evaluation of their reliability is becoming more and more important. Since AUVs are always in motion, the reliability of the system is not stationary, but it varies with time. This paper studies the time-varying reliability evaluation indexes for multi-AUV cooperative systems. Aimed at elucidating the characteristics of the system, by considering the motion of the AUVs, two time dependent reliability evaluation indexes, natural connectivity and all-terminal reliability of the basic reliability indexes, are determined with the graph theory. Then the timevarying
reliability of multi-AUV cooperative systems is evaluated combining with an actual example. This paper provides a method to evaluate the time-varying reliability of multi-AUV cooperative systems.

A hierarchical clustering model for a wireless sensor network (WSN) which takes into account the node fault and the balance of energy consumption is built. With the sensing and transmission as the main tasks of the WSN, its working mode is converted to a modular k-out-of-n system whose each modular is also a k-out-of-n structure. By using the theory of signature and its extending, the reliability of the WSN is computed. Finally, the reliability of WSN changing with the number of nodes, coverage requirements and node lifetimes is given by the numerical analysis. The proposed reliability analysis model is suitable for a large-scale WSN.