Affected by the natural environmental and human activity factors, significant seasonal differences appear on the regional scattering characteristic and ground deformation of saline soil. Interferometric decorrelation due to season replacement limits the conventional multi-temporal interferometric synthetic aperture radar (MT-InSAR) technique and its application in such areas. To extend the monitoring capability in the salt desert area, we select a vast basin of saline soil around Howz-e-Soltan Salt Lake of Iran as the study area and present an improved MT-InSAR for experimental research. Based on 131 C-band Sentinel-1A images collected between October 2014 to July 2020,1896 refined interferograms in total are selected from all interferogram candidates. Interferometric coherence analysis shows that the coherence in the saline soil area has an apparent seasonal variation, and the soil moisture affected by the precipitation may be the main factor that leads to the seasonal variation. Subsequently, the deformation characteristics of saline soil under different environmental conditions and human activity factors are compared and analyzed in detail. Related deformation mechanisms of different saline soil types are initially revealed by combining interferometric coherence, meteorological data, and engineering geological characteristics of saline soil. Related results would provide reference for the large-scale infrastructure construction engineering in similar saline soil areas.

To avoid the complicated motion compensation in interferometric inverse synthetic aperture (InISAR) and achieve real-time three-dimensional (3D) imaging, a novel approach for 3D imaging of the target only using a single echo is presented. This method is based on an isolated scatterer model assumption, thus the scatterers in the beam can be extracted individually. The radial range of each scatterer is estimated by the maximal likelihood estimation. Then, the horizontal and vertical wave path difference is derived by using the phase comparison technology for each scatterer, respectively. Finally, by utilizing the relationship among the 3D coordinates, the radial range, the horizontal and vertical wave path difference, the 3D image of the target can be reconstructed. The reconstructed image is free from the limitation in InISAR that the image plane depends on the target ’s own motions and on its relative position with respect to the radar. Furthermore, a phase ambiguity resolution method is adopted to ensure the success of the 3D imaging when phase ambiguity occurs. It can be noted that the proposed phase ambiguity resolution method only uses one antenna pair and does not require a priori knowledge, whereas the existing phase ambiguity methods may require two or more antenna pairs or a priori knowledge for phase unwarping. To evaluate the performance of the proposed method, the theoretical analyses on estimation accuracy are presented and the simulations in various scenarios are also carried out.

In this paper, stochastic stabilization is investigated by max-plus algebra for a Markovian jump cloud control system with a reference signal. For the Markovian jump cloud control system, there exists framework adjustment whose evolution is satisfied with a Markov chain. Using max-plus algebra, a max-plus stochastic system is used to describe the Markovian jump cloud control system. A causal feedback matrix is obtained by exponential stability analysis for a causal feedback controller of the Markovian jump cloud control system. A sufficient condition is given to ensure existence on the causal feedback matrix of the causal feedback controller. Based on the causal feedback controller, stochastic stabilization in probability is analyzed for the Markovian jump cloud control system with a reference signal. Simulation results are given to show effectiveness of the causal feedback controller for the Markovian jump cloud control system.

Extreme learning machine (ELM) has been proved to be an effective pattern classification and regression learning mechanism by researchers. However, its good performance is based on a large number of hidden layer nodes. With the increase of the nodes in the hidden layers, the computation cost is greatly increased. In this paper, we propose a novel algorithm, named constrained voting extreme learning machine (CV-ELM). Compared with the traditional ELM, the CV-ELM determines the input weight and bias based on the differences of between-class samples. At the same time, to improve the accuracy of the proposed method, the voting selection is introduced. The proposed method is evaluated on public benchmark datasets. The experimental results show that the proposed algorithm is superior to the original ELM algorithm. Further, we apply the CV-ELM to the classification of superheat degree (SD) state in the aluminum electrolysis industry, and the recognition accuracy rate reaches 87.4%, and the experimental results demonstrate that the proposed method is more robust than the existing state-of-the-art identification methods.

