When multiple ground-based radars (GB-rads) are utilized together to resolve three-dimensional (3-D) deformations, the resolving accuracy is related with the measurement geometry constructed by these radars. This paper focuses on constrained geometry analysis to resolve 3-D deformations from three GB-rads. The geometric dilution of precision (GDOP) is utilized to evaluate 3-D deformation accuracy of a single target, and its theoretical equation is derived by building a simplified 3-D coordinate system. Then for a 3-D scene, its optimal accuracy problem is converted into determining the minimum value of an objective function with a boundary constraint. The genetic algorithm is utilized to solve this constrained optimization problem. Numerical simulations are made to validate the correctness of the theoretical analysis results.

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.

The permafrost development in the Qinghai-Tibet Engineering Corridor (QTEC) is affected by natural environment changes and human engineering activities. Human engineering activities may damage the permafrost growing environment, which in turn impact these engineering activities. Thus high spatial-temporal resolution monitoring over the QTEC in the permafrost region is very necessary. This paper presents a method for monitoring the frozen soil area using the intermittent coherence-based small baseline subset (ICSBAS). The method can improve the point density of the results and enhance the interpretability of deformation results by identifying the discontinuous coherent points according to the coherent value of time series. Using the periodic function that models the seasonal variation of permafrost, we separate the long wavelength atmospheric delay and establish an estimation model for the frozen soil deformation. Doing this can raise the monitoring accuracy and improve the understanding of the surface deformation of the frozen soil. In this study, we process 21 PALSAR data acquired by the Alos satellite with the proposed ICSBAS technique. The results show that the frozen soil far from the QTR in the study area experiences frost heave and thaw settlement (4.7 cm to 8.4 cm) alternatively, while the maximum settlement along the QTR reaches 12 cm. The interferomatric syntnetic aperture radar (InSAR)-derived results are validated using the ground leveling data nearby the Beiluhe basin. The validation results show the InSAR results have good consistency with the leveling data in displacement rates as well as time series. We also find that the deformation in the permafrost area is correlated with temperature, human activities and topography. Based on the interfering degree of human engineering activities on the permafrost environment, we divide the QTEC along the Qinghai-Tibet Railway into engineering damage zone, transition zone and natural permafrost.

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.

In the application of persistent scatterer interferometry (PSI), deformation information is extracted from persistent scatterer (PS) points. Thus, the density and position of PS points are critical for PSI. To increase the PS density, a time-series InSAR chain termed as “super-resolution persistent scatterer interferometry” (SR-PSI) is proposed. In this study, we investigate certain important properties of SR-PSI. First, we review the main workflow and dataflow of SR-PSI. It is shown that in the implementation of the Capon algorithm, the diagonal loading (DL) approach should be only used when the condition number of the covariance matrix is sufficiently high to reduce the discontinuities between the joint images. We then discuss the density and positioning accuracy of PS when compared with traditional PSI. The theory and experimental results indicate that SR-PSI can increase the PS density in urban areas. However, it is ineffective for the rural areas, which should be an important consideration for the engineering application of SR-PSI. Furthermore, we validate that the positioning accuracy of PS can be improved by SR-PSI via simulations.

For ship targets with complex motion, it is difficult for the traditional monostatic inverse synthetic aperture radar (ISAR) imaging to improve the cross-range resolution by increasing of accumulation time. In this paper, a distributed ISAR imaging algorithm is proposed to improve the cross-range resolution for the ship target. Multiple stations are used to observe the target in a short time, thereby the effect of incoherence caused by the complex motion of the ship can be reduced. The signal model of ship target with three-dimensional (3-D) rotation is constructed firstly. Then detailed analysis about the improvement of cross-range resolution is presented. Afterward, we propose the methods of parameters estimation to solve the problem of the overlap or gap, which will cause a loss of resolution and is necessary for subsequent processing. Besides, the compressed sensing (CS) method is applied to reconstruct the echoes with gaps. Finally, numerical simulations are presented to verify the effectiveness and the robustness of the proposed algorithm.

