In this paper, a velocity filtering based track-before-detect algorithm in mixed coordinates is presented to address the problem of integration loss caused by inaccurate motion model in polar coordinate sensors. Since the motion of a constant velocity (CV) target is better modeled in Cartesian coordinates, the search of measurements for integration in polar sensor coordinates is carried out according to the CV model in Cartesian coordinates instead of an approximate model in polar sensor coordinates. The position of each cell is converted into Cartesian coordinates and predicted according to an assumed velocity. Then, the predicted Cartesian position is converted back to polar sensor coordinates for multiframe accumulation. The use of the correct model improves integration effectiveness and consequently improves algorithm performance. To handle the weak target with unknown velocity, a velocity filter bank in mixed coordinates is presented. The influence of velocity mismatch on the performance of filter bank is analyzed, and an efficient strategy for filter bank design is proposed. Numerical results are presented to demonstrate the effectiveness of the proposed algorithm.

Aerial image sequence mosaicking is one of the challenging research fields in computer vision. To obtain large-scale orthophoto maps with object detection information, we propose a vision-based image mosaicking algorithm without any extra location data. According to object detection results, we define a complexity factor to describe the importance of each input image and dynamically optimize the feature extraction process. The feature points extraction and matching processes are mainly guided by the speeded-up robust features (SURF) and the grid motion statistic (GMS) algorithm respectively. A robust reference frame selection method is proposed to eliminate the transformation distortion by searching for the center area based on overlaps. Besides, the sparse Levenberg-Marquardt (LM) algorithm and the heavy occluded frames removal method are applied to reduce accumulated errors and further improve the mosaicking performance. The proposed algorithm is performed by using multithreading and graphics processing unit (GPU) acceleration on several aerial image datasets. Extensive experiment results demonstrate that our algorithm outperforms most of the existing aerial image mosaicking methods in visual quality while guaranteeing a high calculation speed.

In this paper, we propose a beam space coversion (BSC)-based approach to achieve a single near-field signal localization under uniform circular array (UCA). By employing the centro-symmetric geometry of UCA, we apply BSC to extract the two-dimensional (2-D) angles of near-field signal in the Vandermonde form, which allows for azimuth and elevation angle estimation by utilizing the improved estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm. By substituting the calculated 2-D angles into the direction vector of near-field signal, the range parameter can be consequently obtained by the 1-D multiple signal classification (MUSIC) method. Simulations demonstrate that the proposed algorithm can achieve a single near-field signal localization, which can provide satisfactory performance and reduce computational complexity.

Outdoor haze has adverse impact on outdoor image quality, including contrast loss and poor visibility. In this paper, a novel dehazing algorithm based on the decomposition strategy is proposed. It combines the advantages of the two-dimensional variational mode decomposition (2DVMD) algorithm and dark channel prior. The original hazy image is adaptively decomposed into low-frequency and high-frequency images according to the image frequency band by using the 2DVMD algorithm. The low-frequency image is dehazed by using the improved dark channel prior, and then fused with the high-frequency image. Furthermore, we optimize the atmospheric light and transmittance estimation method to obtain a defogging effect with richer details and stronger contrast. The proposed algorithm is compared with the existing advanced algorithms. Experiment results show that the proposed algorithm has better performance in comparison with the state-of-the-art algorithms.

Considering the sparsity of hyperspectral images (HSIs), dictionary learning frameworks have been widely used in the field of unsupervised spectral unmixing. However, it is worth mentioning here that existing dictionary learning method-based unmixing methods are found to be short of robustness in noisy contexts. To improve the performance, this study specifically puts forward a new unsupervised spectral unmixing solution. For the reason that the solution only functions in a condition that both endmembers and the abundances meet non-negative constraints, a model is built to solve the unsupervised spectral unmixing problem on the account of the dictionary learning method. To raise the screening accuracy of final members, a new form of the target function is introduced into dictionary learning practice, which is conducive to the growing robustness of noisy HSI statistics. Then, by introducing the total variation (TV) terms into the proposed spectral unmixing based on robust nonnegative dictionary learning (RNDLSU), the context information under HSI space is to be cited as prior knowledge to compute the abundances when performing sparse unmixing operations. According to the final results of the experiment, this method makes favorable performance under varying noise conditions, which is especially true under low signal to noise conditions.

