For localisation of unknown non-cooperative targets in space, the existence of interference points causes inaccuracy of pose estimation while utilizing point cloud registration. To address this issue, this paper proposes a new iterative closest point (ICP) algorithm combined with distributed weights to intensify the dependability and robustness of the non-cooperative target localisation. As interference points in space have not yet been extensively studied, we classify them into two broad categories, far interference points and near interference points. For the former, the statistical outlier elimination algorithm is employed. For the latter, the Gaussian distributed weights, simultaneously valuing with the variation of the Euclidean distance from each point to the centroid, are commingled to the traditional ICP algorithm. In each iteration, the weight matrix ${\boldsymbol{W}} $ in connection with the overall localisation is obtained, and the singular value decomposition is adopted to accomplish high-precision estimation of the target pose. Finally, the experiments are implemented by shooting the satellite model and setting the position of interference points. The outcomes suggest that the proposed algorithm can effectively suppress interference points and enhance the accuracy of non-cooperative target pose estimation. When the interference point number reaches about 700, the average error of angle is superior to 0.88°.

This work explores an alternative 3D geometry measurement method for non-cooperative spacecraft guiding navigation and proximity operations. From one snapshot of an unfocused light-field camera, the 3D point cloud of a non-cooperative spacecraft can be calculated from sub-aperture images with the epipolar plane image (EPI) based light-field rendering algorithm. A Chang’e?3 model (7.2 cm×5.6 cm×7.0 cm) is tested to validate the proposed technique. Three measurement distances (1.0 m, 1.2 m, 1.5 m) are considered to simulate different approaching stages. Measuring errors are quantified by comparing the light-field camera data with a high precision commercial laser scanner. The mean error distance for the three cases are 0.837 mm, 0.743 mm, and 0.973 mm respectively, indicating that the method can well reconstruct 3D geometry of a non-coope-rative spacecraft with a densely distributed 3D point cloud and is thus promising in space-related missions.

In this paper, a low complexity direction of arrival (DOA) estimation method for massive uniform circular array (UCA) with single snapshot is proposed. Firstly, the coarse DOAs are estimated by finding the peaks from the circular convolution between a fixed coefficient vector and the received data vector. Thereafter, in order to refine coarse DOA estimates, we reconstruct the direction matrix based on the coarse DOA estimations and take the first order Taylor expansion with DOA estimation offsets into account. Finally, the refined estimations are obtained by compensating the offsets, which are obtained via least squares (LS) without any complex searches. In addition, the refinement can be iteratively implemented to enhance the estimation results. Compared to the offset search method, the proposed method achieves a better estimation performance while requiring lower complexity. Numerical simulations are presented to demonstrate the effectiveness of the proposed method.

In a global positioning system (GPS) passive radar, a high resolution requires a high sampling frequency, which increases the computational load. Balancing the computational load and the range resolution is challenging. This paper presents a method to trade off the range resolution and the computational load by experimentally determining the optimal sampling frequency through an analysis of multiple sets of GPS satellite data at different sampling frequencies. The test data are used to construct a range resolution-sampling frequency trade-off model using least-squares estimation. The theoretical analysis shows that the experimental data are the best fit using smoothing and nth-order derivative splines. Using field GPS C/A code signal-based GPS radar, the trade-off between the optimal sampling frequency is determined to be in the 20 461.25–24 553.5 kHz range, which supports a resolution of 43–48 m. Compared with the conventional method, the CPU time is reduced by approximately 50%.

This paper introduces an algorithm for beamforming systems by the aid of multidimensional harmonic retrieval (MHR). This algorithm resolves problems, removes limitations of sampling and provides a more robust beamformer. A new sample space is created that can be used for estimating weights of a new beamforming called spatial-harmonics retrieval beamformer (SHRB). Simulation results show that SHRB has a better performance, accuracy, and applicability and more powerful eigenvalues than conventional beamformers. A simple mathematical proof is provided. By changing the number of harmonics, as a degree of freedom that is missing in conventional beamformers, SHRB can achieve more optimal outputs without increasing the number of spatial or temporal samples. We will demonstrate that SHRB offers an improvement of 4 dB in signal to noise ratio (SNR) in bit error rate (BER) of $ {10}^{-4} $ over conventional beamformers. In the case of direction of arrival (DOA) estimation, SHRB can estimate the DOA of the desired signal with an SNR of ?25 dB, when conventional methods cannot have acceptable response.

