For the frequency difference of arrival (FDOA) estimation in passive location, this paper transforms the frequency difference estimation into the radial velocity difference estimation, which is difficult to achieve a high accuracy due to the mismatch between the sampling period and the pulse repetition interval. The proposed algorithm firstly estimates the point-in-time that each pulse arrives at two receivers accurately. Secondly two time of arrival (TOA) sequences are subtracted. And finally the radial velocity difference of a target relative to two stations with the least square method is estimated. This algorithm only needs accurate estimation of the time delay between pulses and is not influenced by parameters such as frequency and modulation mode. It avoids transmitting a large amount of data between two stations in real time. Simulation results corroborate that the performance is better than the arithmetic average of the Cramer-Rao lower bound (CRLB) for monopulse under suitable conditions.

The acceleration of a high maneuvering target in signal processing is helpful to enhance the performance of the tracker and facilitate the classification of targets. At present, most of the research on acceleration estimation is carried out in cases of a single target with time-frequency analysis methods such as fractional Fourier transform (FRFT), Hough-ambiguity transform (HAT), and Wigner-Ville distribution (WVD), which need to satisfy enough time duration and sampling theorem. Only one reference proposed a method of acceleration estimation for multiple targets based on modified polynomial phase transform (MPPT) in the linear frequency modulation (LFM) continuous-wave (CW) radar. The method of acceleration estimation for multiple targets in the pulse Doppler (PD) radar has not been reported so far. Compressive sensing (CS) has the advantage of sampling at a low rate and short duration without sacrificing estimation performance. Therefore, this paper proposes a new method of acceleration estimation for multiple maneuvering targets with the unknown number based on CS with pulse Doppler signals. Simulation results validate the effectiveness of the proposed method under several conditions with different duration, measurement numbers, signal to noise ratios (SNR), and regularization parameters, respectively. Simulation results also show that the performance of the proposed method is superior to that of FRFT and HAT in the condition of multiple targets.

The uncertainty of observers’ positions can lead to significantly degrading in source localization accuracy. This paper proposes a method of using self-location for calibrating the positions of observer stations in source localization to reduce the errors of the observer positions and improve the accuracy of the source localization. The relative distance measurements of the two coordinative observers are used for the linear minimum mean square error (LMMSE) estimator. The results of computer simulations prove the feasibility and effectiveness of the proposed method. With the general estimation errors of observers’ positions, the MSE of the source localization with self-location calibration, which is significantly lower than that without self-location calibration, is approximating to the Cramer-Rao lower bound (CRLB).

In order to reduce power consumption of sensor nodes and extend network survival time in the wireless sensor network (WSN), sensor nodes are scheduled in an active or dormant mode. A chain-type WSN is fundamentally different from other types of WSNs, in which the sensor nodes are deployed along elongated geographic areas and form a chain-type network topology structure. This paper investigates the node scheduling problem in the chain-type WSN. Firstly, a node dormant scheduling mode is analyzed theoretically from geographic coverage, and then three neighboring nodes scheduling criteria are proposed. Secondly, a hybrid coverage scheduling algorithm and dead areas are presented. Finally, node scheduling in mine tunnel WSN with uniform deployment (UD), non-uniform deployment (NUD) and optimal distribution point spacing (ODS) is simulated. The results show that the node scheduling with UD and NUD, especially NUD, can effectively extend the network survival time. Therefore, a strategy of adding a few mobile nodes which activate the network in dead areas is proposed, which can further extend the network survival time by balancing the energy consumption of nodes.

The exact radar cross-section (RCS) measurement is difficult when the scattering of targets is low. Full polarimetric calibration is one technique that offers the potential for improving the accuracy of RCS measurements. There are numerous polarimetric calibration algorithms. Some complex expressions in these algorithms cannot be easily used in an engineering practice. A radar polarimetric coefficients matrix (RPCM) with a simpler expression is presented for the monostatic radar polarization scattering matrix (PSM) measurement. Using a rhombic dihedral corner reflector and a metallic sphere, the RPCM can be obtained by solving a set of equations, which can be used to find the true PSM for any target. An example for the PSM of a metallic dish shows that the proposed method obviously improves the accuracy of crosspolarized RCS measurements.

This paper studies the estimation performance of the coherent processing parameter (CPP), including time delay differences and phase synchronization errors among different apertures of the distributed coherent aperture radar (DCAR). Firstly, three architectures of signal processing in the DCAR are introduced. Secondly, the closed-form Cramer-Rao bound (CRB) of the CPP estimation is derived and compared. Then, the closed-form CRB is verified by numerical simulations. Finally, when the next generation radar works in a fully coherent mode, the closed-form signal-to-noise ratio (SNR) gain of the three architectures is presented.