The existing active tag-based radio frequency identification (RFID) localization techniques show low accuracy in practical applications. To address such problems, we propose a chaotic adaptive genetic algorithm to align the passive tag arrays. We use chaotic sequences to generate the intersection points, the weakest single point intersection is used to ensure the convergence accuracy of the algorithm while avoiding the optimization jitter problem. Meanwhile, to avoid the problem of slow convergence and immature convergence of the algorithm caused by the weakening of individual competition at a later stage, we use adaptive rate of change to improve the optimization efficiency. In addition, to remove signal noise and outliers, we preprocess the data using Gaussian filtering. Experimental results demonstrate that the proposed algorithm achieves higher localization accuracy and improves the convergence speed.

This paper studies a multiple unmanned aerial vehicle (UAV) relaying communication system, where multiple UAV relays assist the blocked communication between a group of ground users (GUs) and a base station (BS). Since the UAVs only have limited-energy in practice, our design aims to maximize the energy efficiency (EE) through jointly designing the communication scheduling, the transmit power allocation, as well as UAV trajectory under the buffer constraint over a given flight period. Actually, the formulated fractional optimization problem is difficult to be solved in general because of non-convexity. To resolve this difficulty, an efficient iterative algorithm is proposed based on the block coordinate descent (BCD) and successive convex approximation (SCA) techniques, as well as the Dinkelbach’s algorithm. Specifically, the optimization variables of the formulated problem are divided into three blocks and we alternately optimize each block of the variables over iteration. Numerical results verify the convergence of the proposed iterative algorithm and show that the proposed designs achieve significant EE gain, which outperform other benchmark schemes.

It is always a challenging issue for radar systems to estimate the height of a low-angle target in the multipath propagation environment. The highly deterministic maximum likelihood estimator has a high accuracy, but the errors of the ground reflection coefficient and the reflecting surface height have serious influence on the method. In this paper, a robust estimation method with less computation burden is proposed based on the compound reflection coefficient multipath model for low-angle targets. The compound reflection coefficient is estimated from the received data of the array and then a one-dimension generalized steering vector is constructed to estimate the target height. The algorithm is robust to the reflecting surface height error and the ground reflection coefficient error. Finally, the experiment and simulation results demonstrate the validity of the proposed method.

This paper proposes a method to improve the spurious-free dynamic ranges (SFDRs) of 1-bit sampled signals greatly, which is very beneficial to multi-tone signals detection. Firstly, the relationship between the fundamental component and the third harmonic component of 1-bit sampled signals is analyzed for determining four contiguous special frequency bands, which do not contain any third harmonics inside and cover 77.8% of the whole Nyquist sampling frequency band. Then, we present a special 4-channel monobit receiver model, where appropriate filter banks are used to obtain four desired pass bands before 1-bit quantization and each channel can sample and process sampled data independently to achieve a good instantaneous dynamic range without sacrificing the real-time performance or computing resources. The simulation results show that the proposed method effectively eliminates the effect of the most harmonics on SFDRs and the mean SFDR is increased to to 20 dB. Besides, the multi-signals simulation results indicate that the maximum amplitude separation (dynamic range) of two signals in each channel is 12 dB while the proposed monobit receiver can deal with up to eight simultaneous arrival signals. In general, the designing method proposed in this paper has a potential engineering value.

This paper dwells upon optimizing the azimuth sampling interval of digital surface maps used to model radar ground clutter. The resulting equations can be used to find the digital map sampling interval for the required calculation error and modeled power of the simulated signal, which determines the resulting distribution of backscatter intensity. The paper further showcases how the sampling interval could be increased by preprocessing the map.

Link prediction of combat networks is of significant military value for precisely identifying the vital infrastructure of the enemy target and optimizing the operational plan of our side. Due to the profound uncertainty in the battleground circumstances, the acquired topological information of the opponent combat network always presents sparse characteristics. To solve this problem, a novel approach named network embedding based combat network link prediction (NECLP) is put forward to predict missing links of sparse combat networks. First, node embedding techniques are presented to preserve as much information of the combat network as possible using a low-dimensional space. Then, we put forward a solution algorithm to predict links between combat networks based on node embedding similarity. Last, massive experiments are carried out on a real-world combat network case to verify the validity and practicality of the proposed NECLP. This paper compares six baseline methods, and experimental results show that the NECLP has outstanding performance and substantially outperforms the baseline methods.

Radiation source identification plays an important role in non-cooperative communication scene and numerous methods have been proposed in this field. Deep learning has gained popularity in a variety of computer vision tasks. Recently, it has also been successfully applied for radiation source identification. However, training deep neural networks for classification requires a large number of labeled samples, and in non-cooperative applications, it is unrealistic. This paper proposes a method for the unlabeled samples of unknown radiation source. It uses semi-supervised learning to detect unlabeled samples and label new samples automatically. It avoids retraining the neural network with parameter-transfer learning. The results show that compared with the traditional algorithms, the proposed algorithm can offer better accuracy.

