In some tracking applications, due to the sensor characteristic, only range measurements are available. If this is the case, due to the lack of full position measurements, the observability of Cartesian states (e.g., position and velocity) are limited to particular cases. For general cases, the range measurements can be utilized by developing a state estimation algorithm in range-Doppler (R-D) plane to obtain accurate range and Doppler estimates. In this paper, a state estimation method based on the proper dynamic model in the R-D plane is proposed. The unscented Kalman filter is employed to handle the strong nonlinearity in the dynamic model. Two filtering initialization methods are derived to extract the initial state estimate and the initial covariance in the R-D plane from the first several range measurements. One is derived based on the well-known two-point differencing method. The other incorporates the correct dynamic model information and uses the unscented transformation method to obtain the initial state estimates and covariance, resulting in a model-based method, which capitalizes the model information to yield better performance. Monte Carlo simulation results are provided to illustrate the effectiveness and superior performance of the proposed state estimation and filter initialization methods.

The scattering centers (SCs) of low-detectable targets (LDTs) have a low scattering intensity. It is difficult to build the SC model of an LDT using the existing methods because these methods mainly concern dominant SCs with strong scattering contributions. This paper presents an SC modeling approach to acquire the weak SCs of LDTs. We employ the induced currents at the LDT to search SCs, and the joint time-frequency transform together with the Hough transform to separate the scattering contributions of different SCs. Particle swarm optimization (PSO) is applied to improve the estimation results of SCs. The accuracy of the SC model built by this approach is verified by a full-wave numerical method. The validation results show that the SC model of the LDT can precisely simulate the signatures of high-resolution images, such as high-resolution range profile and inverse synthetic aperture radar (ISAR) images.

Radio frequency fingerprinting (RFF) is a technology that identifies the specific emitter of a received electromagnetic signal by external measurement of the minuscule hardware-level, device-specific imperfections. The RFF-related information is mainly in the form of unintentional modulation (UIM), which is subtle enough to be effectively imperceptible and is submerged in the intentional modulation (IM). It is necessary to minimize the influence of the IM and expand the slight differences between emitters for successful RFF. This paper proposes a UIM microstructure enlargement (UMME) method based on feature-level adaptive signal decomposition (ASD), accompanied by autocorrelation and cross-correlation analysis. The common IM part is evaluated by analyzing a newly-defined benchmark feature. Three different indexes are used to quantify the similarity, distance, and dependency of the RFF features from different devices. Experiments are conducted based on the real-world signals transmitted from 20 of the same type of radar in the same working mode. The visual image qualitatively shows the magnification of feature differences; different indicators quantitatively describe the changes in features. Compared with the original RFF feature, recognition results based on the Gaussian mixture model (GMM) classifier further validate the effectiveness of the proposed algorithm.

In this paper, we first propose a memristive chaotic system and implement it by circuit simulation. The chaotic dynamics and various attractors are analysed by using phase portrait, bifurcation diagram, and Lyapunov exponents. In particular, the system has robust chaos in a wide parameter range and the initial value space, which is favourable to the security communication application. Consequently, we further explore its application in image encryption and present a new scheme. Before image processing, the external key is protected by the Grain-128a algorithm and the initial values of the memristive system are updated with the plain image. We not only perform random pixel extraction and masking with the chaotic cipher, but also use them as control parameters for Brownian motion to obtain the permutation matrix. In addition, multiplication on the finite field GF(2⁸) is added to further enhance the cryptography. Finally, the simulation results verify that the proposed image encryption scheme has better performance and higher security, which can effectively resist various attacks.

In this paper, we propose a joint waveform selection and power allocation (JWSPA) strategy based on chance-constraint programming (CCP) for manned/unmanned aerial vehicle hybrid swarm (M/UAVHS) tracking a single target. Accordingly, the low probability of intercept (LPI) performance of system can be improved by collaboratively optimizing transmit power and waveform. For target radar cross section (RCS) prediction, we design a random RCS prediction model based on electromagnetic simulation (ES) of target. For waveform selection, we build a waveform library to adaptively manage the frequency modulation slope and pulse width of radar waveform. For power allocation, the CCP is employed to balance tracking accuracy and power resource. The Bayesian Cramér-Rao lower bound (BCRLB) is adopted as a criterion to measure target tracking accuracy. The hybrid intelligent algorithms, in which the stochastic simulation is integrated into the genetic algorithm (GA), are used to solve the stochastic optimization problem. Simulation results demonstrate that the proposed JWSPA strategy can save more transmit power than the traditional fixed waveform scheme under the same target tracking accuracy.