Modern radar signals mostly use low probability of intercept (LPI) waveforms, which have short pulses in the time domain, multicomponent properties, frequency hopping, combined modulation waveforms and other characteristics, making the detection and estimation of LPI radar signals extremely difficult, and leading to highly required significant research on perception technology in the battlefield environment. This paper proposes a visibility graphs (VG)-based multicomponent signals detection method and a modulation waveforms parameter estimation algorithm based on the time-frequency representation (TFR). On the one hand, the frequency domain VG is used to set the dynamic threshold for detecting the multicomponent LPI radar waveforms. On the other hand, the signal is projected into the time and frequency domains by the TFR method for estimating its symbol width and instantaneous frequency (IF). Simulation performance shows that, compared with the most advanced methods, the algorithm proposed in this paper has a valuable advantage. Meanwhile, the calculation cost of the algorithm is quite low, and it is achievable in the future battlefield.

In this paper, a sparse nonuniform rectangular array based on spatially spread electromagnetic vector sensor (SNRA-SSEMVS) is introduced, and a method for estimating 2D-direction of arrival (DOA) and polarization is devised. Firstly, according to the special structure of the sparse nonuniform rectangular array (SNRA), a set of accurate but ambiguous direction-cosine estimates can be obtained. Then the steering vector of spatially spread electromagnetic vector sensor (SSEMVS) can be extracted from the array manifold to obtain the coarse but unambiguous direction-cosine estimates. Finally, the disambiguation approach can be used to get the final accurate estimates of 2D-DOA and polarization. Compared with some existing methods, the SNRA configuration extends the spatial aperture and refines the parameters estimation accuracy without adding any redundant antennas, as well as reduces the mutual coupling effect. Moreover, the proposed algorithm resolves multiple sources without the priori knowledge of signal information, suffers no ambiguity in the estimation of the Poynting vector, and pairs the x-axis direction cosine with the y-axis direction cosine automatically. Simulation results are given to verify the effectiveness and superiority of the proposed algorithm.

Aiming at the problem of gliding near space hypersonic vehicle (NSHV) trajectory prediction, a trajectory prediction method based on aerodynamic acceleration empirical mode decomposition (EMD) is proposed. The method analyzes the motion characteristics of the skipping gliding NSHV and verifies that the aerodynamic acceleration of the target has a relatively stable rule. On this basis, EMD is used to extract the trend of aerodynamic acceleration into multiple sub-items, and aggregate sub-items with similar attributes. Then, a prior basis function is set according to the aerodynamic acceleration stability rule, and the aggregated data are fitted by the basis function to predict its future state. After that, the prediction data of the aerodynamic acceleration are used to drive the system to predict the target trajectory. Finally, experiments verify the effectiveness of the method. In addition, the distribution of prediction errors in space is discussed, and the reasons are analyzed.

The adaptive digital beamforming technique in the space-polarization domain suppresses the interference with forming the coupling nulls of space and polarization domain. When there is the interference in mainlobe, it will cause serious mainlobe distortion, that the target detection suffers from. To overcome this problem and make radar cope with the complex multiple interferences scenarios, we propose a multiple mainlobe and/or sidelobe interferences suppression method for dual polarization array radar. Specifically, the proposed method consists of a signal preprocessing based on the proposed angle estimation with degree of polarization (DoP), and a filtering criterion based on the proposed linear constraint. The signal preprocessing provides the accurate estimated parameters of the interference, which contributes to the criterion for null-decoupling in the space-polarization domain of mainlobe. The proposed method can reduce the mainlobe distortion in the space-polarization domain while suppressing the multiple mainlobe and/or sidelobe interferences. The effectiveness of the proposed method is verified by simulations.

Considering the flexible attitude maneuver and the narrow field of view of agile Earth observation satellite (AEOS) together, a comprehensive task clustering (CTC) is proposed to improve the observation scheduling problem for AEOS (OSPFAS). Since the observation scheduling problem for AEOS with comprehensive task clustering (OSWCTC) is a dynamic combination optimization problem, two optimization objectives, the loss rate (LR) of the image quality and the energy consumption (EC), are proposed to format OSWCTC as a bi-objective optimization model. Harnessing the power of an adaptive large neighborhood search (ALNS) algorithm with a nondominated sorting genetic algorithm II (NSGA-II), a bi-objective optimization algorithm, ALNS+NSGA-II, is developed to solve OSWCTC. Based on the existing instances, the efficiency of ALNS+NSGA-II is analyzed from several aspects, meanwhile, results of extensive computational experiments are presented which disclose that OSPFAS considering CTC produces superior outcomes.

