Global Navigation Satellite System (GNSS)-based passive radar (GBPR) has been widely used in remote sensing applications. However, for moving target detection (MTD), the quadratic phase error (QPE) introduced by the non-cooperative target motion is usually difficult to be compensated, as the low power level of the GBPR echo signal renders the estimation of the Doppler rate less effective. Consequently, the moving target in GBPR image is usually defocused, which aggravates the difficulty of target detection even further. In this paper, a spawning particle filter (SPF) is proposed for defocused MTD. Firstly, the measurement model and the likelihood ratio function (LRF) of the defocused point-like target image are deduced. Then, a spawning particle set is generated for subsequent target detection, with reference to traditional particles in particle filter (PF) as their parent. After that, based on the PF estimator, the SPF algorithm and its sequential Monte Carlo (SMC) implementation are proposed with a novel amplitude estimation method to decrease the target state dimension. Finally, the effectiveness of the proposed SPF is demonstrated by numerical simulations and preliminary experimental results, showing that the target range and Doppler can be estimated accurately.

This paper proposes an optimal deployment method of heterogeneous multistatic radars to construct arc barrier coverage with location restrictions. This method analyzes and proves the properties of different deployment patterns in the optimal deployment sequence. Based on these properties and considering location restrictions, it introduces an optimization model of arc barrier coverage and aims to minimize the total deployment cost of heterogeneous multistatic radars. To overcome the non-convexity of the model and the non-analytical nature of the objective function, an algorithm combining integer line programming and the cuckoo search algorithm (CSA) is proposed. The proposed algorithm can determine the number of receivers and transmitters in each optimal deployment squence to minimize the total placement cost. Simulations are conducted in different conditions to verify the effectiveness of the proposed method.

Long-time coherent integration (LTCI) is an effective way for radar maneuvering target detection, but it faces the problem of a large number of search parameters and large amount of calculation. Realizing the simultaneous compensation of the range and Doppler migrations in complex clutter background, and at the same time improving the calculation efficiency has become an urgent problem to be solved. The sparse transformation theory is introduced to LTCI in this paper, and a non-parametric searching sparse LTCI (SLTCI) based maneuvering target detection method is proposed. This method performs time reversal (TR) and second-order Keystone transform (SKT) in the range frequency & slow-time data to complete high-order range walk compensation, and achieves the coherent integration of maneuvering target across range and Doppler units via the robust sparse fractional Fourier transform (RSFRFT). It can compensate for the nonlinear range migration caused by high-order motion. S-band and X-band radar data measured in sea clutter background are used to verify the detection performance of the proposed method, which can achieve better detection performance of maneuvering targets with less computational burden compared with several popular integration methods.

In the scene of wideband radar, due to the spread of target scattering points, the attitude and angle of view of the target constantly change in the process of moving. It is difficult to predict, and the actual echo of multiple scattered points is not fully matched with the transmitted signal. Therefore, it is challenging for the traditional matching filter method to achieve a complete matching effect in wideband echo detection. In addition, the energy dispersion of complex target echoes is still a problem in radar target detection under broadband conditions. Therefore, this paper proposes a wideband target detection method based on dual-channel correlation processing of range-extended targets. This method fully uses the spatial distribution characteristics of target scattering points of echo signal and the matching characteristics of the dual-channel point extension function itself. The radial accumulation of wideband target echo signal in the complex domain is realized through the adaptive correlation processing of a dual-channel echo signal. The accumulation effect of high matching degree is achieved to improve the detection probability and the performance of wideband detection. Finally, electromagnetic simulation experiments and measured data verify that the proposed method has the advantages of high signal to noise ratio (SNR) gain and high detection probability under low SNR conditions.

Considering the problem that the scattering echo images of airborne Doppler weather radar are often reduced by ground clutters, the accuracy and confidence of meteorology target detection are reduced. In this paper, a deep convolutional neural network (DCNN) is proposed for meteorology target detection and ground clutter suppression with a large collection of airborne weather radar images as network input. For each weather radar image, the corresponding digital elevation model (DEM) image is extracted on basis of the radar antenna scanning parameters and plane position, and is further fed to the network as a supplement for ground clutter suppression. The features of actual meteorology targets are learned in each bottleneck module of the proposed network and convolved into deeper iterations in the forward propagation process. Then the network parameters are updated by the back propagation iteration of the training error. Experimental results on the real measured images show that our proposed DCNN outperforms the counterparts in terms of six evaluation factors. Meanwhile, the network outputs are in good agreement with the expected meteorology detection results (labels). It is demonstrated that the proposed network would have a promising meteorology observation application with minimal effort on network variables or parameter changes.