The widespread 5G base stations can be potential jammers for the vulnerable BeiDou B1I receivers due to its low power. Therefore, a novel analytical model is derived for the 5G signal to evaluate its impact on acquisition performance under three decision methods. The good agreement between the Monte Carlo method (MCM) through software defined receiver (SDR) and the derived expressions validates the effectiveness of the proposed algorithm. It can be found that the receivers exhibit varied responses for different 5G waveforms and decision strategies. The receiver also shows the least endurances for some kind of 5G waveforms, however, this kind of adverse effect can be cancelled by a reduced interference signal ratio (ISR), an increased integration time or a larger accumulation times.

This paper concentrates on super-resolution imaging of the ship target under the sparse aperture situation. Firstly, a multi-static configuration is utilized to solve the coherent processing interval (CPI) problem caused by the slow-speed motion of ship targets. Then, we realize signal restoration and image reconstruction with the alternating direction method of multipliers (ADMM). Furthermore, we adopt the interferometric technique to produce the three-dimensional (3D) images of ship targets, namely interferometric inverse synthetic aperture radar (InISAR) imaging. Experiments based on the simulated data are utilized to verify the validity of the proposed method.

Interference suppression is a challenge for radar researchers, especially when mainlobe and sidelobe interference coexist. We present a comprehensive anti-interference approach based on a cognitive bistatic airborne radar. The risk of interception is reduced by lowering the launch energy of the radar transmitting terminal in the direction of interference; mainlobe and sidelobe interferences are suppressed via cooperation between the two radars. The interference received by a single radar is extracted from the overall radar signal using multiple signal classification (MUSIC), and the interference is cross-located using two different azimuthal angles. Neural networks allowing good, non-linear non-parametric approximations are used to predict the location of interference, and this information is then used to preset the transmitting notch antenna to reduce the likelihood of interception. To simultaneously suppress mainlobe and sidelobe interferences, a blocking matrix is used to mask mainlobe interference based on azimuthal information, and an adaptive process is used to suppress sidelobe interference. Mainlobe interference is eliminated using the data received by the two radars. Simulation verifies the performance of the model.

In order to solve the problem that the traditional space jamming countermeasure cannot deal with the mainlobe self-protecting jammings, a polarization-space joint mainlobe jamming countermeasure technique based on divided dimensions is proposed. Specifically, the digital beam of each row and column is firstly formed by using dual polarization digital receiving in multi-channel. Then, the polarization-space joint cancellation in both azimuth and elevation dimensions is carried out based on the polarization-space joint difference between the target echo and the jamming, as well as the divided dimension feature of the row and column beams. Finally, the sum and difference beams of the full array in the elevation or azimuth dimension are formed by the beams after jamming cancelling, and the monopulse angle measurement is further employed to obtain target angles. The effectiveness of the proposed technique is verified by simulations, indicating that the self-protecting jamming and multiple mainlobe following jammings can be both cancelled simultaneously with the angle measurement unchanged.

Operational disposition of electronic countermeasures (ECM) is a hot topic in modern warfare research. Through fully analyzing the characteristics and shortcomings of the traditional operational disposition scheme, a super-efficient data envelopment analysis support vector machine (SE-DEA-SVM) method for evaluating the operational configuration scheme of ECM is proposed. Firstly, considering the subjective and objective factors affecting the operational disposition of ECM, the index system of operational disposition scheme is established, and we explain the solution method of terminal indexs. Secondly, the evaluation and algorithm process of SE-DEA-SVM evaluation method are introduced. In this method, the super-efficient data envelopment analysis (SE-DEA) model is used to calculate the weight of index system, and the support vector machine (SVM) method combined with the training samples of evaluation index is used to obtain the input-output model of evaluation value of combat configuration. Finally, by an example (obtaining five schemes), we verify the SE-DEA-SVM evaluation method and analyze the results. The efficiency analysis, comparison analysis, and error analysis of this method are carried out. The results show that this method is more suitable for military evaluation with small samples, and it has high efficiency, applicability, and popularization value.

This review article aims to give a comprehensive review of periodic orbits in the circular restricted three-body problem (CRTBP), which is a standard ideal model for the Earth-Moon system and is closest to the practical mechanical model. It focuses the attention on periodic orbits in the Earth-Moon system. This work is primarily motivated by a series of missions and plans that take advantages of the three-body periodic orbits near the libration points or around two gravitational celestial bodies. Firstly, simple periodic orbits and their classi?cation that is usually considered to be early work before 1970 are summarized, and periodic orbits around Lagrange points, either planar or three-dimensional, are intensively studied during past decades. Subsequently, stability index of a periodic orbit and bifurcation analysis are presented, which demonstrate a guideline to ?nd more periodic orbits inspired by bifurcation signals. Then, the practical techniques for computing a wide range of periodic orbits and associated quasi-periodic orbits, as well as constructing database of periodic orbits by numerical searching techniques are also presented. For those unstable periodic orbits, the station keeping maneuvers are reviewed. Finally, the applications of periodic orbits are presented, including those in practical missions, under consideration, and still in conceptual design stage. This review article has the function of bridging between engineers and researchers, so as to make it more convenient and faster for engineers to understand the complex restricted three-body problem (RTBP). At the same time, it can also provide some technical thinking for general researchers.