Channel estimation has been considered as a key issue in the millimeter-wave (mmWave) massive multi-input multi-output (MIMO) communication systems, which becomes more challenging with a large number of antennas. In this paper, we propose a deep learning (DL)-based fast channel estimation method for mmWave massive MIMO systems. The proposed method can directly and effectively estimate channel state information (CSI) from received data without performing pilot signals estimate in advance, which simplifies the estimation process. Specifically, we develop a convolutional neural network (CNN)-based channel estimation network for the case of dimensional mismatch of input and output data, subsequently denoted as channel (H) neural network (HNN). It can quickly estimate the channel information by learning the inherent characteristics of the received data and the relationship between the received data and the channel, while the dimension of the received data is much smaller than the channel matrix. Simulation results show that the proposed HNN can gain better channel estimation accuracy compared with existing schemes.

Multi-access interference (MAI) is the main source limiting the capacity and quality of the multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) system which fulfills the demand of high-speed transmission rate and high quality of service for future underwater acoustic (UWA) communication. Therefore, multi-user detection (MUD) is needed at the receiver of the MIMO-OFDM system to suppress the effect of MAI. In this research, MUD is achieved by using a criterion based adaptive recursive successive interference cancellation (RSIC) scheme at the receiver of a MIMO-OFDM system whose transceiver model in underwater communication is implemented by using the Bellhop simulation system. The proposed scheme estimates and eliminates the MAI through user signal detection and subtraction from received signals at the receiver of the MIMO-OFDM system in underwater environment. The bit error rate (BER) performance of the proposed scheme is analyzed by using weight filtering and weight selection criteria. By Matlab simulation, it is shown that the BER performance of the proposed scheme outperforms the conventional matched filter (MF) detector, the adaptive successive interference cancellation (SIC) scheme, and the adaptive RSIC scheme in the UWA network.

Clustering is one of the unsupervised learning problems. It is a procedure which partitions data objects into groups. Many algorithms could not overcome the problems of morphology, overlapping and the large number of clusters at the same time. Many scientific communities have used the clustering algorithm from the perspective of density, which is one of the best methods in clustering. This study proposes a density-based spatial clustering of applications with noise (DBSCAN) algorithm based on the selected high-density areas by automatic fuzzy-DBSCAN (AFD) which works with the initialization of two parameters. AFD, by using fuzzy and DBSCAN features, is modeled by the selection of high-density areas and generates two parameters for merging and separating automatically. The two generated parameters provide a state of sub-cluster rules in the Cartesian coordinate system for the dataset. The model overcomes the problems of clustering such as morphology, overlapping, and the number of clusters in a dataset simultaneously. In the experiments, all algorithms are performed on eight data sets with 30 times of running. Three of them are related to overlapping real datasets and the rest are morphologic and synthetic datasets. It is demonstrated that the AFD algorithm outperforms other recently developed clustering algorithms.

Trajectory clustering can identify the flight patterns of the air traffic, which in turn contributes to the airspace planning, air traffic flow management, and flight time estimation. This paper presents a semantic-based trajectory clustering method for arrival aircraft via new proposed trajectory representation. The proposed method consists of four significant steps: representing the trajectories, grouping the trajectories based on the new representation, measuring the similarities between different trajectories through dynamic time warping (DTW) in each group, and clustering the trajectories based on k-means and density-based spatial clustering of applications with noise (DBSCAN). We take the inbound trajectories toward Shanghai Pudong International Airport (ZSPD) to carry out the case studies. The corresponding results indicate that the proposed method could not only distinguish the particular flight patterns, but also improve the performance of flight time estimation.

Cooperative search-attack is an important application of unmanned aerial vehicle (UAV) swarm in military field. The coupling between path planning and task allocation, the heterogeneity of UAVs, and the dynamic nature of task environment greatly increase the complexity and difficulty of the UAV swarm cooperative search-attack mission planning problem. Inspired by the collaborative hunting behavior of wolf pack, a distributed self-organizing method for UAV swarm search-attack mission planning is proposed. First, to solve the multi-target search problem in unknown environments, a wolf scouting behavior-inspired cooperative search algorithm for UAV swarm is designed. Second, a distributed self-organizing task allocation algorithm for UAV swarm cooperative attacking of targets is proposed by analyzing the flexible labor division behavior of wolves. By abstracting the UAV as a simple artificial wolf agent, the flexible motion planning and group task coordinating for UAV swarm can be realized by self-organizing. The effectiveness of the proposed method is verified by a set of simulation experiments, the stability and scalability are evaluated, and the integrated solution for the coupled path planning and task allocation problems for the UAV swarm cooperative search-attack task can be well performed.