Collocated multiple input multiple output (MIMO) radar, which has agile multi-beam working mode, can offer enhanced multiple targets tracking (MTT) ability. In detail, it can illuminate different targets simultaneously with multi-beam or one wide beam among multi-beam, providing greater degree of freedom in system resource control. An adaptive time-space resource and waveform control optimization model for the collocated MIMO radar with simultaneous multi-beam is proposed in this paper. The aim of the proposed scheme is to improve the overall tracking accuracy and meanwhile minimize the resource consumption under the guarantee of effective targets detection. A resource and waveform control algorithm which integrates the genetic algorithm (GA) is proposed to solve the optimization problem. The optimal transmitting waveform parameters, system sampling period, sub-array number, binary radar tracking parameter $ \chi _i\left( {{t_k}} \right) $ , transmitting energy and multi-beam direction vector combination are chosen adaptively, where the first one realizes the waveform control and the latter five realize the time-space resource allocation. Simulation results demonstrate the effectiveness of the proposed control method.

This paper deals with subspace detection for range-spread target in non-homogeneous clutter with unknown covariance matrix where structured interference is presented in the received data. Through exploiting the persymmetry of the clutter covariance matrix, we propose two adaptive target detectors, which are referred to as persymmetric subspace Rao to suppress interference and persymmetric subspace Wald to suppress interference (“PS-Rao-I” and “PS-Wald-I”), respectively. The persymmetry-based design brings in the advantage of easy implementation for small training sample support. The signal flow analysis of the two detectors shows that the PS-Rao-I rejects interference and integrates signals successively through separated matrix projection, while the PS-Wald-I jointly achieves interference elimination and signal combination via oblique projection. In addition, both detectors are shown to be constant false alarm rate detectors, significantly improving the detection performance with other competing detectors under the condition of limited training.

This paper presents a novel method for fast calculation of radar echo in near-field regions after the equivalent source has been computed by method of moments (MoM). An easy-to-access near-field database (NFDB) is established, which is built on the auxiliary tetrahedral meshes surrounding the near-field regions of interest. The near-fields calculation (NFC) of arbitrary observation points can be expressed explicitly via the NFDB. An efficient matrix compression scheme named random sampling-based butterfly factorization (RS-BF) is proposed to speed up the construction of NFDB. With this approach, each group of O(N) elements in the database can be calculated through one fast matrix-vector multiplication operation that has a computational complexity below O(Nlog²N). The proposed method can avoid time-consuming point-by-point NFC of the traditional methods. Several numerical examples are presented to demonstrate the accuracy and efficiency of this method. In particular, the echo simulation of a missile-target encounter exam-ple is presented to illustrate its capability for practical applications.

Monopulse radar is widely used in military. Jamming monopulse radar has always been a research hotspot in electronic warfare (EW). Cross-eye jamming has always been considered as the most effective measures to jam with monopulse radar. In this paper, we propose a multi-group three-tuple cross-eye jamming structure where each group contains three antenna elements with a definite phase and an amplitude relationship. Then, based on the principle of monopulse angle measurement, the error angle is deduced theoretically. Simulations show that such a multi-group three-tuple cross-eye jamming structure performs better than the multi-element cross-eye jamming structure previously proposed, and the analysis of the centroid shows that the centroid of the structure proposed in this paper is more widely distributed in space.

Because of the range-angle dependency in random log frequency diverse array (RD-log-FDA) radar, a method for designing beamspace transformation matrix in angle and range based on the receive signal has been proposed. It is demonstrated that the designed beamspace transformation matrix basically meets the requirements of beam gain. However, there are some problems in the transformation matrix designed, such as unstable beam gain and high sidelobe. Hence, we propose an optimization method by adjusting array element spacing and random number in frequency offset to get the optimum beam gain. Therefore, particle swarm optimization (PSO) is used to find the optimal solution. The beam gain comparison before and after the optimization is obtained by simulation, and the results show that the optimized array after beamspace preprocessing has more stable beam gain, lower sidelobe, and higher resolution in parameter estimation. In conclusion, the RD-log-FDA is capable of forming desired beam gain in angle and distance through beamspace preprocessing, and suppressing interference signals in other areas.

In view of the many scenes of unmanned aerial vehicle (UAV) detection, a third-party signal source is used to design a receiver to monitor the UAV. It is of great significance to understand the reflection of the signal illuminating the UAV. Taking the communication base station (BS) signal as the third-party signal source, and considering the complete transmission link, reflection changes and loss fading of the communication signal, this study conducts model fitting for irregular UAV targets, simplifying complex targets into a combination of simple targets. Furthermore, the influence of the dielectric constant of the target surface and the signal irradiation angle on the signal reflection is analyzed. The analysis shows that the simulation results of this model fitting method are consistent with the results of other literature, which provides theoretical support for the detection of low and slow small targets such as UAVs.