Radar detection of small targets in sea clutter is a particularly demanding task because of the nonstationary characteristic of sea clutter. The track-before-detect (TBD) filter is an effective way to increase the signal-to-clutter ratio (SCR), thus improving the detection performance of small targets in sea clutter. To cope with the nonstationary characteristic of sea clutter, an easily-implemented Bayesian TBD filter with adaptive detection threshold is proposed and a new parameter estimation method is devised which is integrated into the detection process. The detection threshold is set according to the parameter estimation result under the framework of information theory. For detection of closely spaced targets, those within the same range cell as the one under test are treated as contribution to sea clutter, and a successive elimination method is adopted to detect them. Simulation results prove the effectiveness of the proposed algorithm in detecting small targets in nonstationary sea clutter, especially closely spaced ones.

Signal reconstruction is a significantly important theoretical issue for compressed sensing. Considering the situation of signal reconstruction with unknown sparsity, the conventional signal reconstruction algorithms usually perform low accuracy. In this work, a sparsity adaptive signal reconstruction algorithm using sensing dictionary is proposed to achieve a lower reconstruction error. The sparsity estimation method is combined with the construction of the support set based on sensing dictionary. Using the adaptive sparsity method, an iterative signal reconstruction algorithm is proposed. The sufficient conditions for the exact signal reconstruction of the algorithm also is proved by theory. According to a series of simulations, the results show that the proposed method has higher precision compared with other state-of-the-art signal reconstruction algorithms especially in a high compression ratio scenarios.

Higher-order statistics based approaches and signal sparseness based approaches have emerged in recent decades to resolve the underdetermined direction-of-arrival (DOA) estimation problem. These model-based methods face great challenges in practical applications due to high computational complexity and dependence on ideal assumptions. This paper presents an effective DOA estimation approach based on a deep residual network (DRN) for the underdetermined case. We first extract an input feature from a new matrix calculated by stacking several covariance matrices corresponding to different time delays. We then provide the input feature to the trained DRN to construct the super resolution spectrum. The DRN learns the mapping relationship between the input feature and the spatial spectrum by training. The proposed approach is superior to existing model-based estimation methods in terms of calculation efficiency, independence of source sparseness and adaptive capacity to non-ideal conditions (e.g., low signal to noise ratio, short bit sequence). Simulations demonstrate the validity and strong performance of the proposed algorithm on both overdetermined and underdetermined cases.

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.

This paper proposes a source localization solution robust to measurement outliers in time differences of arrivals (TDOA) measurements. The solution uses a piecewise loss function named as mixed Huber loss (MHL) proposed based on the classical Huber loss (HL) and its refined version. The MHL is able to effectively mitigate the impact of all levels of measurement outliers by setting two triggering thresholds. In practice, appropriate triggering threshold values can be obtained through simulation given the level of measurement noise and a rough range of potential measurement outliers. A clustering based approach is proposed to further improve the robustness of localization solution against reference sensor related outliers. Simulations are included to examine the solution’s performance and compare it with several benchmarks. The proposed MHL based solution is shown to be superior to the classical solution and the benchmarks. The solution is shown to be even robust to multiple measurement outliers. Furthermore, the influence of range measurement outliers in the reference sensor can be effectively mitigated by the clustering based approach.

To improve the ability of detecting underwater targets under strong wideband interference environment, an efficient method of line spectrum extraction is proposed, which fully utilizes the feature of the target spectrum that the high intense and stable line spectrum is superimposed on the wide continuous spectrum. This method modifies the traditional beam forming algorithm by calculating and fusing the beam forming results at multi-frequency band and multi-azimuth interval, showing an excellent way to extract the line spectrum when the interference and the target are not in the same azimuth interval simultaneously. Statistical efficiency of the estimated azimuth variance and corresponding power of the line spectrum band depends on the line spectra ratio (LSR) of the line spectrum. The change laws of the output signal to noise ratio (SNR) with the LSR, the input SNR, the integration time and the filtering bandwidth of different algorithms bring the selection principle of the critical LSR. As the basis, the detection gain of wideband energy integration and the narrowband line spectrum algorithm are theoretically analyzed. The simulation detection gain demonstrates a good match with the theoretical model. The application conditions of all methods are verified by the receiver operating characteristic (ROC) curve and experimental data from Qiandao Lake. In fact, combining the two methods for target detection reduces the missed detection rate. The proposed post-processing method in 2-dimension with the Kalman filter in the time dimension and the background equalization algorithm in the azimuth dimension makes use of the strong correlation between adjacent frames, could further remove background fluctuation and improve the display effect.