The planetary reducer is a common type of transmission mechanism, which can provide high transmission accuracy and has been widely used, and it is usually required with high reliability of transmission characteristics in practice. During the manufacturing and usage stages of planetary reducers, uncertainties are ubiquitous and wear is inevitable, which affect the transmission characteristics and the reliability of planetary reducers. In this paper, belief reliability modeling and analysis considering multi-uncertainties and wear are proposed for planetary reducers. Firstly, based on the functional principle and the influence of wear, the performance margin degradation model is established using the hysteresis error as the key performance parameter, where the degradation is mainly caused by the accumulated wear. After that, multi-source uncertainties are analyzed and quantified separately, including manufacturing errors, uncertainties in operational and environmental conditions, and uncertainties in performance thresholds. Finally, the belief reliability model is established based on the performance margin degradation model. A case study of a planetary reducer is applied and the reliability sensitivity analysis is implemented to show the practicability of the proposed method. The results show that the proposed method can provide some suggestions to the design and manufacturing phases of the planetary reducer.

Pilot contamination can spoil the accuracy of channel estimation and then has become one of the key problems influencing the performance of massive multiple input multiple output (MIMO) systems. This paper proposes a method based on cell classification and users grouping to mitigate the pilot contamination in multi-cell massive MIMO systems and improve the spectral efficiency. The pilots of the terminals are allocated one-bit orthogonal identifier to diminish the cell categories by the operation of exclusive OR (XOR). At the same time, the users are divided into edge user groups and central user groups according to the large-scale fading coefficients by the clustering algorithm, and different pilot sequences are assigned to different groups. The simulation results show that the proposed method can effectively improve the spectral efficiency of multi-cell massive MIMO systems.

Inverse synthetic aperture radar (ISAR) imaging of near-field targets is potentially useful in some specific applications, which makes it very important to efficiently produce high-quality image of the near-field target. In this paper, the simplified target model with uniform linear motion is applied to the near-field target imaging, which overcomes the complexity of the traditional near-field imaging algorithm. According to this signal model, the method based on coordinate conversion and image interpolation combined with the range-Doppler (R-D) algorithm is proposed to correct the near-field distortion problem. Compared with the back-projection (BP) algorithm, the proposed method produces better focused ISAR images of the near-field target, and decreases the computation complexity significantly. Experimental results of the simulated data have demonstrated the effectiveness and robustness of the proposed method.

In order to improve the autonomous ability of unmanned aerial vehicles (UAV) to implement air combat mission, many artificial intelligence-based autonomous air combat maneuver decision-making studies have been carried out, but these studies are often aimed at individual decision-making in 1v1 scenarios which rarely happen in actual air combat. Based on the research of the 1v1 autonomous air combat maneuver decision, this paper builds a multi-UAV cooperative air combat maneuver decision model based on multi-agent reinforcement learning. Firstly, a bidirectional recurrent neural network (BRNN) is used to achieve communication between UAV individuals, and the multi-UAV cooperative air combat maneuver decision model under the actor-critic architecture is established. Secondly, through combining with target allocation and air combat situation assessment, the tactical goal of the formation is merged with the reinforcement learning goal of every UAV, and a cooperative tactical maneuver policy is generated. The simulation results prove that the multi-UAV cooperative air combat maneuver decision model established in this paper can obtain the cooperative maneuver policy through reinforcement learning, the cooperative maneuver policy can guide UAVs to obtain the overall situational advantage and defeat the opponents under tactical cooperation.

Synthetic aperture radar (SAR) is able to detect surface changes in urban areas with a short revisit time, showing its capability in disaster assessment and urbanization monitoring. Most presented change detection methods are conducted using couples of SAR amplitude images. However, a prior date of surface change is required to select a feasible image pair. We propose an automatic spatio-temporal change detection method by identifying the temporary coherent scatterers. Based on amplitude time series, ${\chi ^2} $ -test and iterative single pixel change detection are proposed to identify all step-times: the moments of the surface change. Then the parameters, e.g., deformation velocity and relative height, are estimated and corresponding coherent periods are identified by using interferometric phase time series. With identified temporary coherent scatterers, different types of temporal surface changes can be classified using the location of the coherent periods and spatial significant changes are identified combining point density and F values. The main advantage of our method is automatically detecting spatio-temporal surface changes without prior information. Experimental results by the proposed method show that both appearing and disappearing buildings with their step-times are successfully identified and results by ascending and descending SAR images show a good agreement.