This paper employs system dynamics to explore how the synergy between technology management and technological capability affects new product development. The results show that the synergy between technology management and technological capability has positive impact on new product development. Moreover, the leading synergy processes between technology management and technological capability in different new product development stages are different. This paper deepens the theoretical understanding of how to achieve new product development, and also provides useful guidance for firms to implement new product development.

The hesitant fuzzy set (HFS) is an important tool to deal with uncertain and vague information. In equipment system portfolio selection, the index attribute of the equipment system may not be expressed by precise data; it is usually described by qualitative information and expressed as multiple possible values. We propose a method of equipment system portfolio selection under hesitant fuzzy environment. The hesitant fuzzy element (HFE) is used to describe the index and attribute values of the equipment system. The hesitation degree of HFEs measures the uncertainty of the criterion data of the equipment system. The hesitant fuzzy grey relational analysis (GRA) method is used to evaluate the score of the equipment system, and the improved HFE distance measure is used to fully consider the influence of hesitation degree on the grey correlation degree. Based on the score and hesitation degree of the equipment system, two portfolio selection models of the equipment system and an equipment system portfolio selection case is given to illustrate the application process and effectiveness of the method.

Main lobe jamming seriously affects the detection performance of airborne early warning radar. The joint processing of polarization-space has become an effective way to suppress the main lobe jamming. To avoid the main beam distortion and wave crest migration caused by the main lobe jamming in adaptive beamforming, a joint optimization algorithm based on adaptive polarization canceller (APC) and stochastic variance reduction gradient descent (SVRGD) is proposed. First, the polarization plane array structure and receiving signal model based on primary and auxiliary array cancellation are established, and an APC iterative algorithm model is constructed to calculate the optimal weight vector of the auxiliary channel. Second, based on the stochastic gradient descent principle, the variance reduction method is introduced to modify the gradient through internal and external iteration to reduce the variance of the stochastic gradient estimation, the airspace optimal weight vector is calculated and the equivalent weight vector is introduced to measure the beamforming effect. Third, by setting up a planar polarization array simulation scene, the performance of the algorithm against the interference of the main lobe and the side lobe is analyzed, and the effectiveness of the algorithm is verified under the condition of short snapshot number and certain signal to interference plus noise ratio.

This paper studies the optimal policy for joint control of admission, routing, service, and jockeying in a queueing system consisting of two exponential servers in parallel. Jobs arrive according to a Poisson process. Upon each arrival, an admission/routing decision is made, and the accepted job is routed to one of the two servers with each being associated with a queue. After each service completion, the servers have an option of serving a job from its own queue, serving a jockeying job from another queue, or staying idle. The system performance is inclusive of the revenues from accepted jobs, the costs of holding jobs in queues, the service costs and the job jockeying costs. To maximize the total expected discounted return, we formulate a Markov decision process (MDP) model for this system. The value iteration method is employed to characterize the optimal policy as a hedging point policy. Numerical studies verify the structure of the hedging point policy which is convenient for implementing control actions in practice.

The two-archive 2 algorithm (Two_Arch2) is a many-objective evolutionary algorithm for balancing the convergence, diversity, and complexity using diversity archive (DA) and convergence archive (CA). However, the individuals in DA are selected based on the traditional Pareto dominance which decreases the selection pressure in the high-dimensional problems. The traditional algorithm even cannot converge due to the weak selection pressure. Meanwhile, Two_Arch2 adopts DA as the output of the algorithm which is hard to maintain diversity and coverage of the final solutions synchronously and increase the complexity of the algorithm. To increase the evolutionary pressure of the algorithm and improve distribution and convergence of the final solutions, an ${\textit{ε}} $-domination based Two_Arch2 algorithm (${\textit{ε}} $-Two_Arch2) for many-objective problems (MaOPs) is proposed in this paper. In ${\textit{ε}} $-Two_Arch2, to decrease the computational complexity and speed up the convergence, a novel evolutionary framework with a fast update strategy is proposed; to increase the selection pressure, ${\textit{ε}} $-domination is assigned to update the individuals in DA; to guarantee the uniform distribution of the solution, a boundary protection strategy based on $ {\mathit{I}}_{\mathit{{\textit{ε}} }+} $ indicator is designated as two steps selection strategies to update individuals in CA. To evaluate the performance of the proposed algorithm, a series of benchmark functions with different numbers of objectives is solved. The results demonstrate that the proposed method is competitive with the state-of-the-art multi-objective evolutionary algorithms and the efficiency of the algorithm is significantly improved compared with Two_Arch2.