Effectiveness evaluation of the joint operation system is an important basis for the demonstration and development of weapon equipment. With the consideration that existing models of system effectiveness evaluation seldom describe the structural relationship among equipment clearly as well as reflect the dynamic, the analog-to-digital converter-graphical evaluation and review technique (ADC-GERT) network parameter estimation model is proposed based on the ADC model and the joint operation system structure. Firstly, analysis of the joint operation system structure and operation process is conducted to build the GERT network, where equipment subsystems are nodes and activities are directed arches. Then the mission effectiveness of equipment subsystems is calculated by the ADC model. The probability transfer parameters are modified by the mission effectiveness of equipment subsystems based on the Bayesian theorem, with the ADC-GERT network parameter estimation model constructed. Finally, a case study is used to validate the efficiency and dynamic of the ADC-GERT network parameter estimation model.

The emergent task is a kind of uncertain event that satellite systems often encounter in the application process. In this paper, the multi-satellite distributed coordinating and scheduling problem considering emergent tasks is studied. Due to the limitation of onboard computational resources and time, common online onboard rescheduling methods for such problems usually adopt simple greedy methods, sacrificing the solution quality to deliver timely solutions. To better solve the problem, a new multi-satellite onboard scheduling and coordinating framework based on multi-solution integration is proposed. This method uses high computational power on the ground and generates multiple solutions, changing the complex onboard rescheduling problem to a solution selection problem. With this method, it is possible that little time is used to generate a solution that is as good as the solutions on the ground. We further propose several multi-satellite coordination methods based on the multi-agent Markov decision process (MMDP) and mixed-integer programming (MIP). These methods enable the satellite to make independent decisions and produce high-quality solutions. Compared with the traditional centralized scheduling method, the proposed distributed method reduces the cost of satellite communication and increases the response speed for emergent tasks. Extensive experiments show that the proposed multi-solution integration framework and the distributed coordinating strategies are efficient and effective for onboard scheduling considering emergent tasks.

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.

Industrial and academic interest in how to effectively manage technology resources is increasing as it becomes more and more important. Effective managing of technology resources depends on technology management system, and thus understanding how such system evolves becomes an ongoing research topic. Based on the self-organization theory, this paper constructs an evolution model of technology management system. The simulation results show that the evolution of each of the technology management subsystem is affected by the knowledge growth rate of its own, and it is also affected by the coupling and synergy relationship with other subsystems. Moreover, the coupling and synergy relationship can make the speed of evolution higher than the knowledge growth rate of the subsystem itself.

In this paper, a comparative study of the path planning problem using evolutionary algorithms, in comparison with classical methods such as the $ {{\rm{A}}}^{\mathbf{*}} $ algorithm, is presented for a holonomic mobile robot. The configured navigation system, which consists of the integration of sensors sources, map formatting, global and local path planners, and the base controller, aims to enable the robot to follow the shortest smooth path delicately. Grid-based mapping is used for scoring paths efficiently, allowing the determination of collision-free trajectories from the initial to the target position. This work considers the evolutionary algorithms, the mutated cuckoo optimization algorithm (MCOA) and the genetic algorithm (GA), as a global planner to find the shortest safe path among others. A non-uniform motion coefficient is introduced for MCOA in order to increase the performance of this algorithm. A series of experiments are accomplished and analyzed to confirm the performance of the global planner implemented on a holonomic mobile robot. The results of the experiments show the capacity of the planner framework with respect to the path planning problem under various obstacle layouts.

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.

This paper proposes an $ {\cal{L}}_1$ adaptive fault tolerant control method for trajectory tracking of tail-sitter aircraft in the state of motor loss fault. The tail-sitter model considers the uncertainties produced by the features of nonlinearities and couplings which cause difficulties in control. An $ {\cal{L}}_1$ adaptive controller is designed to reduce the position and attitude error when actuators have faults. A reference trajectory containing large maneuver flight transitions is designed, which makes it even harder for the $ {\cal{L}}_1$ controller to track accurately. Compensators are designed to assist $ {\cal{L}}_1$ adaptive controller tracking of the reference trajectory. The stability of the $ {\cal{L}}_1$ adaptive controller including compensators is proved. Finally, the simulation results are used to analyse the effectiveness of the proposed controller. Compared to the ${H_\infty }$ controller, the $ {\cal{L}}_1$ adaptive controller with compensators has better performance in position control and attitude control under fault tolerance state even when the aircraft conducts large maneuver. Besides, as the $ {\cal{L}}_1$ adaptive control method separates feedback control and adaptive law design, the response speed of the whole system is improved.

