Image fusion based on the sparse representation (SR) has become the primary research direction of the transform domain method. However, the SR-based image fusion algorithm has the characteristics of high computational complexity and neglecting the local features of an image, resulting in limited image detail retention and a high registration misalignment sensitivity. In order to overcome these shortcomings and the noise existing in the image of the fusion process, this paper proposes a new signal decomposition model, namely the multi-source image fusion algorithm of the gradient regularization convolution SR (CSR). The main innovation of this work is using the sparse optimization function to perform two-scale decomposition of the source image to obtain high-frequency components and low-frequency components. The sparse coefficient is obtained by the gradient regularization CSR model, and the sparse coefficient is taken as the maximum value to get the optimal high frequency component of the fused image. The best low frequency component is obtained by using the fusion strategy of the extreme or the average value. The final fused image is obtained by adding two optimal components. Experimental results demonstrate that this method greatly improves the ability to maintain image details and reduces image registration sensitivity.

Tolerance sensitivity limits the practical application of the cross-eye jammer. Previous literature has demonstrated that retrodirective cross-eye jamming with multiple antenna elements possesses the advantage of loose tolerance requirements compared to traditional cross-eye jamming. However, the previous analysis was limited, because there are still some factors affecting the parameter tolerance of the multiple-element retrodirective cross-eye jamming (MRCJ) system and they have not been investigated completely, such as the loop difference, the baseline ratio and the jammer-to-signal ratio. This paper performs a comprehensive tolerance analysis of the MRCJ system with a nonuniform-spacing linear array. Simulation results demonstrate the tolerance effects of the above influence factors and give reasonable advice for easing tolerance sensitivity.

Under the conditions of strong sea clutter and complex moving targets, it is extremely difficult to detect moving targets in the maritime surface. This paper proposes a new algorithm named improved tunable Q-factor wavelet transform (TQWT) for moving target detection. Firstly, this paper establishes a moving target model and sparsely compensates the Doppler migration of the moving target in the fractional Fourier transform (FRFT) domain. Then, TQWT is adopted to decompose the signal based on the discrimination between the sea clutter and the target's oscillation characteristics, using the basis pursuit denoising (BPDN) algorithm to get the wavelet coefficients. Furthermore, an energy selection method based on the optimal distribution of sub-bands energy is proposed to sparse the coefficients and reconstruct the target. Finally, experiments on the Council for Scientific and Industrial Research (CSIR) dataset indicate the performance of the proposed method and provide the basis for subsequent target detection.

Since the joint probabilistic data association (JPDA) algorithm results in calculation explosion with the increasing number of targets, a multi-target tracking algorithm based on Gaussian mixture model (GMM) clustering is proposed. The algorithm is used to cluster the measurements, and the association matrix between measurements and tracks is constructed by the posterior probability. Compared with the traditional data association algorithm, this algorithm has better tracking performance and less computational complexity. Simulation results demonstrate the effectiveness of the proposed algorithm.

The rotating micro-motion parts produce micro-Doppler (m-D) effects which severely influence the quality of inverse synthetic aperture radar (ISAR) imaging for complex moving targets. Recently, a method based on short-time Fourier transform (STFT) and L-statistics to remove m-D effects is proposed, which can separate the rigid body parts from interferences introduced by rotating parts. However, during the procedure of removing m-D parts, the useful data of the rigid body parts are also removed together with the m-D interferences. After summing the rest STFT samples, the result will be affected. A novel method is proposed to recover the missing values of the rigid body parts by the particle swarm optimization (PSO) algorithm. For PSO, each particle corresponds to a possible phase estimation of the missing values. The best particle is selected which has the minimal energy of the side lobes according to the best fitness value of particles. The simulation and measured data results demonstrate the effectiveness of the proposed method.

Real-time resource allocation is crucial for phased array radar to undertake multi-task with limited resources, such as the situation of multi-target tracking, in which targets need to be prioritized so that resources can be allocated accordingly and effectively. A three-way decision-based model is proposed for adaptive scheduling of phased radar dwell time. Using the model, the threat posed by a target is measured by an evaluation function, and therefore, a target is assigned to one of the three possible decision regions, i.e., positive region, negative region, and boundary region. A different region has a various priority in terms of resource demand, and as such, a different radar resource allocation decision is applied to each region to satisfy different tracking accuracies of multi-target. In addition, the dwell time scheduling model can be further optimized by implementing a strategy for determining a proper threshold of three-way decision making to optimize the thresholds adaptively in real-time. The advantages and the performance of the proposed model have been verified by experimental simulations with comparison to the traditional two-way decision model and the three-way decision model without threshold optimization. The experiential results demonstrate that the performance of the proposed model has a certain advantage in detecting high threat targets.