This paper proposes a liner active disturbance rejection control (LADRC) method based on the Q-Learning algorithm of reinforcement learning (RL) to control the six-degree-of-freedom motion of an autonomous underwater vehicle (AUV). The number of controllers is increased to realize AUV motion decoupling. At the same time, in order to avoid the oversize of the algorithm, combined with the controlled content, a simplified Q-learning algorithm is constructed to realize the parameter adaptation of the LADRC controller. Finally, through the simulation experiment of the controller with fixed parameters and the controller based on the Q-learning algorithm, the rationality of the simplified algorithm, the effectiveness of parameter adaptation, and the unique advantages of the LADRC controller are verified.

For the problem of sensor faults and actuator faults in aircraft attitude control, this paper proposes a fault tolerant control (FTC) scheme based on extended state observer (ESO) and nonlinear dynamic inversion (NDI). First, two ESOs are designed to estimate sensor faults and actuator faults respectively. Second, the angular rate signal is reconstructed according to the estimation of sensor faults. Third, in angular rate loop, NDI is designed based on reconstruction of angular rate signals and estimation of actuator faults. The FTC scheme proposed in this paper is testified through numerical simulations. The results show that it is feasible and has good fault tolerant ability.

In this paper, a model reference adaptive control (MRAC) augmentation method of a linear controller is proposed for air-breathing hypersonic vehicle (AHV) during inlet unstart. With the development of hypersonic flight technology, hypersonic vehicles have been gradually moving to the stage of weaponization. During the maneuvers, changes of attitude, Mach number and the back pressure can cause the inlet unstart phenomenon of scramjet. Inlet unstart causes significant changes in the aerodynamics of AHV, which may lead to deterioration of the tracking performance or instability of the control system. Therefore, we firstly establish the model of hypersonic vehicle considering inlet unstart, in which the changes of aerodynamics caused by inlet unstart is described as nonlinear uncertainty. Then, an MRAC augmentation method of a linear controller is proposed and the radial basis function (RBF) neural network is used to schedule the adaptive parameters of MRAC. Furthermore, the Lyapunov function is constructed to prove the stability of the proposed method. Finally, numerical simulations show that compared with the linear control method, the proposed method can stabilize the attitude of the hypersonic vehicle more quickly after the inlet unstart, which provides favorable conditions for inlet restart, thus verifying the effectiveness of the augmentation method proposed in the paper.

The cooperative interception trajectories generation of multiple interceptors to hypersonic targets is studied. First, to solve the problem of on-line trajectory generation of the single interceptor, a generation method based on neighborhood optimal control is adopted. Then, when intercepting the strong maneuvering targets, the single interceptor is insufficient in maneuverability, therefore, an on-line multiple trajectories generation algorithm is proposed, which uses the multiple interceptors intercept area (IIA) to cover the target’s predicted intercept area (PIA) cooperatively. Through optimizing the interceptors’ zero control terminal location, the trajectories are generated on-line by using the neighborhood optimal control method, these trajectories could make the IIA maximally cover the PIA. The simulation results show that the proposed method can greatly improve the interception probability, which provides a reference for the collaborative interception of multiple interceptors.

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.

Recently, the physics-of-failure (PoF) method has been more and more popular in engineering to understand the failure mechanisms (FMs) of products. However, due to the lack of system modeling methods and problem-solving algorithms, the information of FMs cannot be used to evaluate system reliability. This paper presents a system reliability evaluation method with failure mechanism tree (FMT) considering physical dependency (PDEP) such as competition, trigger, acceleration, inhibition, damage accumulation, and parameter combination. And the binary decision diagram (BDD) analytical algorithm is developed to establish a system reliability model. The operation rules of ite operators for generating BDD are discussed. The flow chart of system reliability evaluation method based on FMT and BDD is proposed. The proposed method is applied in the case of an electronic controller drive unit. Results show that the method is effective to evaluate system reliability from the perspective of FM.

Rolling element bearings are machine components used to allow circular movement and hence deliver forces between components of machines used in diverse areas of industry. The likelihood of failure has the propensity of increasing under prolonged operation and varying working conditions. Hence, the accurate fault severity categorization of bearings is vital in diagnosing faults that arise in rotating machinery. The variability and complexity of the recorded vibration signals pose a great hurdle to distinguishing unique characteristic fault features. In this paper, the efficacy and the leverage of a pre-trained convolutional neural network (CNN) is harnessed in the implementation of a robust fault classification model. In the absence of sufficient data, this method has a high-performance rate. Initially, a modified VGG16 architecture is used to extract discriminating features from new samples and serves as input to a classifier. The raw vibration data are strategically segmented and transformed into two representations which are trained separately and jointly. The proposed approach is carried out on bearing vibration data and shows high-performance results. In addition to successfully implementing a robust fault classification model, a prognostic framework is developed by constructing a health indicator (HI) under varying operating conditions for a given fault condition.