This paper presents a joint high order statistics (HOS) and signal-to-noise ratio (SNR) algorithm for the recognition of multiple-input multiple-output (MIMO) radar signal without a priori knowledge of the signal parameters. This method is capable of recognizing the MIMO radar signal as well as discriminating it from single-carrier signal adopted by conventional radar. Meanwhile, the sub-carrier number of the none-coding MIMO radar signal is estimated. Extensive simulations are carried out in different operating conditions. Simulation results prove the feasibility and indicate that the recognition probability could reach over 90% when the value of SNR is above 0 dB.

Optimization of architecture design has recently drawn research interest. System deployment optimization (SDO) refers to the process of optimizing systems that are being deployed to activities. This paper first formulates a mathematical model to theorize and operationalize the SDO problem and then identifies optimal solutions to solve the SDO problem. In the solutions, the success rate of the combat task is maximized, whereas the execution time of the task and the cost of changes in the system structure are minimized. The presented optimized algorithm generates an optimal solution without the need to check the entire search space. A novel method is finally proposed based on the combination of heuristic method and genetic algorithm (HGA), as well as the combination of heuristic method and particle swarm optimization (HPSO). Experiment results show that the HPSO method generates solutions faster than particle swarm optimization (PSO) and genetic algorithm (GA) in terms of execution time and performs more efficiently than the heuristic method in terms of determining the best solution.

In order to solve the problem that the ripple-effect analysis for the operational architecture of air defense systems (OAADS) is hardly described in quantity with previous modeling approaches, a supernetwork modeling approach for the OAADS is put forward by extending granular computing. Based on that perational
units and links are equal to different information granularities, the supernetwork framework of the OAADS is constructed as a “fournetwork within two-layer” structure by forming dynamic operating coalitions, and measuring indexes of the ripple-effect analysis for the OAADS are given combining with Laplace spectral radius. In this framework, via analyzing multidimensional attributes which inherit relations between operational units in different granular scales, an extended granular computing is put forward integrating with a topological structure. Then the operation process within the supernetwork framework, including transformation relations between two layers in the vertical view and mapping relations among functional networks in the horizontal view, is studied in quantity. As the application case shows, comparing with previous modeling approaches, the supernetwork model can validate and analyze the operation mechanism in the air defense architecture, and the ripple-effect analysis can be used to confirm the key operational unit with micro and macro viewpoints.

A proper weapon system is very important for a national defense system. Generally, it means selecting the optimal weapon system among many alternatives, which is a multipleattribute decision making (MADM) problem. This paper proposes a new mathematical model based on the response surface method (RSM) and the grey relational analysis (GRA). RSM is used to obtain the experimental points and analyze the factors that have a significant impact on the selection results. GRA is used to analyze the trend relationship between alternatives and reference series. And then an RSM model is obtained, which can be used to calculate all alternatives and obtain ranking results. A real world application is introduced to illustrate the utilization of the model for the weapon selection problem. The results show that this model can be used to help decision-makers to make a quick comparison of alternatives and select a proper weapon system from multiple alternatives, which is an effective and adaptable method for solving the weapon system selection problem.

The missile autopilot for an interceptor with tail fins and pulse thrusters is designed via the θ–D approach. The nonlinear dynamic model of the pitch and yaw motion of the missile is transformed into a linear-like structure with state-dependent coefficient (SDC) matrices. Based on the linear-like structure, a θ–D feedback controller is designed to steer the missile to track reference acceleration commands. A sufficient condition that ensures the asymptotic stability of the tracking system is given based on Lyapunov’s theorem. Numerical results show that the proposed autopilot achieves good tracking performance and the closed-loop tracking system is asymptotically stable.

Transfer alignment is an effective alignment method for the strapdown inertial navigation system (SINS) of airborne weapon systems. The traditional transfer alignment methods for large misalignment angles alignment use nonlinear transfer alignment models and incorporate nonlinear filtering. A rapid transfer alignment method with linear models and linear filtering for arbitrary misalignment angles is presented. Through the attitude quaternion decomposition, the purpose of transfer alignment is converted to estimate a constant quaternion. Employing special manipulations on measurement equation, velocity and attitude linear measurement equations are derived. Then the linear transfer alignment model for arbitrary misalignment angles is built. An adaptive Kalman filter is developed to handle modeling errors of the measurement noise statistics. Simulation results show feasibility and effectiveness of the proposed method, which provides an alternative rapid transfer alignment method for airborne weapons.

When a pico satellite is under normal operational conditions, whether it is extended or unscented, a conventional Kalman filter gives sufficiently good estimation results. However, if the measurements are not reliable because of any kind of malfunctions in the estimation system, the Kalman filter gives inaccurate results and diverges by time. This study compares two different robust Kalman filtering algorithms, robust extended Kalman filter (REKF) and robust unscented Kalman filter (RUKF), for the case of measurement malfunctions. In both filters, by the use of defined variables named as the measurement noise scale factor, the faulty measurements are taken into the consideration with a small weight, and the estimations are corrected without affecting the characteristic of the accurate ones. The proposed robust Kalman filters are applied for the attitude estimation process of a pico satellite, and the results are compared.