Owing to the advantages in detecting the low altitude and stealth target, passive bistatic radar (PBR) has received much attention in surveillance purposes. Due to the uncontrollable characteristic of the transmitted signal, a high level range or Doppler sidelobes may exist in the ambiguity function which will degrade the target detection performance. Mismatched filtering is a common method to deal with the ambiguity sidelobe problem. However, when mismatched filtering is applied, sidelobes cannot be eliminated completely. The residual sidelobes will cause false-alarm when the constant false alarm ratio (CFAR) is applied. To deal with this problem, a new target detection method based on preprocessing is proposed. In this new method, the ambiguity range and Doppler sidelobes are recognized and eliminated by the preprocessing method according to the prior information. CFAR is also employed to obtain the information of the target echo. Simulation results and results on real data illustrate the effectiveness of the proposed method.

Radial velocity estimation used in wide-band radar systems is investigated. By analyzing the signal of cross-correlation output of adjacent echoes, it is found that the frequency and phase of the cross-correlation output are related to the target's radial velocity. Since the precision of the phase estimation is higher than that of the frequency, a phase-based velocity estimator is proposed. However, the ambiguity problem exists in the phase estimators, and thus the estimation of the cross-correlation of adjacent echoes (CCAE) is used to calculate the ambiguity number. The root-mean-square-error (RMSE) of the proposed estimator is derived. Simulation results show that the performance of the proposed method is better than that of the frequency-based estimator.

Recently, the ontological metamodel plays an increasingly important role to specify systems in two forms: ontology and metamodel. Ontology is a descriptive model representing reality by a set of concepts, their interrelations, and constraints. On the other hand, metamodel is a more classical, but more powerful model in which concepts and relationships are represented in a prescriptive way. This study firstly clarifies the difference between the two approaches, then explains their advantages and limitations, and attempts to explore a general ontological metamodeling framework by integrating each characteristic, in order to implement semantic simulation model engineering. As a proof of concept, this paper takes the combat effectiveness simulation systems as a motivating case, uses the proposed framework to define a set of ontological composable modeling frameworks, and presents an underwater targets search scenario for running simulations and analyzing results. Finally, this paper expects that this framework will be generally used in other fields.

For better reflecting the interactive defense between targets in practical combat scenarios, the basic weapon-target allocation (WTA) framework needs to be improved. A multi-stage attack WTA method is proposed. First, a defense area analysis is presented according to the targets' positions and the radii of the defense areas to analyze the interactive coverage and protection between targets' defense areas. Second, with the coverage status and coverage layer number, a multi-stage attack planning method is proposed and the multi-stage attack objective function model is established. Simulation is conducted with interactive defense combat scenarios, the traditional WTA method and the multi-stage WTA method are compared, and the objective function model is validated with the Monte-Carlo method. The results suggest that if the combat scenario involves interactive coverage of targets' defense areas, it is imperative to analyze the defense areas and apply the multi-stage attack method to weakening the target defense progressively for better combat effectiveness.

Weapon system portfolio selection is an important combinatorial problem that arises in various applications, such as weapons development planning and equipment procurement, which are of concern to military decision makers. However, the existing weapon system-of-systems (SoS) is tightly coupled. Because of the diversity and connectivity of mission requirements, it is difficult to describe the direct mapping relationship from the mission to the weapon system. In the latest service-oriented research, the introduction of service modules to build a service-oriented, flexible, and combinable structure is an important trend. This paper proposes a service-oriented weapon system portfolio selection method, by introducing service to serve as an intermediary to connect missions and system selection, and transferring the weapon system selection into the service portfolio selection. Specifically, the relation between the service and the task is described through the service-task mapping matrix; and the relation between the service and the weapon system is constructed through the service-system mapping matrix. The service collaboration network to calculate the flexibility and connectivity of each service portfolio is then established. Through multi-objective programming, the optimal service portfolios are generated, which are further decoded into weapon system portfolios.

Aiming at the problem that the consumption data of new ammunition is less and the demand is difficult to predict, combined with the law of ammunition consumption under different damage grades, a Bayesian inference method for ammunition demand based on Gompertz distribution is proposed. The Bayesian inference model based on Gompertz distribution is constructed, and the system contribution degree is introduced to determine the weight of the multi-source information. In the case where the prior distribution is known and the distribution of the field data is unknown, the consistency test is performed on the prior information, and the consistency test problem is transformed into the goodness of the fit test problem. Then the Bayesian inference is solved by the Markov chain-Monte Carlo (MCMC) method, and the ammunition demand under different damage grades is gained. The example verifies the accuracy of this method and solves the problem of ammunition demand prediction in the case of insufficient samples.