The angular resolution of radar is of crucial significance to its tracking performance. In this paper, a super-resolution parameter estimation algorithm based on wide-narrowband joint processing is proposed to improve the angular resolution of wideband monopulse radar. The range cells containing resolvable scattering points are detected in the wideband mode, and these range cells are adopted to estimate part of the target parameters by algorithms of low computational requirement. Then, the likelihood function of the echo is constructed in the narrow-band mode to estimate the rest of the parameters, and the parameters estimated in the wideband mode are employed to reduce computation and enhance estimation accuracy. Simulation results demonstrate that the proposed algorithm has higher estimation accuracy and lower computational complexity than the current algorithm and can avoid the risk of model mismatch.

This paper considers the non-line-of-sight (NLOS) vehicle localization problem by using millimeter-wave (MMW) automotive radar. Several preliminary attempts for NLOS vehicle detection are carried out and achieve good results. Firstly, an electromagnetic (EM) wave NLOS multipath propagation model for vehicle scene is established. Subsequently, with the help of available multipath echoes, a complete NLOS vehicle localization algorithm is proposed. Finally, simulation and experimental results validate the effectiveness of the established EM wave propagation model and the proposed NLOS vehicle localization algorithm.

Rare labeled data are difficult to recognize by using conventional methods in the process of radar emitter recognition. To solve this problem, an optimized cooperative semi-supervised learning radar emitter recognition method based on a small amount of labeled data is developed. First, a small amount of labeled data are randomly sampled by using the bootstrap method, loss functions for three common deep learning networks are improved, the uniform distribution and cross-entropy function are combined to reduce the overconfidence of softmax classification. Subsequently, the dataset obtained after sampling is adopted to train three improved networks so as to build the initial model. In addition, the unlabeled data are preliminarily screened through dynamic time warping (DTW) and then input into the initial model trained previously for judgment. If the judgment results of two or more networks are consistent, the unlabeled data are labeled and put into the labeled data set. Lastly, the three network models are input into the labeled dataset for training, and the final model is built. As revealed by the simulation results, the semi-supervised learning method adopted in this paper is capable of exploiting a small amount of labeled data and basically achieving the accuracy of labeled data recognition.

Aiming at the suppression of enemy air defense (SEAD) task under the complex and complicated combat scenario, the spatiotemporal cooperative path planning methods are studied in this paper. The major research contents include optimal path points generation, path smoothing and cooperative rendezvous. In the path points generation part, the path points availability testing algorithm and the path segments availability testing algorithm are designed, on this foundation, the swarm intelligence-based path point generation algorithm is utilized to generate the optimal path. In the path smoothing part, taking terminal attack angle constraint and maneuverability constraint into consideration, the Dubins curve is introduced to smooth the path segments. In cooperative rendezvous part, we take estimated time of arrival requirement constraint and flight speed range constraint into consideration, the speed control strategy and flight path control strategy are introduced, further, the decoupling scheme of the circling maneuver and detouring maneuver is designed, in this case, the maneuver ways, maneuver point, maneuver times, maneuver path and flight speed are determined. Finally, the simulation experiments are conducted and the acquired results reveal that the time-space cooperation of multiple unmanned aeriel vehicles (UAVs) is effectively realized, in this way, the combat situation suppression against the enemy can be realized in SEAD scenarios.

The deep deterministic policy gradient (DDPG) algorithm is an off-policy method that combines two mainstream reinforcement learning methods based on value iteration and policy iteration. Using the DDPG algorithm, agents can explore and summarize the environment to achieve autonomous decisions in the continuous state space and action space. In this paper, a cooperative defense with DDPG via swarms of unmanned aerial vehicle (UAV) is developed and validated, which has shown promising practical value in the effect of defending. We solve the sparse rewards problem of reinforcement learning pair in a long-term task by building the reward function of UAV swarms and optimizing the learning process of artificial neural network based on the DDPG algorithm to reduce the vibration in the learning process. The experimental results show that the DDPG algorithm can guide the UAVs swarm to perform the defense task efficiently, meeting the requirements of a UAV swarm for non-centralization, autonomy, and promoting the intelligent development of UAVs swarm as well as the decision-making process.

Strategic management of equipment system development must attach importance to effective strategic risk management. Aiming at the identification of strategic risk of equipment system development, firstly, the source of strategic risk of equipment system development is analyzed and classified. Based on this, a causal loop diagram of strategic risk of equipment system development based on system dynamics is established. The system dynamics analysis software Vensim PLE is used to carry out the risk influencing factors analysis, risk consequences analysis, risk feedback loop identification and corresponding pre-control measures, and achieves a good risk identification effect.

