A novel practical codebook-precoding multiple-input multiple-output (MIMO) system based on signal space diversity (SSD) with the minimum mean squared error (MMSE) receiver is proposed. This scheme utilizes rotation modulation and space-time-frequency component interleaving. A novel precoding matrix selection criterion to maximize the average signal to interference plus noise ratio (SINR) is also put forward for the proposed scheme, which has a larger average mutual information (AMI). Based on the AMI- maximization criterion, the optimal rotation angles for the proposed system are also investigated. The new scheme can make full use of space-time-frequency diversity and signal space diversity, and exhibit high spectral efficiency and high reliability in fading channels. Simulation results show that the proposed scheme greatly outperforms the conventional bit- interleaved coded modulation (BICM) MIMO-orthogonal frequency division multiplexing (OFDM) scheme without SSD, which is up to 4.5 dB signal-to-noise ratio (SNR) gain.

Compressive sensing (CS) has emerged as a novel sampling framework which enables sparse signal acquisition and reconstruction with fewer measurements below the Nyquist rate. An important issue for CS is the construction of measurement matrix or sensing matrix. A new deterministic sensing matrix, named as OOC-B, is proposed by exploiting optical orthogonal codes (OOCs), Bernoulli matrix and Singer structure, which has the entries of 0, +1 and −1 before normalization. We have proven that the designed deterministic matrix is asymptotically optimal. In addition, the proposed deterministic sensing matrix is applied to direction of arrival (DOA) estimation of narrowband signals by CS arrays (CSA) processing and CS recovery. Theoretical analysis and simulation results show that the proposed sensing matrix has good performance for DOA estimation. It is very effective for simplifying hardware structure and decreasing computational complexity in DOA estimation by CSA processing. Besides, lower root mean square error (RMSE) and bias are obtained in DOA estimation by CS recovery.

For the direction of arrival (DOA) estimation, traditional sparse reconstruction methods for wideband signals usually need many iteration times. For this problem, a new method for two-dimensional wideband signals based on block sparse reconstruction is proposed. First, a prolate spheroidal wave function (PSWF) is used to fit the wideband signals, then the block sparse reconstruction technology is employed for DOA estimation. The proposed method uses orthogonalization to choose the matching atoms, ensuring that the residual components correspond to the minimum absolute value. Meanwhile, the vectors obtained by iteration are back-disposed according to the corresponding atomic matching rules, so the extra atoms are abandoned in the course of iteration, and the residual components of current iteration are reduced. Thus the original sparse signals are reconstructed. The proposed method reduces iteration times comparing with the traditional reconstruction methods, and the estimation precision is better than the classical two-sided correlation transformation (TCT) algorithm when the snapshot is small or the signal-to-noise ratio (SNR) is low.

The conventional direct position determination (DPD) algorithm processes all received signals on a single sensor. When sensors have limited computational capabilities or energy storage, it is desirable to distribute the computation among other sensors. A distributed adaptive DPD (DADPD) algorithm based on diffusion framework is proposed for emitter localization. Unlike the corresponding centralized adaptive DPD (CADPD) algorithm, all but one sensor in the proposed algorithm participate in processing the received signals and estimating the common emitter position, respectively. The computational load and energy consumption on a single sensor in the CADPD algorithm is distributed among other computing sensors in a balanced manner. Exactly the same iterative localization algorithm is carried out in each computing sensor, respectively, and the algorithm in each computing sensor exhibits quite similar convergence behavior. The difference of the localization and tracking performance between the proposed distributed algorithm and the corresponding CADPD algorithm is negligible through simulation evaluations.

With the vigorous expansion of nonlinear adaptive filtering with real-valued kernel functions, its counterpart complex kernel adaptive filtering algorithms were also sequentially proposed to solve the complex-valued nonlinear problems arising in almost all real-world applications. This paper firstly presents two schemes of the complex Gaussian kernel-based adaptive filtering algorithms to illustrate their respective characteristics. Then the theoretical convergence behavior of the complex Gaussian kernel least mean square (LMS) algorithm is studied by using the fixed dictionary strategy. The simulation results demonstrate that the theoretical curves predicted by the derived analytical models consistently coincide with the Monte Carlo simulation results in both transient and steady-state stages for two introduced complex Gaussian kernel LMS algorithms using non-circular complex data. The analytical models are able to be regard as a theoretical tool evaluating ability and allow to compare with mean square error (MSE) performance among of complex kernel LMS (KLMS) methods according to the specified kernel bandwidth and the length of dictionary.