Owing to the wide range of applications in various fields, generative models have become increasingly popular. However, they do not handle spatio-temporal features well. Inspired by the recent advances in these models, this paper designs a distributed spatio-temporal generative adversarial network (STGAN-D) that, given some initial data and random noise, gene-rates a consecutive sequence of spatio-temporal samples which have a logical relationship. This paper builds a spatio-temporal discriminator to distinguish whether the samples generated by the generator meet the requirements for time and space coherence, and builds a controller for distributed training of the network gradient updated to separate the model training and parameter updating, to improve the network training rate. The model is trained on the skeletal dataset and the traffic dataset. In contrast to traditional generative adversarial networks (GANs), the proposed STGAN-D can generate logically coherent samples with the corresponding spatial and temporal features while avoiding mode collapse. In addition, this paper shows that the proposed model can generate different styles of spatio-temporal samples given different random noise inputs, and the controller can improve the network training rate. This model will extend the potential range of applications of GANs to areas such as traffic information simulation and multi-agent adversarial simulation.

For the classical GM(1, 1) model, the prediction accuracy is not high, and the optimization of the initial and background values is one-sided. In this paper, the Lagrange mean value theorem is used to construct the background value as a variable related to $\boldsymbol k$. At the same time, the initial value is set as a variable, and the corresponding optimal parameter and the time response formula are determined according to the minimum value of mean relative error (MRE). Combined with the domestic natural gas annual consumption data, the classical model and the improved GM(1, 1) model are applied to the calculation and error comparison respectively. It proves that the improved model is better than any other models.

The optical navigation errors of Mars probe in the capture stage depend closely on which targets are selected to be observed in the Mars system. As for this problem, an integrated navigation scheme is proposed wherein the optical observation is aided by one-way Doppler measurements. The errors are then analyzed respectively for the optical observation and one-way Doppler measurements. The real-time calculating scheme which exploits the extended Kalman filter (EKF) framework is designed for the integrated navigation. The simulation tests demonstrate that the errors of optical navigation, which select the Mars moon as the observation target, are relatively smaller than those in the Mars-orientation optical navigation case. On one hand, the integrated navigation errors do not depend on the selecting pattern of optical observation targets. On the other hand, the integrated navigation errors are significantly reduced as compared with those in the optical-alone autonomous navigation mode.

The BeiDou software receiver uses the fast Fourier transform (FFT) to perform the acquisition. The Doppler shift estimation accuracy should be less than 500 Hz to ensure satellite signals to enter a locked state in the tracking loop. Since the frequency step is usually 500 Hz or larger, the Doppler shift estimation accuracy cannot guarantee that satellite signals are brought into a stable tracking state. The straightforward solutions consist in increasing the sampling time and using zero-padding to improve the frequency resolution of the FFT. However, these solutions intensify the complexity and amount of computation. The contradiction between the acquisition accuracy and the computational load leads us to research for a more simple and effective algorithm, which achieves fine acquisition by a look-up table. After coarse acquisition using the parallel frequency acquisition (PFA) algorithm, the proposed algorithm optimizes the Doppler shift estimation through the look-up table method based on the FFT results to improve the acquisition accuracy of the Doppler shift with a minimal additional computing load. When the Doppler shift is within the queryable range of the table, the proposed algorithm can improve the Doppler shift estimation accuracy to 50 Hz for the BeiDou B1I signal.

In reliability engineering, the observations of the variables of interest are always limited due to cost or schedule constraints. Consequently, the epistemic uncertainty, which derives from lack of knowledge and information, plays a vital influence on the reliability evaluation. Belief reliability is a new reliability metric that takes the impact of epistemic uncertainty into consideration and belief reliability distribution is fundamental to belief reliability application. This paper develops a new method called graduation formula to construct belief reliability distribution with limited observations. The developed method constructs the belief reliability distribution by determining the corresponding belief degrees of the observations. An algorithm is designed for the graduation formula as it is a set of transcendental equations, which is difficult to determine the analytical solution. The developed method and the proposed algorithm are illustrated by two numerical examples to show their efficiency and future application.

Up to present, the problem of the evaluation of fault diagnosability for nonlinear systems has been investigated by many researchers. However, no attempt has been done to evaluate the diagnosability of multiple faults occurring simultaneously for nonlinear systems. This paper proposes a method based on differential geometry theories to solve this problem. Then the evaluation of fault diagnosability for affine nonlinear systems with multiple faults occurring simultaneously is achieved. To deal with the effect of control laws on the evaluation results of fault diagnosability, a design scheme of the evaluation of fault diagnosability is proposed. Then the influence of uncertainties on the evaluation results of fault diagnosability for affine nonlinear systems with multiple faults occurring simultaneously is analyzed. The numerical simulation results are obtained to show the effectiveness of the proposed evaluation scheme of fault diagnosability.