Based on a uniform linear array, a new widely linear unscented Kalman filter-based constant modulus algorithm (WLUKF-CMA) for blind adaptive beamforming is proposed. The new algorithm is designed according to the constant modulus criterion and takes full advantage of the noncircular property of the signal of interest (SOI), significantly increasing the output signal-tointerference-plus-noise ratio (SINR), enhancing the convergence speed and decreasing the steady-state misadjustment. Since it requires no known training data, the proposed algorithm saves a large amount of the available spectrum. Theoretical analysis and simulation results are presented to demonstrate its superiority over the conventional linear least mean square-based CMA (LLMS-CMA), the conventional linear recursive least square-based CMA (L-RLS-CMA), WL-LMS-CMA, WL-RLS-CMA and L-UKFCMA.

Two novel schemes are proposed to synthesize highresolution range profile (HRRP) based on co-located multiple-input multiple-output (MIMO) system in the context of the joint radar and communication system. The difference between two schemes is the pattern of selecting pulses, which depends on the demand for the velocity information. The system, a type of frequency diverse array (FDA), takes full advantage of the phase-coded orthogonal frequency division multiplexing (OFDM) signal. Furthermore, the complete discrete form of the phase-coded OFDM echoes is utilized to derive the HRRP processing. The velocity estimation in the second scheme aims to eliminate velocity ambiguity, and high velocity can be retrieved exactly. Meanwhile, the imaging method is investigated with random frequency coding applied to an array. The desired performance of resolving velocity ambiguity and suppressing noise is shown by means of comparisons with previous work. The advantages in the radar imaging and the significance of the work are concluded in the end.

The in-phase and quadrature-phase imbalance (IQI) is one of the major radio frequency impairments existing in orthogonal frequency division multiplexing (OFDM) systems with direct-conversion transceivers. During the transmission of the communication signal, the impact of IQI is coupled with channel impulse responses (CIR), which makes the traditional channel estimation schemes ineffective. A decoupled estimation scheme is proposed to separately estimate the frequency-dependent IQI and wireless channel. Firstly, the generalized channel model is built to separate the parameters of IQI and wireless channel. Then an iterative estimation scheme of frequency-dependent IQI is designed at the initial stage of communication. Finally, based on the estimation result of IQI, the least square algorithm is utilized to estimate the channel-related parameters at each time of channel variation. Compared with the joint estimation schemes of IQI and channel, the proposed decoupled estimation scheme requires much lower training overhead at each time of channel variation. Simulation results demonstrate the good estimation performance of the proposed scheme.

The satellite-based automatic identification system (AIS) receiver has to encounter the frequency offset caused by the Doppler effect and the oscillator instability. This paper proposes a non-coherent sequence detection scheme for the satellite-based AIS signal transmitted over the white Gaussian noise channel. Based on the maximum likelihood estimation and a Viterbi decoder, the proposed scheme is capable of tolerating a frequency offset up to 5% of the symbol rate. The complexity of the proposed scheme is reduced by the state-complexity reduction, which is based on per-survivor processing. Simulation results prove that the proposed non-coherent sequence detection scheme has high robustness to frequency offset compared to the relative scheme when messages collision exists.

To improve the data rate of underwater acoustic frequency-hopped communications, frequency hopping is applied to different orders of fractional Fourier domain (FrFD), to enable non-intrusive, bandwidth-limited acoustic communications. An FrFD frequency-hopped communication method based on chirp modulation, namely multiple chirp shift keying-FrFD hopping (MCSK-FrFDH), is proposed for underwater acoustic channels. Validated by both simulations and experimental results, this method can reach a bandwidth efficiency twice more than conventional frequency-hopped methods with the same data rate and anti-multipath capability, suggesting that the proposed method achieves a better performance than the traditional frequencyhopped communication in underwater acoustic communication channels. Results also show that in practical scenarios, the MCSKFrFDH system with longer symbol length performs better at the low signal-to-noise ratio (SNR), while the system with larger frequency sweeping range performs better at a high SNR.

For the polarimetric synthetic aperture radar interferometry (PolInSAR) processing, it is necessary to coregister all the images, including the coregistration of polarimetric SAR images and the coregistration of interferometric SAR images. Otherwise, the performance of the estimated optimal interferograms will be deteriorated. A generalized scattering vector (GSV) model is proposed to execute the PolInSAR optimal interferograms estimation. The generalized scattering vector is constructed by the Pauli scattering vectors of the processing pixel and the surrounding pixels. Even though there are coregistration errors, all the polarimetric information of the current processing pixel is entirely included in the generalized scattering vector. Therefore, the GSV-based method can automatically recover the optimal scattering mechanisms of the processing pixel with coregistration errors either in interferoemetric channels or polarimetric channels. Theoretical analysis and processing results of simulated PolSARPro data and real PALSAR data validate the effectiveness and correctness of the proposed method.