Affinity propagation (AP) is a classic clustering algorithm. To improve the classical AP algorithms, we propose a clustering algorithm namely, adaptive spectral affinity propagation (AdaSAP). In particular, we discuss why AP is not suitable for non-spherical clusters and present a unifying view of nine famous arbitrary-shaped clustering algorithms. We propose a strategy of extending AP in non-spherical clustering by constructing category similarity of objects. Leveraging the monotonicity that the clusters’ number increases with the self-similarity in AP, we propose a model selection procedure that can determine the number of clusters adaptively. For the parameters introduced by extending AP in non-spherical clustering, we provide a grid-evolving strategy to optimize them automatically. The effectiveness of AdaSAP is evaluated by experiments on both synthetic datasets and real-world clustering tasks. Experimental results validate that the superiority of AdaSAP over benchmark algorithms like the classical AP and spectral clustering algorithms.

GM(1,1) models have been widely used in various fields due to their high performance in time series prediction.However, some hypotheses of the existing GM(1,1) model family may reduce their prediction performance in some cases. To solve this problem, this paper proposes a self-adaptive GM(1,1) model, termed as SAGM(1,1) model, which aims to solve the defects of the existing GM (1,1) model family by deleting their modeling hypothesis. Moreover, a novel multi-parameter simultaneous optimization scheme based on firefly algorithm is proposed, the proposed multi-parameter optimization scheme adopts machine learning ideas, takes all adjustable parameters of SAGM(1,1) model as input variables, and trains it with firefly algorithm. And Sobol’ sensitivity indices are applied to study global sensitivity of SAGM(1,1) model parameters, which provides an important reference for model parameter calibration. Finally, forecasting capability of SAGM(1,1) model is illustrated by Anhui electricity consumption dataset. Results show that prediction accuracy of SAGM(1,1) model is significantly better than other models, and it is shown that the proposed approach enhances the prediction performance of GM(1,1) model significantly.

Intelligent wars can take place not only in the physical domain and information domain but also in the cognitive domain. The cognitive domain will become the key domain to win in the future intelligent war. A Lanchester equation considering cognitive domain is proposed to fit the development tendency intelligent wars in this paper. One party is considered to obtain the exponential enhancement advantage on combat forces in combat if it can gain an advantage in the cognitive domain over the other party according to the systemic advantage function. The operational effectiveness of the cognitive domain in war is considered to consist of a series of indicators. Hesitant fuzzy sets and linguistic term sets are powerful tools when evaluating indicators, hence the indicators are scored by experts using hesitant fuzzy linguistic terms sets here. A unique hesitant fuzzy hybrid arithmetical averaging operator is used to aggregate the evaluation.

With the rapid development of data applications in the scene of Industrial Internet of Things (IIoT), how to schedule resources in IIoT environment has become an urgent problem to be solved. Due to benefit of its strong scalability and compatibility, Kubernetes has been applied to resource scheduling in IIoT scenarios. However, the limited types of resources, the default scheduling scoring strategy, and the lack of delay control module limit its resource scheduling performance. To address these problems, this paper proposes a multi-resource scheduling (MRS) scheme of Kubernetes for IIoT. The MRS scheme dynamically balances resource utilization by taking both requirements of tasks and the current system state into consideration. Furthermore, the experiments demonstrate the effectiveness of the MRS scheme in terms of delay control and resource utilization.

Driven by the improvement of the smart grid, the active distribution network (ADN) has attracted much attention due to its characteristic of active management. By making full use of electricity price signals for optimal scheduling, the total cost of the ADN can be reduced. However, the optimal day-ahead scheduling problem is challenging since the future electricity price is unknown. Moreover, in ADN, some schedulable variables are continuous while some schedulable variables are discrete, which increases the difficulty of determining the optimal scheduling scheme. In this paper, the day-ahead scheduling problem of the ADN is formulated as a Markov decision process (MDP) with continuous-discrete hybrid action space. Then, an algorithm based on multi-agent hybrid reinforcement learning (HRL) is proposed to obtain the optimal scheduling scheme. The proposed algorithm adopts the structure of centralized training and decentralized execution, and different methods are applied to determine the selection policy of continuous scheduling variables and discrete scheduling variables. The simulation experiment results demonstrate the effectiveness of the algorithm.