The unmanned combat aerial vehicle (UCAV) is a research hot issue in the world, and the situation assessment is an important part of it. To overcome shortcomings of the existing situation assessment methods, such as low accuracy and strong dependence on prior knowledge, a data-driven situation assessment method is proposed. The clustering and classification are combined, the former is used to mine situational knowledge, and the latter is used to realize rapid assessment. Angle evaluation factor and distance evaluation factor are proposed to transform multi-dimensional air combat information into two-dimensional features. A convolution success-history based adaptive differential evolution with linear population size reduction-means (C-LSHADE-Means) algorithm is proposed. The convolutional pooling layer is used to compress the size of data and preserve the distribution characteristics. The LSHADE algorithm is used to initialize the center of the mean clustering, which overcomes the defect of initialization sensitivity. Comparing experiment with the seven clustering algorithms is done on the UCI data set, through four clustering indexes, and it proves that the method proposed in this paper has better clustering performance. A situation assessment model based on stacked autoencoder and learning vector quantization (SAE-LVQ) network is constructed, and it uses SAE to reconstruct air combat data features, and uses the self-competition layer of the LVQ to achieve efficient classification. Compared with the five kinds of assessments models, the SAE-LVQ model has the highest accuracy. Finally, three kinds of confrontation processes from air combat maneuvering instrumentation (ACMI) are selected, and the model in this paper is used for situation assessment. The assessment results are in line with the actual situation.

The key advantage of unmanned swarm operation is its autonomous cooperation. How to improve the proportion of cooperators is one of the key issues of autonomous collaboration in unmanned swarm operations. This work proposes a strategy dominance mechanism of autonomous collaboration in unmanned swarm within the framework of public goods game. It starts with the requirement analysis of autonomous collaboration in unmanned swarm; and an aspiration-driven multiplayer evolutionary game model is established based on the requirement. Then the average abundance function and strategy dominance condition of the model are constructed by theoretical derivation. Furthermore, the evolutionary mechanism of parameter adjustment in swarm cooperation is revealed via simulation, and the influences of the multiplication factor $ r $ , aspiration level $ \alpha $ , threshold $ m $ and other parameters on the strategy dominance conditions were simulated for both linear and threshold public goods games (PGGs) to determine the strategy dominance characteristics; Finally, deliberate proposals are suggested to provide a meaningful exploration in the actual control of unmanned swarm cooperation.

Space emergency launching is to send a satellite into space by using a rapid responsive solid rocket in the bounded time to implement the emergency Earth observation mission. The key and difficult points mainly include the business process construction of launching mission planning, validation of the effectiveness of the launching scheme, etc. This paper proposes the agile space emergency launching mission planning simulation and verification method, which systematically constructs the overall technical framework of space emergency launching mission planning with multi-field area, multi-platform and multi-task parallel under the constraint of resource scheduling for the first time. It supports flexible reconstruction of mission planning processes such as launching target planning, trajectory planning, path planning, action planning and launching time analysis, and can realize on-demand assembly of operation links under different mission scenarios and different plan conditions, so as to quickly modify and generate launching schemes. It supports the fast solution of rocket trajectory data and the accurate analysis of multi-point salvo time window recheck and can realize the fast conflict resolution of launching missions in the dimensions of launching position and launching window sequence. It supports lightweight scenario design, modular flexible simulation, based on launching style, launching platform, launching rules, etc., can realize the independent mapping of mission planning results to two-dimensional and three-dimensional visual simulation models, so as to achieve a smooth connection between mission planning and simulation.

Satellites with altitudes below 400 km are called super low altitude satellites (SLAS), often used to achieve responsive imaging tasks. Therefore, it is important for the manipulation of its ground track. Aiming at the problem of ground track manipulation of SLAS, a control method based on tangential impulse thrust is proposed. First, the equation of the longitude difference between SLAS and the target point on the target latitude is derived based on Gauss’s variational equations. On this basis, the influence of the tangential impulse thrust on the ground track’s longitude is derived. Finally, the method for ground track manipulation of SLAS under the tangential impulse thrust is proposed. The simulation results verify the effectiveness of the method, after manipulation, the satellite can visit the target point and revisit it for multiple days.

To ensure safe flight of multiple fixed-wing unmanned aerial vehicles (UAVs) formation, considering trajectory planning and formation control together, a leader trajectory planning method based on the sparse A* algorithm is introduced. Firstly, a formation controller based on prescribed performance theory is designed to control the transient and steady formation configuration, as well as the formation forming time, which not only can form the designated formation configuration but also can guarantee collision avoidance and terrain avoidance theoretically. Next, considering the constraints caused by formation controller on trajectory planning such as the safe distance, turn angle and step length, as well as the constraint of formation shape, a leader trajectory planning method based on sparse A* algorithm is proposed. Simulation results show that the UAV formation can arrive at the destination safely with a short trajectory no matter keeping the formation or encountering formation transformation.