An image segmentation algorithm of the restrained fuzzy Kohonen clustering network (RFKCN) based on highdimension fuzzy character is proposed. The algorithm includes two steps. The first step is the fuzzification of pixels in which two redundant images are built by fuzzy mean value and fuzzy median value. The second step is to construct a three-dimensional (3-D) feature vector of redundant images and their original images and cluster the feature vector through RFKCN, to realize image segmentation. The proposed algorithm fully takes into account not only gray distribution information of pixels, but also relevant information and fuzzy information among neighboring pixels in constructing 3-D character space. Based on the combination of competitiveness, redundancy and complementary of the information, the proposed algorithm improves the accuracy of clustering. Theoretical analyses and experimental results demonstrate that the proposed algorithm has a good segmentation performance.

The active contour model based on local image fitting (LIF) energy is an effective method to deal with intensity inhomogeneities, but it always conflicts with the local minimum problem because LIF has a nonconvex energy function form. At the same time, the parameters of LIF are hard to be chosen for better performance. A global minimization of the adaptive LIF energy model is proposed. The regularized length term which constrains the zero level set is introduced to improve the accuracy of the boundaries, and a global minimization of the active contour model is presented. In addition, based on the statistical information of the intensity histogram, the standard deviation σ with respect to the truncated Gaussian window is automatically computed according to images. Consequently, the proposed method improves the performance and adaptivity to deal with the intensity inhomogeneities. Experimental results for synthetic and real images show desirable performance and efficiency of the proposed method.

This paper presents a new method for image separation through employing a combined dictionary consisting of wavelets and complex shearlets. Because the combined dictionary sparsely represents points and curvilinear singularities respectively, the image can be decomposed into pointlike and curvelike parts as accurate as possible. The proposed method based on the geometric separation theory introduced by Donoho in 2005 shows that accurate geometric separation of the morphologically distinct features of points and curves can be achieved by l1 minimization. The experimental results show that the proposed method can not only be effective but also greatly reduce the computing time.

A fundamental premise of an accelerated testing is that the failure mechanism under elevated and normal stress levels should remain the same. Thus, verification of the consistency of failure mechanisms is essential during an accelerated testing. A new consistency analysis method based on the gray theory is proposed for complex products. First of all, existing consistency analysis methods are reviewed with a focus on the comparison of the differences among them. Then, the proposed consistency analysis method is introduced. Two effective gray prediction models, gray dynamic model and new information and equal dimensional (NIED) model, are adapted in the proposed method. The process to determine the dimension of NIED model is also discussed, and a decision rule is expanded. Based on that, the procedure of applying the new consistent analysis method is developed. Finally, a case study of the consistency analysis of a reliability enhancement testing is conducted to demonstrate and validate the proposed method.

The problem of stochastically allocating redundant components to increase the system lifetime is an important topic of reliability. An optimal redundancy allocation is proposed, which maximizes the expected lifetime of a reliability system with subsystems consisting of components in parallel. The constraints are minimizing the total resources and the sizes of subsystems. In this system, each switching is independent with each other and works with probability p. Two optimization problems are studied by an incremental algorithm and dynamic programming technique respectively. The incremental algorithm proposed could obtain an approximate optimal solution, and the dynamic programming method could generate the optimal solution.

This paper proposes a simple constant-stress accelerated life test (ALT) model from Burr type XII distribution when the data are Type-I progressively hybrid censored. The maximum likelihood estimation (MLE) of the parameters is obtained through the numerical method for solving the likelihood equations. Approximate confidence interval (CI), based on normal approximation to the asymptotic distribution of MLE and percentile bootstrap CI is derived. Finally, a numerical example is introduced and then a Monte Carlo simulation study is carried out to illustrate the proposed method.

As the web-server based business is rapidly developed and popularized, how to evaluate and improve the reliability of web-servers has been extremely important. Although a large number of software reliability growth models (SRGMs), including those combined with multiple change-points (CPs), have been available, these conventional SRGMs cannot be directly applied to web software reliability analysis because of the complex web operational profile. To characterize the web operational profile precisely, it should be realized that the workload of a web server is normally non-homogeneous and often observed with the pattern of random impulsive shocks. A web software reliability model with random impulsive shocks and its statistical analysis method are developed. In the proposed model, the web server workload is characterized by a geometric Brownian motion process. Based on a real data set from IIS server logs of ICRMS website (www.icrms.cn), the proposed model is demonstrated to be powerful for estimating impulsive shocks and web software reliability.