A novel non-contact spacecraft architecture with the extended stochastic state observer for disturbance rejection control of the gravity satellite is proposed. First, the precise linear driving non-contact voice-coil actuators are used to separate the whole spacecraft into the non-contact payload module and the service module, and to build an ideal loop with precise dynamics for disturbance rejection control of the payload module. Second, an extended stochastic state observer is enveloped to construct the overall nonlinear external terms and the internal coupled terms of the payload module, enabling the controller design of the payload module turned into the linear form with simple bandwidth-parameterization tuning in the frequency domain. As a result, the disturbance rejection control of the payload module can be explicitly achieved in a timely manner without complicated tuning in actual implementation. Finally, an extensive numerical simulation is conducted to validate the feasibility and effectiveness of the proposed approach.

This study focuses on implementing consensus tracking using both open-loop and closed-loop $D^{\alpha} $ -type iterative learning control (ILC) schemes, for fractional-order multi-agent systems (FOMASs) with state-delays. The desired trajectory is constructed by introducing a virtual leader, and the fixed communication topology is considered and only a subset of followers can access the desired trajectory. For each control scheme, one controller is designed for one agent individually. According to the tracking error between the agent and the virtual leader, and the tracking errors between the agent and neighboring agents during the last iteration (for open-loop scheme) or the current running (for closed-loop scheme), each controller continuously corrects the last control law by a combination of communication weights in the topology to obtain the ideal control law. Through the rigorous analysis, sufficient conditions for both control schemes are established to ensure that all agents can achieve the asymptotically consistent output along the iteration axis within a finite-time interval. Sufficient numerical simulation results demonstrate the effectiveness of the control schemes, and provide some meaningful comparison results.

The evolution of airborne tactical networks (ATNs) is impeded by the network ossification problem. As a solution, network virtualization (NV) can provide a flexible and scalable architecture where virtual network embedding (VNE) is a key part. However, existing VNE algorithms cannot be optimally adopted in the virtualization of ATN due to the complex interference in air-combat field. In this context, a highly reliable VNE algorithm based on the transmission rate for ATN virtualization (TR-ATVNE) is proposed to adapt well to the specific electromagnetic environment of ATN. Our algorithm coordinates node and link mapping. In the node mapping, transmission-rate resource is firstly defined to effectively evaluate the ranking value of substrate nodes under the interference of both environmental noises and enemy attacks. Meanwhile, a feasible splitting rule is proposed for path splitting in the link mapping, considering the interference between wireless links. Simulation results reveal that our algorithm is able to improve the acceptance ratio of virtual network requests while maintaining a high revenue-to-cost ratio under the complex electromagnetic interference.

The optimization of inspection intervals for composite structures has been proposed, but only one damage type, dent damage, has been addressed so far. The present study focuses on the two main damage types of dent and delamination, and a model for optimizing the inspection interval of composite structures is proposed to minimize the total maintenance cost on the premise that the probability of structure failure will not exceed the acceptable level. In order to analyze the damage characteristics and the residual strength of the composite structure, the frequency, energy, size, and depth of the damage are studied, and the situation of missing detection during the inspection is considered. The structural residual strength and total maintenance cost are quantified corresponding to different inspection intervals. The proposed optimization method relieves the constraints in previous simulation methods, and is more consistent with the actual situation. Finally, the outer wing of aircraft is taken as an example, and with the historical cases and experimental data, the optimization method is verified. The optimal inspection interval is shorter than the actually implemented inspection interval, and the corresponding maintenance cost is reduced by 23.3%. The result shows the feasibility and effectiveness of the proposed optimization method.

To solve the problem of time difference of arrival (TDOA) positioning and tracking of targets by the unmanned aerial vehicles (UAV) swarm in future air combat, this paper adopts the TDOA positioning method and uses time difference sensors of the UAV swarm to locate target radiation sources. Firstly, a TDOA model for the target is set up for the UAV swarm under the condition that the error variance varies with the received signal-to-noise ratio. The accuracy of the positioning error is analyzed by geometric dilution of precision (GDOP). The D-optimality criterion of the positioning model is theoretically derived. The target is positioned and settled, and the maximum value of the Fisher information matrix determinant is used as the optimization objective function to optimize the track of the UAV in real time. Simulation results show that the track optimization improves the positioning accuracy and stability of the UAV swarm to the target.