A hierarchical particle filter (HPF) framework based on multi-feature fusion is proposed. The proposed HPF effectively uses different feature information to avoid the tracking failure based on the single feature in a complicated environment. In this approach, the Harris algorithm is introduced to detect the corner points of the object, and the corner matching algorithm based on singular value decomposition is used to compute the firstorder weights and make particles centralize in the high likelihood area. Then the local binary pattern (LBP) operator is used to build the observation model of the target based on the color and texture features, by which the second-order weights of particles and the accurate location of the target can be obtained. Moreover, a backstepping controller is proposed to complete the whole tracking system. Simulations and experiments are carried out, and the results show that the HPF algorithm with the backstepping controller
achieves stable and accurate tracking with good robustness in complex environments.

To reduce the computation burden of a large-aperture multiple-input multiple-output (MIMO) sonar imaging system, the phase-shift beamformer (PSBF) is used at the cost of bringing the intensity loss (IL). The cause of the IL is analyzed in detail and a variable termed as IL factor is defined to quantify the loss amount. To compensate for the IL, two methods termed as intensity compensation for the PSBF (IC-PSBF) and the hybrid beamforming (HBF), respectively, are proposed. The IC-PSBF uses previously estimated IL factors to compensate for output intensities of all PSBFs; and the HBF applies the IC-PSBF to the center beam region and the shifted-sideband beamformer (SSBF) to the side beam region, respectively. Numerical simulations demonstrate the effectiveness of the two proposed methods.

In order to improve the performance of line spectrum detection, according to the feature that the underwater target radiated noise containing stable line spectrum, the differences of the phase difference between line spectrum and background noise, a weighted line spectrum detection algorithm based on the phase variance is proposed in frequency domain. After phase difference alignment, the phase variance of line spectrum and the phase of background noise, respectively, are small and big in frequency domain, this method utilizes the weighted statistical algorithm to cumulate the frequency spectrum based on the phase variance, which can restrain the background noise disturbance, and enhance the signal to noise ratio (SNR). The theory analysis and experimental results both verify that the proposed method can well enhance the energy of line spectrum, restrain the energy of background noise, and have better detection performance under lower SNR.

Relative navigation is a key feature in the joint tactical information distribution system (JTIDS). A parametric message passing algorithm based on factor graph is
proposed to perform relative navigation in JTIDS. First of all, the joint posterior distribution of all the terminals’ positions is represented by factor graph. Because of the nonlinearity between the positions and time-of-arrival (TOA) measurement, messages cannot be obtained in closed forms by directly using the sum-product algorithm on factor graph. To this end, the Euclidean norm is approximated by Taylor expansion. Then, all the messages on the factor graph can be derived in Gaussian forms, which enables the terminals to transmit means and covariances. Finally, the impact of major error sources on the navigation performance are evaluated by Monte Carlo simulations, e.g., range measurement noise, priors of position uncertainty and velocity noise. Results show that the proposed algorithm outperforms the extended Kalman filter and cooperative extended Kalman filter in both static and mobile scenarios of the JTIDS.

Backoff mechanism is a key component of contention-based medium access control (MAC) layer protocol. It has been shown that the backoff mechanism of IEEE 802.11 standard may be very inefficient especially when the network is congested. Numbers of methods have been proposed to tune the contention window (CW) with the aim to achieve the optimal throughput in IEEE 802.11 WLANs. However, the mechanisms do not specifically address proper settings for the variable packet length influence and CW diverging problem. This paper proposes a novel four-way handshaking full-feedback backoff algorithm named adoptive contention window backoff (ACWB) to overcome these drawbacks. The performance of the proposed algorithm is investigated through analysis and simulation. Simulation results demonstrate that the ACWB algorithm provides a remarkable performance improvement in terms of short-term fairness, packet delay and delay jitter, while maintaining an optimal throughput close to the theoretical throughput limit of the IEEE 802.11 distributed coordination function (DCF) access scheme.

For real-time jamming signal generation in deceiving inverse synthetic aperture radar (ISAR), the target characteristics modulation is always processed in the expensive field programmable gate array (FPGA). Due to the large computational complexity of the traditional modulating operation, the size and structure of simulated false-target are limited. With regard to the principle of dechirping in range compression of linear frequency modulated (LFM) radar, a novel algorithm named “inverse dechirping” is proposed for target characteristics modulation. This algorithm only needs one complex multiplier in the FPGA to generate the jamming signal when the radar signal is intercepted, which can be obtained by multiplication of radar signal samplings and the equivalent dechirped target echo in the time domain. As the complex synthesis of dechirped target echo can be realized by cheap digital signal processor (DSP) within the interpulse time, the overall cost of the jamming equipment will be reduced and the false-target size will not be limited by the scale of FPGA. Numerical simulations are performed to verify the correctness and effectiveness of the proposed algorithm.