Due to the randomness and time dependence of the factors affecting software reliability, most software reliability models are treated as stochastic processes, and the non-homogeneous Poisson process (NHPP) is the most used one. However, the failure behavior of software does not follow the NHPP in a statistically rigorous manner, and the pure random method might be not enough to describe the software failure behavior. To solve these problems, this paper proposes a new integrated approach that combines stochastic process and grey system theory to describe the failure behavior of software. A grey NHPP software reliability model is put forward in a discrete form, and a grey-based approach for estimating software reliability under the NHPP is proposed as a nonlinear multi-objective programming problem. Finally, four grey NHPP software reliability models are applied to four real datasets, the dynamic R-square and predictive relative error are calculated. Comparing with the original single NHPP software reliability model, it is found that the modeling using the integrated approach has a higher prediction accuracy of software reliability. Therefore, there is the characteristics of grey uncertain information in the NHPP software reliability models, and exploiting the latent grey uncertain information might lead to more accurate software reliability estimation.

In this paper, an optimization model is proposed to simulate and predict the current situation of smog. The model takes the interval grey number sequence with the known possibility function as the original data, and constructs a time-delay nonlinear multivariable grey model MGM $(1,m|\tau ,\gamma )$ based on the new kernel and degree of greyness sequences considering its time-delay and nonlinearity. The time-delay parameter is determined by the maximum value of the grey time-delay absolute correlation degree, and the nonlinear parameter is determined by the minimum value of average relative error. In order to verify the feasibility of the model, this paper uses the smog related data of Nanjing city for simulation and prediction. Compared with the other four models, the new model has higher simulation and prediction accuracy.

In the world, most of the successes are results of long-term efforts. The reward of success is extremely high, but before that, a long-term investment process is required. People who are “myopic” only value short-term rewards and are unwilling to make early-stage investments, so they hardly get the ultimate success and the corresponding high rewards. Similarly, for a reinforcement learning (RL) model with long-delay rewards, the discount rate determines the strength of agent’s “farsightedness”. In order to enable the trained agent to make a chain of correct choices and succeed finally, the feasible region of the discount rate is obtained through mathematical derivation in this paper firstly. It satisfies the “farsightedness” requirement of agent. Afterwards, in order to avoid the complicated problem of solving implicit equations in the process of choosing feasible solutions, a simple method is explored and verified by theoretical demonstration and mathematical experiments. Then, a series of RL experiments are designed and implemented to verify the validity of theory. Finally, the model is extended from the finite process to the infinite process. The validity of the extended model is verified by theories and experiments. The whole research not only reveals the significance of the discount rate, but also provides a theoretical basis as well as a practical method for the choice of discount rate in future researches.

The research on the damage effectiveness assessment of anti-ship missiles involves system science and weapon science, and has essential strategic research significance. With comprehensive analysis of the specific process of the damage assessment process of anti-missile against ships, a synthetic damage effectiveness assessment process is proposed based on the double hierarchy linguistic term set and the evidence theory. In order to improve the accuracy of the expert’s assessment information, double hierarchy linguistic terms are used to describe the assessment opinions of experts. In order to avoid the loss of experts’ original information caused by information fusion rules, the evidence theory is used to fuse the assessment information of various experts on each case. Good stability of the assessment process can be reflected through sensitivity analysis, and the fluctuation of a certain parameter does not have an excessive influence on the assessment results. The assessment process is accurate enough to be reflected through comparative analysis and it has a good advantage in damage effectiveness assessment.

Projects on unmanned aerial vehicle (UAV) swarms have been initiated in a big way in the last few years, especially from 2015 to 2016. As a result, the number of related works on UAV swarms has been on the rise, with the rate of growth dramatically accelerating since 2017. This research conducts a bibliometric analysis of robotics swarms and UAV swarms to answer the following questions: (i) Disciplines mentioned in the UAV swarms research. (ii) The future development trends and hotspots in the UAV swarms research. (iii) Tracking related outcomes in the UAV swarms research.

In this paper, a trajectory shaping guidance law, which considers constraints of ?eld-of-view (FOV) angle, impact angle, and terminal lateral acceleration, is proposed for a constant speed missile against a stationary target. First, to decouple constraints of the FOV angle and the terminal lateral acceleration, the third-order polynomial with respect to the line-of-sight (LOS) angle is introduced. Based on an analysis of the relationship between the looking angle and the guidance coefficient, the boundary of the coefficient that satisfies the FOV constraint is obtained. The terminal guidance law coefficient is used to guarantee the convergence of the terminal conditions. Furthermore, the proposed law can be implemented under bearings-only information, as the guidance command does not involve the relative range and the LOS angle rate. Finally, numerical simulations are performed based on a kinematic vehicle model to verify the effectiveness of the guidance law. Overall, the work offers an easily implementable guidance law with closed-form guidance gains, which is suitable for engineering applications.