This paper presents a deep reinforcement learning (DRL)-based motion control method to provide unmanned aerial vehicles (UAVs) with additional flexibility while flying across dynamic unknown environments autonomously. This method is applicable in both military and civilian fields such as penetration and rescue. The autonomous motion control problem is addressed through motion planning, action interpretation, trajectory tracking, and vehicle movement within the DRL framework. Novel DRL algorithms are presented by combining two difference-amplifying approaches with traditional DRL methods and are used for solving the motion planning problem. An improved Lyapunov guidance vector field (LGVF) method is used to handle the trajectory-tracking problem and provide guidance control commands for the UAV. In contrast to conventional motion-control approaches, the proposed methods directly map the sensor-based detections and measurements into control signals for the inner loop of the UAV, i.e., an end-to-end control. The training experiment results show that the novel DRL algorithms provide more than a 20% performance improvement over the state-of-the-art DRL algorithms. The testing experiment results demonstrate that the controller based on the novel DRL and LGVF, which is only trained once in a static environment, enables the UAV to fly autonomously in various dynamic unknown environments. Thus, the proposed technique provides strong flexibility for the controller.

This paper focuses on the influence of the disturbance rejection rate (DRR) and parasitic loop parameters on the stability domain of the roll-pitch seeker’s guidance system. The DRR models of the roll-pitch seeker caused by different types of disturbance torques and the scale deviation of different sensors are established. The optimal DRR model of the roll-pitch seeker, which contains the scale deviation model, is proposed by formula derivation. The model of the roll-pitch seeker’s guidance system is established and equivalently simplified by the dimensionless method. The Lyapunov stability criterion for stability analysis of the guidance system is given by means of the passivity theorem and related definitions and lemmas. A simplified model of the roll-pitch seeker’s guidance system, which is suitable for the Lyapunov stability criterion, is established by formula derivation and equivalent transformation. Three conditions that satisfy the Lyapunov stability criterion are obtained. Mathematical simulation with Nyquist plots is used to analyze the influence of different DRR parameters on the stability domain of the roll-pitch seeker’s guidance system. Simulation results of this paper can provide reference for the stability analysis of systems related to the roll-pitch seeker.

Constellation reconfiguration is a critical issue to recover from the satellite failure, maintain the regular operation, and enhance the overall performance. The constellation reconfiguration problem faces the difficulties of high dimensionality of design variables and extremely large decision space due to the great and continuously growing constellation size. To solve such real-world problems that can be hardly solved by traditional algorithms, the evolutionary operators should be promoted with available domain knowledge to guide the algorithm to explore the promising regions of the trade space. An adaptive innovation-driven multi-objective evolutionary algorithm (MOEA-AI) employing automated innovation (AI) and adaptive operator selection (AOS) is proposed to extract and apply domain knowledge. The available knowledge is extracted from the final or intermediate solution sets and integrated into an operator by the automated innovation mechanism. To prevent the overuse of knowledge-dependent operators, AOS provides top-level management between the knowledge-dependent operators and conventional evolutionary operators. It evaluates and selects operators according to their actual performance, which helps to identify useful operators from the candidate set. The efficacy of the MOEA-AI framework is demonstrated by the simulation of emergency missions. It was verified that the proposed algorithm can discover a non-dominant solution set with better quality, more homogeneous distribution, and better adaptation to practical situations.

Due to unreliable and bandwidth-limited characteristics of communication link in networked control systems, the real-time compensated methods for single-output systems and multi-output systems are proposed in this paper based on the compressed sensing (CS) theory and sliding window technique, by which the estimates of dropping data packets in the feedback channel are obtained and the performance degradation induced by packet drops is reduced. Specifically, in order to reduce the cumulative error caused by the algorithm, the compensated estimates for single-output systems are corrected via the regularization term; considering the process of single-packet transmission, a new sequential CS framework of sensor data streams is introduced to effectively compensate the dropping packet on single-channel of multi-output systems; in presence of the medium access constraints on multi-channel, the communication sequence for scheduling is coupled to the algorithm and the estimates of the multiple sensors for multi-output systems are obtained via the regularization term. Simulation results illustrate that the proposed methods perform well and receive satisfactory performance.