Bistatic forward-looking synthetic aperture radar (SAR) has many advantages and applications owing to its twodimensional imaging capability. There could be various imaging configurations because of the geometric flexibility of bistatic platforms, resulting in kinds of models built independently among which there could be some similar even the same motion features. Comprehensive research on such systems in a more comprehensive and general point of view is required to address their difference and consistency. Property analysis of bistatic forwardlooking SAR with arbitrary geometry is achieved including stripmap and spotlight modes on airborne platform, missile-borne platform, and hybrid platform of both. Emphasis is placed on azimuth space variance of some key parameters significantly affecting the subsequent imaging processing, based on which the frequency spectra are further described and compared considering respective features of different platforms for frequency imaging algorithm developing. Simulation results confirm the effectiveness and correctness of our analysis.

In an active radar-tracking system, the target-motion model is usually modeled in the Cartesian coordinates, while the radar measurement usually is obtained in polar/spherical coordinates. Therefore the target-tracking problem in the Cartesian coordinates becomes a nonlinear state estimation problem. A number of measurement-conversion techniques, which are based on position measurements, are widely used such that the Kalman filter can be used in the Cartesian coordinates. However, they have fundamental limitations to result in filtering performance degradation. In fact, in addition to position measurements, the Doppler measurement or range rate, containing information of target velocity, has the potential capability to improve the tracking performance. A filter is proposed that can use converted Doppler measurements (i.e. the product of the range measurements and Doppler measurements) in the Cartesian coordinates. The novel filter is theoretically optimal in the rule of the best linear unbiased estimation among all linear unbiased filters in the Cartesian coordinates, and is free of the fundamental limitations of the measurement-conversion approach. Based on simulation experiments, an approximate, recursive implementation of the novel filter is compared with those obtained by four state-of-the-art conversion techniques recently. Simulation results demonstrate the effectiveness of the proposed filter.

It is significant to combine multiple tasks into an optimal work package in decision-making of aircraft maintenance to reduce cost, so a cost rate model of combinatorial maintenance is an urgent need. However, the optimal combination under various constraints not only involves numerical calculations but also is an NP-hard combinatorial problem. To solve the problem, an adaptive genetic algorithm based on cluster search, which is divided into two phases, is put forward. In the first phase, according to the density, all individuals can be homogeneously scattered over the whole solution space through crossover and mutation and better individuals are collected as candidate cluster centres. In the second phase, the search is confined to the neighbourhood of some selected possible solutions to accurately solve with cluster radius decreasing slowly, meanwhile all clusters continuously move to better regions until all the peaks in the question space is searched. This algorithm can efficiently solve the combination problem. Taking the optimization on decision-making of aircraft maintenance by the algorithm for an example, maintenance which combines multiple parts or tasks can significantly enhance economic benefit when the halt cost is rather high.

The platform scheduling problem in battlefield is one of the important problems in military operational research. It needs to minimize mission completing time and meanwhile maximize the mission completing accuracy with a limited number of platforms. Though the traditional certain models obtain some good results, uncertain model is still needed to be introduced since the battlefield environment is complex and unstable. An uncertain model is prposed for the platform scheduling problem. Related parameters in this model are set to be fuzzy or stochastic. Due to the inherent disadvantage of the solving methods for traditional models, a new method is proposed to solve the uncertain model. Finally, the practicability and availability of the proposed method are demonstrated with a case of joint campaign.

The decisions concerning portfolio selection for army engineering and manufacturing development projects determine the benefit of those projects to the country concerned. Projects are typically selected based on ex ante estimates of future return values, which are usually difficult to specify or only generated after project launch. A scenario-based approach is presented here to address the problem of selecting a project portfolio under incomplete scenario information and interdependency constraints. In the first stage, the relevant dominance concepts of scenario analysis are studied to handle the incomplete information. Then, a scenario-based programming approach is proposed to handle the interdependencies to obtain the projects, whose return values are multi-criteria with interval data. Finally, an illustrative example of army engineering and manufacturing development shows the feasibility and advantages of the scenario-based multi-objective programming approach.