This paper studies the fixed-time output-feedback control for a class of linear systems subject to matched uncertainties. To estimate the uncertainties and system states, we design a composite observer which consists of a high-order sliding mode observer and a Luenberger observer. Then, a robust output-feedback controller with fixed-time convergence guarantee is constructed. Rigorous theoretical proof shows that with the proposed controller, the system states can converge to zero in fixed-time free of the initial conditions. Finally, simulation comparison with existing algorithms is given. Simulation results verify the effectiveness of the proposed controller in terms of its fixed-time convergence and perfect disturbance rejection.

There exist many two-level group consensus problems with different psychological behaviors of decision makers. To deal with these group consensus problems and reach a stable consensus, based on the principles and methods of grey system, utility theory and group consensus, we use grey utility function to describe and reflect decision makers’ opinion preferences in different subgroups and different levels, and then we construct a two-level group consensus method with a moderator, and exploit it to solve the negotiation problems of the natural gas subsidy.

Foot-mounted pedestrian navigation system (PNS) is a common solution to pedestrian navigation using micro-electro mechanical system (MEMS) inertial sensors. The inherent problems of inertial navigation system (INS) by the traditional algorithm, such as the accumulated errors and the lack of observation of heading and altitude information, have become obstacles to the application and development of the PNS. In this paper, we introduce a heuristic heading constraint method. First of all, according to the movement characteristics of human gait, we use the generalized likelihood ratio test (GLRT) detector and introduce a time threshold to classify the human gait, so that we can effectively identify the stationary state of the foot. In addition, based on zero velocity update (ZUPT) and zero angular rate update (ZARU), the cumulative error of the inertial measurement unit (IMU) is limited and corrected, and then a heuristic heading estimation is used to constrain and correct the heading of the pedestrian. After simulation and experiments with low-cost IMU, the method is proved to reduce the localization error of end-point to less than 1% of the total distance, and it has great value in application.

Input variables selection (IVS) is proved to be pivotal in nonlinear dynamic system modeling. In order to optimize the model of the nonlinear dynamic system, a fuzzy modeling method for determining the premise structure by selecting important inputs of the system is studied. Firstly, a simplified two stage fuzzy curves method is proposed, which is employed to sort all possible inputs by their relevance with outputs, select the important input variables of the system and identify the structure. Secondly, in order to reduce the complexity of the model, the standard fuzzy c-means clustering algorithm and the recursive least squares algorithm are used to identify the premise parameters and conclusion parameters, respectively. Then, the effectiveness of IVS is verified by two well-known issues. Finally, the proposed identification method is applied to a realistic variable load pneumatic system. The simulation experiments indicate that the IVS method in this paper has a positive influence on the approximation performance of the Takagi-Sugeno (T-S) fuzzy modeling.

Aiming to evaluate the reliability of phase-transition degrading systems, a generalized stochastic degradation model with phase transition is constructed, and the corresponding analytical reliability function is formulated under the concept of the first hitting time. The phase-varying stochastic property and the phase-varying nonlinearity are considered simultaneously in the proposed model. To capture the phase-varying stochastic property, a Wiener process is adopted to model the non-monotonous degradation phase, while a Gamma process is utilized to model the monotonous one. In addition, the phase-varying nonlinearity is captured by different transformed time scale functions. To facilitate the practical application of the proposed model, identification of phase model type and estimation of model parameters are discussed, and the initial guesses for parameters optimization are also given. Based on the constructed model, two simulation studies are carried out to verify the analytical reliability function and analyze the influence of model misspecification. Finally, a practical case study is conducted for illustration.

In this paper, we focus on the failure analysis of unmanned autonomous swarm (UAS) considering cascading effects. A framework of failure analysis for UAS is proposed. Guided by the framework, the failure analysis of UAS with crash fault agents is performed. Resilience is used to analyze the processes of cascading failure and self-repair of UAS. Through simu-lation studies, we reveal the pivotal relationship between resilience, the swarm size, and the percentage of failed agents. The simulation results show that the swarm size does not affect the cascading failure process but has much influence on the process of self-repair and the final performance of the swarm. The results also reveal a tipping point exists in the swarm. Meanwhile, we get a counter-intuitive result that larger-scale UAS loses more resilience in the case of a small percentage of failed individuals, suggesting that the increasing swarm size does not necessarily lead to high resilience. It is also found that the temporal degree failure strategy performs much more harmfully to the resilience of swarm systems than the random failure. Our work can provide new insights into the mechanisms of swarm collapse, help build more robust UAS, and develop more efficient failure or protection strategies.