Current successes in artificial intelligence domain have revitalized interest in neural networks and demonstrated their potential in solving spacecraft trajectory optimization problems. This paper presents a data-free deep neural network (DNN) based trajectory optimization method for intercepting non-cooperative maneuvering spacecraft, in a continuous low-thrust scenario. Firstly, the problem is formulated as a standard constrained optimization problem through differential game theory and minimax principle. Secondly, a new DNN is designed to integrate interception dynamic model into the network and involve it in the process of gradient descent, which makes the network endowed with the knowledge of physical constraints and reduces the learning burden of the network. Thus, a DNN based method is proposed, which completely eliminates the demand of training datasets and improves the generalization capacity. Finally, numerical results demonstrate the feasibility and efficiency of our proposed method.

In this paper, the reinforcement learning method for cooperative multi-agent systems (MAS) with incremental number of agents is studied. The existing multi-agent reinforcement learning approaches deal with the MAS with a specific number of agents, and can learn well-performed policies. However, if there is an increasing number of agents, the previously learned in may not perform well in the current scenario. The new agents need to learn from scratch to find optimal policies with others, which may slow down the learning speed of the whole team. To solve that problem, in this paper, we propose a new algorithm to take full advantage of the historical knowledge which was learned before, and transfer it from the previous agents to the new agents. Since the previous agents have been trained well in the source environment, they are treated as teacher agents in the target environment. Correspondingly, the new agents are called student agents. To enable the student agents to learn from the teacher agents, we first modify the input nodes of the networks for teacher agents to adapt to the current environment. Then, the teacher agents take the observations of the student agents as input, and output the advised actions and values as supervising information. Finally, the student agents combine the reward from the environment and the supervising information from the teacher agents, and learn the optimal policies with modified loss functions. By taking full advantage of the knowledge of teacher agents, the search space for the student agents will be reduced significantly, which can accelerate the learning speed of the holistic system. The proposed algorithm is verified in some multi-agent simulation environments, and its efficiency has been demonstrated by the experiment results.

To improve applicability and adaptability of the impact time control guidance (ITCG) in practical engineering, a two-stage ITCG law with simple but effective structure is proposed based on the hybrid proportional navigation, namely, the pure-proportional-navigation and the retro-proportional-navigation. For the case with the impact time error less than zero, the first stage of the guided trajectory is driven by the retro-proportional-navigation and the second one is driven by the pure-proportional-navigation. When the impact time error is greater than zero, both of the stages are generated by the pure-proportional-navigation but using different navigation gains. It is demonstrated by two- and three-dimensional numerical simulations that the proposed guidance law at least has comparable results to existing proportional-navigation-based ITCG laws and is shown to be advantageous in certain circumstances in that the proposed guidance law alleviates its dependence on the time-to-go estimation, consumes less control energy, and adapts itself to more boundary conditions and constraints. The results of this research are expected to be supplementary to the current research literature.

An effective maintenance policy optimization model can reduce maintenance cost and system operation risk. For mission-oriented systems, the degradation process changes dynamically and is monotonous and irreversible. Meanwhile, the risk of early failure is high. Therefore, this paper proposes a dynamic condition-based maintenance (CBM) optimization model for mission-oriented system based on inverse Gaussian (IG) degradation process. Firstly, the IG process with random drift coefficient is used to describe the degradation process and the relevant probability distributions are obtained. Secondly, the dynamic preventive maintenance threshold (DPMT) function is used to control the early failure risk of the mission-oriented system, and the influence of imperfect preventive maintenance (PM) on the degradation amount and degradation rate is analysed comprehensively. Thirdly, according to the mission availability requirement, the probability formulas of different types of renewal policies are obtained, and the CBM optimization model is constructed. Finally, a numerical example is presented to verify the proposed model. The comparison with the fixed PM threshold model and the sensitivity analysis show the effectiveness and application value of the optimization model.

Electric power is widely used as the main energy source of ship integrated power system (SIPS), which contains power network and electric power network. SIPS network reconfiguration is a non-linear large-scale problem. The reconfiguration solution influences the safety and stable operation of the power system. According to the operational characteristics of SIPS, a simplified model of power network and a mathematical model for network reconfiguration are established. Based on these models, a multi-agent and ant colony optimization (MAACO) is proposed to solve the problem of network reconfiguration. The simulations are carried out to demonstrate that the optimization method can reconstruct the integrated power system network accurately and efficiently.