Partial cooperation formulation is a more viable option than optimistic’s and pessimistic’s to solve an ill-posed bilevel programming problem. Aboussoror’s partial cooperation model uses a constant as a cooperation index to describe the degree of follower’s cooperation. The constant only indicates the leader’s expectation coefficient for the follower’s action, not the follower’s own willingness. To solve this situation, a new model is proposed by using the follower’s satisfactory degree as the cooperation degree. Then, because this new cooperation degree is a function which is dependent on the leader’s choice and decided by the follower’s satisfactory degree, this paper proves such proposed model not only leads an optimal value between the optimistic value and pessimistic’s, but also leads a more satisfactory solution than Aboussoror’s. Finally, a numerical experiment is given to demonstrate the feasibility of this new model.

This paper focuses on synthesizing a mixed robust H2/H∞ linear parameter varying (LPV) controller for the longitudinal motion of an air-breathing hypersonic vehicle via a high order singular value decomposition (HOSVD) approach. The design of hypersonic flight control systems is highly challenging due to the enormous complexity of the vehicle dynamics and the presence of significant uncertainties. Motivated by recent results on both LPV control and tensor-product (TP) model transformation approach, the velocity and altitude tracking control problems for the air-breathing hypersonic vehicle is reduced to that of a state feedback stabilizing controller design for a polytopic LPV system with guaranteed performances. The controller implementation is converted into a convex optimization problem with parameterdependent linear matrix inequalities (LMIs) constraints, which is intuitively tractable using LMI control toolbox. Finally, numerical simulation results demonstrate the effectiveness of the proposed approach.

A decoupled nonsingular terminal sliding mode control (DNTSMC) approach is proposed to address the tracking control problem of affine nonlinear systems. A nonsingular terminal sliding mode control (NTSMC) method is presented, in which the nonsingular terminal sliding surface is defined as a special nonsingular terminal function and the convergence time of the system states can be specified. The affine nonlinear system is firstly decoupled into linear subsystems via feedback linearization. Then, a nonsingular terminal sliding surface is defined and the NTSMC method is applied to each subsystem separately to ensure the finite time convergence of the closed-loop system. The verification example is given to demonstrate the effectiveness and robustness of the proposed approach. The proposed approach exhibits a considerable advantage in terms of faster tracking error convergence and less chattering compared with the conventional sliding mode control (CSMC).

The problems of stability and stabilization for the discrete Takagi-Sugeno (T-S) fuzzy time-delay system are investigated. By constructing a discrete piecewise Lyapunov-Krasovskii function (PLKF) in each maximal overlapped-rules group (MORG), a new sufficient stability condition for the open-loop discrete T-S fuzzy time-delay system is proposed and proved. Then the systematic design of the fuzzy controller is investigated via the parallel distributed compensation control scheme, and a new stabilization condition for the closed-loop discrete T-S fuzzy time-delay system is proposed. The above two sufficient conditions only require finding common matrices in each MORG. Compared with the common Lyapunov-Krasovskii function (CLKF) approach and the fuzzy Lyapunov-Krasovskii function (FLKF) approach, these proposed sufficient conditions can not only overcome the defect of finding common matrices in the whole feasible region but also largely reduce the number of linear matrix inequalities to be solved. Finally, simulation examples show that the proposed PLKF approach is effective.

The dissociation between data management and data ownership makes it difficult to protect data security and privacy in cloud storage systems. Traditional encryption technologies are not suitable for data protection in cloud storage systems. A novel multi-authority proxy re-encryption mechanism based on ciphertext-policy attribute-based encryption (MPRE-CPABE) is proposed for cloud storage systems. MPRE-CPABE requires data owner to split each file into two blocks, one big block and one small block. The small block is used to encrypt the big one as the private key, and then the encrypted big block will be uploaded to the cloud storage system. Even if the uploaded big block of file is stolen, illegal users cannot get the complete information of the file easily. Ciphertext-policy attribute-based encryption (CPABE) is always criticized for its heavy overload and insecure issues when distributing keys or revoking user’s access right. MPRE-CPABE applies CPABE to the multi-authority cloud storage system, and solves the above issues. The weighted access structure (WAS) is proposed to support a variety of fine-grained threshold access control policy in multi-authority environments, and reduce the computational cost of key distribution. Meanwhile, MPRE-CPABE uses proxy re-encryption to reduce the computational cost of access revocation. Experiments are implemented on platforms of Ubuntu and CloudSim. Experimental results show that MPRE-CPABE can greatly reduce the computational cost of the generation of key components and the revocation of user’s access right. MPRE-CPABE is also proved secure under the security model of decisional bilinear Diffie-Hellman (DBDH).