The dynamic characteristics related to micro-motions, such as mechanical vibration or rotation, play an essential role in classifying and recognizing ballistic targets in the midcourse, and recent researches explore ways of extracting the micro-motion features from radar signals of ballistic targets. In this paper, we focus on how to investigate the micro-motion dynamic characteristics of the ballistic targets from the signals based on infrared (IR) detection, which is mainly achieved by analyzing the periodic fluctuation characteristics of the target IR irradiance intensity signatures. Simulation experiments demonstrate that the periodic characteristics of IR signatures can be used to distinguish different micromotion types and estimate related parameters. Consequently, this is possible to determine the micro-motion dynamics of ballistic targets based on IR detection.

The seriously range-ambiguous clutter is one of the main problems in clutter suppression for hypersonic vehicle-borne forward-looking radar. An approach based on the frequency diverse array (FDA) technique is proposed to mitigate the rangeambiguous clutter. The frequency increment is designed to distinguish the clutter at ambiguous ranges and suppress the clutter by using a subspace projection algorithm. On the platform with high altitude or limited array antennas, the proposed method performs better for its independence of the elevation degrees-of-freedom (DOF). Finally, simulation results verify the effectiveness of the proposed method.

Multi-radar signal separation is a critical process in modern reconnaissance systems. However, the complicated battlefield is typically confronted with increasing electronic equipment and complex radar waveforms. The intercepted signal is difficult to separate with conventional parameters because of severe overlapping in both time and frequency domains. On the contrary, timefrequency analysis maps the 1D signal into a 2D time-frequency plane, which provides a better insight into the signal than traditional methods. Particularly, the parameterized time-frequency analysis (PTFA) shows great potential in processing such nonstationary signals. Five procedures for the PTFA are proposed to separate the overlapped multi-radar signal, including initiation, instantaneous frequency estimation with PTFA, signal demodulation, signal separation with adaptive filter and signal recovery. The proposed method is verified with both simulated and real signals, which shows good performance in the application on multi-radar signal separation.

Aiming at the problem that the traditional Dempster-Shafer (D-S) evidence theory cannot deal with conflicted evidences effectively and correctly, this paper points out that the key issue of this problem is to measure the degree of conflict between evidences correctly after analyzing various improved methods. The existing evidence conflict measure methods are analyzed, and a new evidence conflict measure method called evidence similarity measure based on the Tanimoto measure is proposed, while a new evidence combination method is proposed on the basis of evidence similarity measure. Firstly, the conflict degrees between evidences are obtained through the evidence similarity measure. Then the evidence sources are modified based on the credibility of different evidences and the weights of conflicted parts of evidences on different focal elements are determined. Finally, the fusion result is obtained by this method. Numerical examples show that the proposed method can effectively fuse evidences when evidences are consistent or highly conflicted, and it has a fast convergence speed, a high degree of accuracy and good adaptability.

Ballistic missile defense system (BMDS) is important
for its special role in ensuring national security and maintaining
strategic balance. Research on modeling and simulation of the
BMDS beforehand is essential as developing a real one requires
lots of manpower and resources. BMDS is a typical complex system
for its nonlinear, adaptive and uncertainty characteristics.
The agent-based modeling method is well suited for the complex
system whose overall behaviors are determined by interactions
among individual elements. A multi-agent decision support system
(DSS), which includes missile agent, radar agent and command
center agent, is established based on the studies of structure and
function of BMDS. Considering the constraints brought by radar,
intercept missile, offensive missile and commander, the objective
function of DSS is established. In order to dynamically generate
the optimal interception plan, the variable neighborhood negative
selection particle swarm optimization (VNNSPSO) algorithm is
proposed to support the decision making of DSS. The proposed
algorithm is compared with the standard PSO, constriction factor
PSO (CFPSO), inertia weight linear decrease PSO (LDPSO),
variable neighborhood PSO (VNPSO) algorithm from the aspects
of convergence rate, iteration number, average fitness value and
standard deviation. The simulation results verify the efficiency of
the proposed algorithm. The multi-agent DSS is developed through
the Repast simulation platform and the constructed DSS can generate
intercept plans automatically and support three-dimensional
dynamic display of missile defense process.