In this paper, we study scene image recognition with knowledge transfer for drone navigation. We divide navigation scenes into three macro-classes, namely outdoor special scenes (OSSs), the space from indoors to outdoors or from outdoors to indoors transitional scenes (TSs), and others. However, there are difficulties in how to recognize the TSs, to this end, we employ deep convolutional neural network (CNN) based on knowledge transfer, techniques for image augmentation, and fine tuning to solve the issue. Moreover, there is still a novelty detection problem in the classifier, and we use global navigation satellite systems (GNSS) to solve it in the prediction stage. Experiment results show our method, with a pre-trained model and fine tuning, can achieve 91.3196% top-1 accuracy on Scenes21 dataset, paving the way for drones to learn to understand the scenes around them autonomously.

For the underwater long baseline (LBL) positioning systems, the traditional distance intersection algorithm simplifies the sound speed to a constant, and calculates the underwater target position parameters with a nonlinear iteration. However, due to the complex underwater environment, the sound speed changes with time and space, and then the acoustic propagation path is actually a curve, which inevitably causes some errors to the traditional distance intersection positioning algorithm. To reduce the position error caused by the uncertain underwater sound speed, a new time of arrival (TOA) intersection underwater positioning algorithm of LBL system is proposed. Firstly, combined with the vertical layered model of the underwater sound speed, an implicit positioning model of TOA intersection is constructed through the constant gradient acoustic ray tracing. And then an optimization function based on the overall TOA residual square sum is advanced to solve the position parameters for the underwater target. Moreover, the particle swarm optimization (PSO) algorithm is replaced with the traditional nonlinear least square method to optimize the implicit positioning model of TOA intersection. Compared with the traditional distance intersection positioning model, the TOA intersection positioning model is more suitable for the engineering practice and the optimization algorithm is more effective. Simulation results show that the proposed methods in this paper can effectively improve the positioning accuracy for the underwater target.

The performance of a strapdown inertial navigation system (SINS) largely depends on the accuracy and rapidness of the initial alignment. A novel anti-interference self-alignment algorithm by attitude optimization estimation for SINS on a rocking base is presented in this paper. The algorithm transforms the initial alignment into the initial attitude determination problem by using infinite vector observations to remove the angular motions, the SINS alignment is heuristically established as an optimization problem of finding the minimum eigenvector. In order to further improve the alignment precision, an adaptive recursive weighted least squares (ARWLS) curve fitting algorithm is used to fit the translational motion interference-contaminated reference vectors according to their time domain characteristics. Simulation studies and experimental results favorably demonstrate its rapidness, accuracy and robustness.

To solve the path following control problem for unmanned surface vehicles (USVs), a control method based on deep reinforcement learning (DRL) with long short-term memory (LSTM) networks is proposed. A distributed proximal policy optimization (DPPO) algorithm, which is a modified actor-critic-based type of reinforcement learning algorithm, is adapted to improve the controller performance in repeated trials. The LSTM network structure is introduced to solve the strong temporal correlation USV control problem. In addition, a specially designed path dataset, including straight and curved paths, is established to simulate various sailing scenarios so that the reinforcement learning controller can obtain as much handling experience as possible. Extensive numerical simulation results demonstrate that the proposed method has better control performance under missions involving complex maneuvers than trained with limited scenarios and can potentially be applied in practice.

Effective bearing fault diagnosis is vital for the safe and reliable operation of rotating machinery. In practical applications, bearings often work at various rotational speeds as well as load conditions. Yet, the bearing fault diagnosis under multiple conditions is a new subject, which needs to be further explored. Therefore, a multi-scale deep belief network (DBN) method integrated with attention mechanism is proposed for the purpose of extracting the multi-scale core features from vibration signals, containing four primary steps: preprocessing of multi-scale data, feature extraction, feature fusion, and fault classification. The key novelties include multi-scale feature extraction using multi-scale DBN algorithm, and feature fusion using attention mechanism. The benchmark dataset from University of Ottawa is applied to validate the effectiveness as well as advantages of this method. Furthermore, the aforementioned method is compared with four classical fault diagnosis methods reported in the literature, and the comparison results show that our proposed method has higher diagnostic accuracy and better robustness.

The dynamic wireless communication network is a complex network that needs to consider various influence factors including communication devices, radio propagation, network topology, and dynamic behaviors. Existing works focus on suggesting simplified reliability analysis methods for these dynamic networks. As one of the most popular modeling methodologies, the dynamic Bayesian network (DBN) is proposed. However, it is insufficient for the wireless communication network which contains temporal and non-temporal events. To this end, we present a modeling methodology for a generalized continuous time Bayesian network (CTBN) with a 2-state conditional probability table (CPT). Moreover, a comprehensive reliability analysis method for communication devices and radio propagation is suggested. The proposed methodology is verified by a reliability analysis of a real wireless communication network.