Digital image stabilization technique plays important roles in video surveillance and object acquisition. Many useful electronic image stabilization algorithms have been studied. A real-time algorithm is proposed based on field image gray projection which enables the regional odd and even field image to be projected into x and y directions and thus to get the regional gray projection curves in x and y directions, respectively. For the odd field image channel, motion parameters can be estimated via iterative minimum absolute difference based on two successive field image regional gray projection curves. Then motion compensations can be obtained after using the Kalman filter method. Finally, the odd field image is adjusted according to the compensations. In the mean time, motion compensation is applied to the even field image channel with the odd field image gray projection curves of the current frame. By minimizing absolute difference between odd and even field image gray projection curves of the current frame, the inter-field motion parameters can be estimated. Therefore, the even field image can be adjusted by combining the inter-field motion parameters and the odd field compensations. Finally, the stabilized image sequence can be obtained by synthesizing the adjusted odd and even field images. Experimental results show that the proposed algorithm can run in real-time and have a good stabilization performance. In addition, image blurring can be improved.

Being as unique nonlinear components of block ciphers, substitution boxes (S-boxes) directly affect the security of the cryptographic systems. It is important and difficult to design cryptographically strong S-boxes that simultaneously meet with multiple cryptographic criteria such as bijection, non-linearity, strict avalanche criterion (SAC), bits independence criterion (BIC), differential probability (DP) and linear probability (LP). To deal with this problem, a chaotic S-box based on the artificial bee colony algorithm (CSABC) is designed. It uses the S-boxes generated by the six-dimensional compound hyperchaotic map as the initial individuals and employs ABC to improve their performance. In addition, it considers the nonlinearity and differential uniformity as the fitness functions. A series of experiments have been conducted to compare multiple cryptographic criteria of this algorithm with other algorithms. Simulation results show that the new algorithm has cryptographically strong S-box while meeting multiple cryptographic criteria.

This paper improves the resampling step of particle filtering (PF) based on a broad interactive genetic algorithm to resolve particle degeneration and particle shortage. For target tracking in image processing, this paper uses the information coming from the particles of the previous fame image and new observation data to self-adaptively determine the selecting range of particles in current fame image. The improved selecting operator with jam gene is used to ensure the diversity of particles in mathematics, and the absolute arithmetical crossing operator whose feasible solution space being close about crossing operation, and non-uniform mutation operator is used to capture all kinds of mutation in this paper. The result of simulating experiment shows that the algorithm of this paper has better iterative estimating capability than extended Kalman filtering (EKF), PF, regularized partide filtering (RPF), and genetic algorithm (GA)-PF.

With the development of the support vector machine (SVM), the kernel function has become one of the cores of the research on SVM. To a large extent, the kernel function determines the generalization ability of the classifier, but there is still no general theory to guide the choice and structure of the kernel function. An ensemble kernel function model based on the game theory is proposed, which is used for the SVM classification algorithm. The model can effectively integrate the advantages of the local kernel and the global kernel to get a better classification result, and can provide a feasible way for structuring the kernel function. By making experiments on some standard datasets, it is verified that the new method can significantly improve the accuracy of classification.

In recent decades, many software reliability growth models (SRGMs) have been proposed for the engineers and testers in measuring the software reliability precisely. Most of them is established based on the non-homogeneous Poisson process (NHPP), and it is proved that the prediction accuracy of such models could be improved by adding the describing of characterization of testing effort. However, some research work indicates that the fault detection rate (FDR) is another
key factor affects final software quality. Most early NHPPbased models deal with the FDR as constant or piecewise function, which does not fit the different testing stages well. Thus, this paper first incorporates a multivariate function of FDR, which is bathtub-shaped, into the NHPP-based SRGMs considering testing effort in order to further improve performance. A new model framework is proposed, and a stepwise method is used to apply the framework with real data sets to find the optimal model. Experimental studies show that the obtained new model can provide better performance of fitting and prediction compared with other traditional SRGMs.

The parameter estimation is considered for the Gompertz distribution under frequensitst and Bayes approaches when records are available. Maximum likelihood
estimators, exact and approximate confidence intervals are developed for the model parameters, and Bayes estimators of reliability performances are obtained under different losses based on a mixture of continuous and discrete priors. To investigate the performance of the proposed estimators, a record simulation algorithm is provided and a numerical study is presented by using Monte-Carlo simulation.