This paper studies the problem of using multiple unmanned
air vehicles (UAVs) to search for moving targets with
sensing capabilities. When multiple UAVs (multi-UAV) search for a
number of moving targets in the mission area, the targets can intermittently
obtain the position information of the UAVs from sensing
devices, and take appropriate actions to increase the distance
between themselves and the UAVs. Aiming at this problem, an
environment model is established using the search map, and the
updating method of the search map is extended by considering the
sensing capabilities of the moving targets. A multi-UAV search path
planning optimization model based on the model predictive control
(MPC) method is constructed, and a hybrid particle swarm optimization
algorithm with a crossover operator is designed to solve
the model. Simulation results show that the proposed method can
effectively improve the cooperative search efficiency and can find
more targets per unit time compared with the coverage search
method and the random search method.

This paper proposes an effective algorithm to work out the linear parameter-varying (LPV) framework autopilot for the air defense missile so as to simultaneously guarantee the closed-loop system properties globally and locally, which evidently reduces the number of unknown variables and hence increases the computational efficiency. The notion of “robust quadratic stability” is inducted to meet the global properties, including the robust stability and robust performance, while the regional pole placement scheme together with the adoption of a model matching structure is involved to satisfy the dynamic performance, including limiting the “fast poles”. In order to reduce the conservatism, the full block multiplier is employed to depict the properties, with all specifications generalized in integral quadratic constraint frame and finally transformed into linear matrix inequalities for tractable solutions through convex optimization. Simulation results validate the performance of the designed robust LPV autopilot and the proposed framework control method integrating with the full block multiplier approach and the regional pole placement scheme, and demonstrate the efficiency of the algorithm. An efficient algorithm for the air defense missile is proposed to satisfy the required global stability and local dynamical properties by a varying controller according to the flight conditions, and shows sufficient promise in the computational efficiency and the real-time performance of the missile-borne computer system.

A novel integrated guidance and control (IGC) design method is proposed to solve problems of low control accuracy for a suicide unmanned combat aerial vehicle (UCAV) in the terminal attack stage. First of all, the IGC system model of the UCAV is built based on the three-channel independent design idea, which reduces the difficulties of designing the controller. Then, IGC control laws are designed using the trajectory linearization control (TLC). A nonlinear disturbance observer (NDO) is introduced to the IGC controller to reject various uncertainties, such as the aerodynamic parameter perturbation and the measurement error interference. The stability of the closed-loop system is proven by using the Lyapunov theorem. The performance of the proposed IGC design method is verified in a terminal attack mission of the suicide UCAV. Finally, simulation results demonstrate the superiority and effectiveness in the aspects of guidance accuracy and system robustness.

A new Gaussian approximation nonlinear filter called generalized cubature quadrature Kalman filter (GCQKF) is introduced for nonlinear dynamic systems. Based on standard GCQKF, two extensions are developed, namely square root generalized cubature quadrature Kalman filter (SR-GCQKF) and iterated generalized cubature quadrature Kalman filter (I-GCQKF). In SR-GCQKF, the QR decomposition is exploited to alter the Cholesky decomposition and both predicted and filtered error covariances have been propagated in square root format to make sure the numerical stability. In I-GCQKF, the measurement update step is executed iteratively to make full use of the latest measurement and a new terminal criterion is adopted to guarantee the increase of likelihood. Detailed numerical experiments demonstrate the superior performance on both tracking stability and estimation accuracy of I-GCQKF and SR-GCQKF compared with GCQKF.

Fuze is the information processing and control unit of the ammunition, so the quality of the fuze becomes one of the most important aspects of ammunition detection. Since using recoil force is a common method to the arm fuze, its dynamic simulation test has always been the focus of the fuze test research. A new fuze recoil environmental simulation method is proposed based on the electromagnetic launcher. Then the trigger control characteristics of the fuze recoil simulation system and the influence of the trigger position on the recoil force are studied. The results of the study show that although the pulse width of the armature force curve can be changed by adjusting the trigger position, due to the limit of the range, there also exists the contradiction that the electromagnetic pulse width gets narrow with the increase of electromagnetic force peak. Thus, it cannot meet the requirements of the fuze launch recoil simulation. In order to make the recoil force close to the actual environment, the multi-stage trigger control characteristics are analyzed, and the influence of trigger position on recoil environmental force characteristics is studied. Then a fuze launch recoil environmental simulation platform is established and continuous electromagnetic force is achieved by using the trigger strategy. Finally, the experiment is performed to simulate the fuze launch recoil environment and show the feasibility and effectiveness of the proposed theoretical analysis. The major research work of this paper includes studying the composition and basic principle of the simulation system, establishing a launch model to analyze the single-stage and multi-stage coil fuze launch recoil characteristics, designing the test device to verify the correctness and validity of the research. This paper draws the conclusions that the feasibility of the fuze launch environmental simulation based on the electromagnetic launcher is verified, the trigger position has a great influence on force peak continuity, the problems of low maximum overload peak and short peak duration in the multi-stage coil fuze launch environmental simulation can be effectively solved through adjusting the trigger position, the system has creative and extensive application prospects.