This paper analyzes a problem processing mechanism in a new collaboration system between the main manufacturer and the supplier in the “main manufacturer-supplier” mode, which has been widely applied in the collaborative development management of the complex product. This paper adopts the collaboration theory, the evolutionary game theory and numerical simulation to analyze the decision-making mechanism where one upstream supplier and one downstream manufacturer must process an unpredicted problem without any advance contract in common. Results show that both players ’ decision-makings are in some correlation with the initial state, income impact coefficients, and dealing cost. It is worth noting that only the initial state influences the final decision, while income impact coefficients and dealing cost just influence the decision process. This paper shows reasonable and practical suggestions for the manufacturer and supplier in a new collaboration system for the first time and is dedicated to the managerial implications on reducing risks of processing problems.

For forward-looking array synthetic aperture radar (FASAR), the scattering intensity of ground scatterers fluctuates greatly since there are kinds of vegetations and topography on the surface of the ground, and thus the signal-to-noise ratio (SNR) of its echo signals corresponding to different vegetations and topography also varies obviously. Owing to the reason known to all, the performance of the sparse reconstruction of compressed sensing (CS) becomes worse in the case of lower SNR, and the quality of the sparse three-dimensional imaging for FASAR would be affected significantly in the practical application. In this paper, the spatial continuity of the ground scatterers is introduced to the sparse recovery algorithm of CS in the three-dimensional imaging for FASAR, in which the weighted least square method of the cubic interpolation is used to filter out the bad and isolated scatterer. The simulation results show that the proposed method can realize the sparse three-dimensional imaging of FASAR more effectively in the case of low SNR.

The advancement of small satellites is promoting the development of distributed satellite systems, and for the latter, it is essential to coordinate the spatial and temporal relations between mutually visible satellites. By now, dual one-way ranging (DOWR) and two-way time transfer (TWTT) are generally integrated in the same software and hardware system to meet the limitations of small satellites in terms of size, weight and power (SWaP) consumption. However, studies show that pseudo-noise regenerative ranging (PNRR) performs better than DOWR if some advanced implementation technologies are employed. Besides, PNRR has no requirement on time synchronization. To apply PNRR to small satellites, and meanwhile, meet the demand for time difference measurement, we propose the round-way time difference measurement, which can be combined with PNRR to form a new integrated system without exceeding the limits of SWaP. The new integrated system can provide distributed small satellite systems with on-orbit high-accuracy and high-precision distance measurement and time difference measurement in real time. Experimental results show that the precision of ranging is about 1.94 cm, and that of time difference measurement is about 78.4 ps, at the signal to noise ratio of 80 dBHz.

Sliding mode control (SMC) becomes a common tool in designing robust nonlinear control systems, due to its inherent characteristics such as insensitivity to system uncertainties and fast dynamic response. Two modes are involved in the SMC operation, namely reaching mode and sliding mode. In the reaching mode, the system state is forced to reach the sliding surface in a finite time. The major drawback of the SMC approach is the occurrence of chattering in the sliding mode, which is undesirable in most applications. Generally, the trade-off between chattering reduction and fast reaching time must be considered in the conventional SMC design. This paper proposes SMC design with a novel reaching law called the exponential rate reaching law (ERRL) to reduce chattering, and the control structure of the converter is designed based on the multi-input SMC that is applied to a three-phase AC/DC power converter. The simulation and experimental results show the effectiveness of the proposed technique.

The airborne conformal array (CFA) radar’s clutter ridges are range-modulated, which result in a bias in the estimation of the clutter covariance matrix (CCM) of the cell under test (CUT), further, reducing the clutter suppression performance of the airborne CFA radar. The clutter ridges can be effectively compensated by the space-time separation interpolation (STSINT) method, which costs less computation than the space-time interpolation (STINT) method, but the performance of interpolation algorithms is seriously affected by the short-range clutter, especially near the platform height. Location distributions of CFA are free, which yields serious impact that range spaces of steering vector matrices are non-orthogonal complement and even no longer disjoint. Further, a new method is proposed that the short-range clutter is pre-processed by oblique projection with the intersected range spaces (OPIRS), and then clutter data after being pre-processed are compensated to the desired range bin through the STSINT method. The OPIRS also has good compatibility and can be used in combination with many existing methods. At the same time, oblique projectors of OPIRS can be obtained in advance, so the proposed method has almost the same computational load as the traditional compensation method. In addition, the proposed method can perform well when the channel error exists. Computer simulation results verify the effectiveness of the proposed method.

In the field of satellite imagery, remote sensing image captioning (RSIC) is a hot topic with the challenge of overfitting and difficulty of image and text alignment. To address these issues, this paper proposes a vision-language aligning paradigm for RSIC to jointly represent vision and language. First, a new RSIC dataset DIOR-Captions is built for augmenting object detection in optical remote (DIOR) sensing images dataset with manually annotated Chinese and English contents. Second, a Vision-Language aligning model with Cross-modal Attention (VLCA) is presented to generate accurate and abundant bilingual descriptions for remote sensing images. Third, a cross-modal learning network is introduced to address the problem of visual-lingual alignment. Notably, VLCA is also applied to end-to-end Chinese captions generation by using the pre-training language model of Chinese. The experiments are carried out with various baselines to validate VLCA on the proposed dataset. The results demonstrate that the proposed algorithm is more descriptive and informative than existing algorithms in producing captions.

Time series analysis is a key technology for medical diagnosis, weather forecasting and financial prediction systems. However, missing data frequently occur during data recording, posing a great challenge to data mining tasks. In this study, we propose a novel time series data representation-based denoising autoencoder (DAE) for the reconstruction of missing values. Two data representation methods, namely, recurrence plot (RP) and Gramian angular field (GAF), are used to transform the raw time series to a 2D matrix for establishing the temporal correlations between different time intervals and extracting the structural patterns from the time series. Then an improved DAE is proposed to reconstruct the missing values from the 2D representation of time series. A comprehensive comparison is conducted amongst the different representations on standard datasets. Results show that the 2D representations have a lower reconstruction error than the raw time series, and the RP representation provides the best outcome. This work provides useful insights into the better reconstruction of missing values in time series analysis to considerably improve the reliability of time-varying system.

In the evolutionary game of the same task for groups, the changes in game rules, personal interests, the crowd size, and external supervision cause uncertain effects on individual decision-making and game results. In the Markov decision framework, a single-task multi-decision evolutionary game model based on multi-agent reinforcement learning is proposed to explore the evolutionary rules in the process of a game. The model can improve the result of a evolutionary game and facilitate the completion of the task. First, based on the multi-agent theory, to solve the existing problems in the original model, a negative feedback tax penalty mechanism is proposed to guide the strategy selection of individuals in the group. In addition, in order to evaluate the evolutionary game results of the group in the model, a calculation method of the group intelligence level is defined. Secondly, the Q-learning algorithm is used to improve the guiding effect of the negative feedback tax penalty mechanism. In the model, the selection strategy of the Q-learning algorithm is improved and a bounded rationality evolutionary game strategy is proposed based on the rule of evolutionary games and the consideration of the bounded rationality of individuals. Finally, simulation results show that the proposed model can effectively guide individuals to choose cooperation strategies which are beneficial to task completion and stability under different negative feedback factor values and different group sizes, so as to improve the group intelligence level.

Low elevation estimation, which has attracted wide attention due to the presence of specular multipath, is essential for tracking radars. Frequency agility not only has the advantage of strong anti-interference ability, but also can enhance the performance of tracking radars. A frequency-agile refined maximum likelihood (RML) algorithm based on optimal fusion is proposed. The algorithm constructs an optimization problem, which minimizes the mean square error (MSE) of angle estimation. Thereby, the optimal weight at different frequency points is obtained for fusing the angle estimation. Through theoretical analysis and simulation, the frequency-agile RML algorithm based on optimal fusion can improve the accuracy of angle estimation effectively.