A novel simulation method for fuze warhead system (FWS) at very low altitude flight is proposed to solve adaptability issues of the traditional one in the naval battle. Firstly, a simulation system framework is presented. Then the detailed implementation of a novel general fuze model, a novel sea echo model and a novel warhead dynamic effectiveness power field algorithm including the simulation system are presented. Finally, simulation results show good performance of the proposed method. The proposed method can simulate the echo signal when the complex fuze antennas detect target and the sea at the same time, and can truly reflect the target positions hit by the warhead fragments. The proposed method can solve the existing problems in the FWS simulation system.

To enhance the accuracy of intuitionistic fuzzy time
series forecasting model, this paper analyses the influence of
universe of discourse partition and compares with relevant literature.
Traditional models usually partition the global universe of
discourse, which is not appropriate for all objectives. For example,
the universe of the secular trend model is continuously variational.
In addition, most forecasting methods rely on prior information, i.e.,
fuzzy relationship groups (FRG). Numerous relationship groups
lead to the explosive growth of relationship library in a linear model
and increase the computational complexity. To overcome problems
above and ascertain an appropriate order, an intuitionistic
fuzzy time series forecasting model based on order decision and
adaptive partition algorithm is proposed. By forecasting the vector
operator matrix, the proposed model can adjust partitions and
intervals adaptively. The proposed model is tested on student enrollments
of Alabama dataset, typical seasonal dataset Taiwan
Stock Exchange Capitalization Weighted Stock Index (TAIEX) and
a secular trend dataset of total retail sales for social consumer
goods in China. Experimental results illustrate the validity and applicability
of the proposed method for different patterns of dataset.

In foggy weather, images of outdoor scene are usually
characterized with poor visibility as well as faint color saturation.
The degraded hazy images may have substantial negative impact
on most computer vision systems. Thus image haze removal is
of the practical significance in engineering. This paper proposes a
fast and effective single image haze removal algorithm on the basis
of the physics imaging model. To extract the global atmospheric
light accurately, we exploit multiple prior rules underlying hazy images,
and put forward a novel measurement to judge the likelihood
that a pixel is regarded as the global atmospheric light. In addition,
the rough transmission map is estimated through a multiscale fusion
process based on the Laplace pyramid transform, and refined
by a total variation model. Experimental results demonstrate the
proposed method outperforms most of the state-of-the-art algorithms
in terms of the dehazing quality, and achieves a trade-off
between the computational efficiency and haze removal capability.

The degradation data of multi-components in missile is derived by periodical testing. How to use these data to assess the storage reliability (SR) of the whole missile is a difficult problem in current research. An SR assessment model based on competition failure of multi-components in missile is proposed. By analyzing the missile life profile and its storage failure feature, the key components in missile are obtained and the characteristics voltage is assumed to be its key performance parameter. When the voltage testing data of key components in missile are available, a state space model (SSM) is applied to obtain the whole missile degradation state, which is defined as the missile degradation degree (DD). A Wiener process with the time-scale model (TSM) is applied to build the degradation failure model with individual variability and nonlinearity. The Weibull distribution and proportional risk model are applied to build an outburst failure model with performancedegradation effect. Furthermore, a competition failure model with the correlation between  degradation failure and outburst failure is proposed. A numerical example with a set of missiles in storage is analyzed to demonstrate the accuracy and superiority of the proposed model.

The Internet now is a large-scale platform with big data. Finding truth from a huge dataset has attracted extensive attention, which can maintain the quality of data collected by users and provide users with accurate and efficient data. However, current truth finder algorithms are unsatisfying, because of their low accuracy and complication. This paper proposes a truth finder algorithm based on entity attributes (TFAEA). Based on the iterative computation of source reliability and fact accuracy, TFAEA considers the interactive degree among facts and the degree of dependence among sources, to simplify the typical truth finder algorithms. In order to improve the accuracy of them, TFAEA combines the oneway text similarity and the factual conflict to calculate the mutual support degree among facts. Furthermore, TFAEA utilizes the symmetric saturation of data sources to calculate the degree of dependence among sources. The experimental results show that TFAEA is not only more stable, but also more accurate than the typical truth finder algorithms.