Strong spatial variance of the imaging parameters and serious geometric distortion of the image are induced by the acceleration and vertical velocity in a high-squint synthetic aperture radar (SAR) mounted on maneuvering platforms. In this paper, a frequency-domain imaging algorithm is proposed based on a novel slant range model and azimuth perturbation resampling. First, a novel slant range model is presented for mitigating the geometric distortion according to the equal squint angle curve on the ground surface. Second, the correction of azimuth-dependent range cell migration (RCM) is achieved by introducing a high-order time-domain perturbation function. Third, an azimuth perturbation resampling method is proposed for azimuth compression. The azimuth resampling and the time-domain perturbation are used for correcting first-order and high-order azimuthal spatial-variant components, respectively. Experimental results illustrate that the proposed algorithm can improve the focusing quality and the geometric distortion correction accuracy of the imaging scene effectively.

How to make use of limited onboard resources for complex and heavy space tasks has attracted much attention. With the continuous improvement on satellite payload capacity and the increasing complexity of observation requirements, the importance of satellite autonomous task scheduling research has gradually increased. This article first gives the problem description and mathematical model for the satellite autonomous task scheduling and then follows the steps of “satellite autonomous task scheduling, centralized autonomous collaborative task scheduling architecture, distributed autonomous collaborative task scheduling architecture, solution algorithm". Finally, facing the complex and changeable environment situation, this article proposes the future direction of satellite autonomous task scheduling.

To overcome the defects that the traditional approach for multi-objective programming under uncertain random environment (URMOP) neglects the randomness and uncertainty of the problem and the volatility of the results, a new approach is proposed based on expected value-standard deviation value criterion (C_ESD criterion). Firstly, the effective solution to the URMOP problem is defined; then, by applying sequence relationship between the uncertain random variables, the URMOP problem is transformed into a single-objective programming (SOP) under uncertain random environment (URSOP), which are transformed into a deterministic counterpart based on the C_ESD criterion. Then the validity of the new approach is proved that the optimal solution to the SOP problem is also efficient for the URMOP problem; finally, a numerical example and a case application are presented to show the effectiveness of the new approach.

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.

Nonuniform linear arrays, such as coprime array and nested array, have received great attentions because of the increased degrees of freedom (DOFs) and weakened mutual coupling. In this paper, inspired by the existing coprime array, we propose a high-order extended coprime array (HoECA) for improved direction of arrival (DOA) estimation. We first derive the closed-form expressions for the range of consecutive lags. Then, by changing the inter-element spacing of a uniform linear array (ULA), three cases are proposed and discussed. It is indicated that the HoECA can obtain the largest number of consecutive lags when the spacing takes the maximum value. Finally, by comparing it with the other sparse arrays, the optimized HoECA enjoys a larger number of consecutive lags with mitigating mutual coupling. Simulation results are shown to evaluate the superiority of HoECA over the others in terms of DOF, mutual coupling leakage and estimation accuracy.

Unauthorized operations referred to as “black flights” of unmanned aerial vehicles (UAVs) pose a significant danger to public safety, and existing low-attitude object detection algorithms encounter difficulties in balancing detection precision and speed. Additionally, their accuracy is insufficient, particularly for small objects in complex environments. To solve these problems, we propose a lightweight feature-enhanced convolutional neural network able to perform detection with high precision detection for low-attitude flying objects in real time to provide guidance information to suppress black-flying UAVs. The proposed network consists of three modules. A lightweight and stable feature extraction module is used to reduce the computational load and stably extract more low-level feature, an enhanced feature processing module significantly improves the feature extraction ability of the model, and an accurate detection module integrates low-level and advanced features to improve the multiscale detection accuracy in complex environments, particularly for small objects. The proposed method achieves a detection speed of 147 frames per second (FPS) and a mean average precision (mAP) of 90.97% for a dataset composed of flying objects, indicating its potential for low-altitude object detection. Furthermore, evaluation results based on microsoft common objects in context (MS COCO) indicate that the proposed method is also applicable to object detection in general.

This paper investigates the guidance method based on reinforcement learning (RL) for the coplanar orbital interception in a continuous low-thrust scenario. The problem is formulated into a Markov decision process (MDP) model, then a well-designed RL algorithm, experience based deep deterministic policy gradient (EBDDPG), is proposed to solve it. By taking the advantage of prior information generated through the optimal control model, the proposed algorithm not only resolves the convergence problem of the common RL algorithm, but also successfully trains an efficient deep neural network (DNN) controller for the chaser spacecraft to generate the control sequence. Numerical simulation results show that the proposed algorithm is feasible and the trained DNN controller significantly improves the efficiency over traditional optimization methods by roughly two orders of magnitude.