LFM Radar Source Passive Localization Algorithm Based on Range Migration

Traditional single-satellite passive localization algorithms are influenced by frequency and angle measurement accuracies,resulting in error estimation of emitter position on the order of kilometers.Subsequently,a single-satellite localization algorithm based on passive synthetic aper-ture(PSA)was introduced,enabling high-precision positioning.However,its estimation of azimuth and range distance is considerably affected by the residual frequency offset(RFO)of uncoopera-tive system transceivers.Furthermore,it requires data containing a satellite flying over the radia-tion source for RFO search.After estimating the RFO,an accurate estimation of azimuth and range distance can be carried out,which is difficult to achieve in practical situations.An LFM radar source passive localization algorithm based on range migration is proposed to address the dif-ficulty in estimating frequency offset.The algorithm first provides a rough estimate of the pulse repetition time(PRT).It processes intercepted signals through range compression,range interpola-tion,and polynomial fitting to obtain range migration observations.Subsequently,it uses the changing information of range migration and an accurate PRT to formulate a system of nonlinear equations,obtaining the emitter position and a more accurate PRT through a two-step localization algorithm.Frequency offset only induces a fixed offset in range migration,which does not affect the changing information.This algorithm can also achieve high-precision localization in squint scenar-ios.Finally,the effectiveness of this algorithm is verified through simulations.

A Novel Belief Rule-Based Fault Diagnosis Method with Interpretability

Fault diagnosis plays an irreplaceable role in the normal operation of equipment.A fault diagnosis model is often required to be interpretable for increasing the trust between humans and the model.Due to the understandable knowledge expression and transparent reasoning process,the belief rule base(BRB)has extensive applications as an interpretable expert system in fault diagnosis.Optimization is an effective means to weaken the subjectivity of experts in BRB,where the interpretability of BRB may be weakened.Hence,to obtain a credible result,the weakening factors of interpretability in the BRB-based fault diagnosis model are firstly analyzed,which are manifested in deviation from the initial judgement of experts and over-optimization of parameters.For these two factors,three indexes are proposed,namely the consistency index of rules,consistency index of the rule base and over-optimization index,tomeasure the interpretability of the optimizedmodel.Considering both the accuracy and interpretability of amodel,an improved coordinate ascent(I-CA)algorithmis proposed to fine-tune the parameters of the fault diagnosis model based on BRB.In I-CA,the algorithm combined with the advance and retreat method and the golden section method is employed to be one-dimensional search algorithm.Furthermore,the random optimization sequence and adaptive step size are proposed to improve the accuracy of the model.Finally,a case study of fault diagnosis in aerospace relays based on BRB is carried out to verify the effectiveness of the proposed method.

Robust Consensus Tracking Control of Uncertain Multi-Agent Systems With Local Disturbance Rejection

In this paper,a new distributed consensus tracking protocol incorporating local disturbance rejection is devised for a multi-agent system with heterogeneous dynamic uncertainties and disturbances over a directed graph.It is of two-degree-of-freedom nature.Specifically,a robust distributed controller is designed for consensus tracking,while a local disturbance estimator is designed for each agent without requiring the input channel information of disturbances.The condition for asymptotic disturbance rejection is derived.Moreover,even when the disturbance model is not exactly known,the developed method also provides good disturbance-rejection performance.Then,a robust stabilization condition with less conservativeness is derived for the whole multi-agent system.Further,a design algorithm is given.Finally,comparisons with the conventional one-degree-of-freedombased distributed disturbance-rejection method for mismatched disturbances and the distributed extended-state observer for matched disturbances validate the developed method.

Safety Assessment of Liquid Launch Vehicle Structures Based on Interpretable Belief Rule Base

A liquid launch vehicle is an important carrier in aviation,and its regular operation is essential to maintain space security.In the safety assessment of fluid launch vehicle body structure,it is necessary to ensure that the assessmentmodel can learn self-response rules from various uncertain data and not differently to provide a traceable and interpretable assessment process.Therefore,a belief rule base with interpretability(BRB-i)assessment method of liquid launch vehicle structure safety status combines data and knowledge.Moreover,an innovative whale optimization algorithm with interpretable constraints is proposed.The experiments are carried out based on the liquid launch vehicle safety experiment platform,and the information on the safety status of the liquid launch vehicle is obtained by monitoring the detection indicators under the simulation platform.The MSEs of the proposed model are 3.8000e-03,1.3000e-03,2.1000e-03,and 1.8936e-04 for 25%,45%,65%,and 84%of the training samples,respectively.It can be seen that the proposed model also shows a better ability to handle small sample data.Meanwhile,the belief distribution of the BRB-i model output has a high fitting trend with the belief distribution of the expert knowledge settings,which indicates the interpretability of the BRB-i model.Experimental results show that,compared with other methods,the BRB-i model guarantees the model’s interpretability and the high precision of experimental results.

A WSN Node Fault Diagnosis Model Based on BRB with Self-Adaptive Quality Factor

Wireless sensor networks (WSNs) operate in complex and harshenvironments;thus, node faults are inevitable. Therefore, fault diagnosis ofthe WSNs node is essential. Affected by the harsh working environment ofWSNs and wireless data transmission, the data collected by WSNs containnoisy data, leading to unreliable data among the data features extracted duringfault diagnosis. To reduce the influence of unreliable data features on faultdiagnosis accuracy, this paper proposes a belief rule base (BRB) with a selfadaptivequality factor (BRB-SAQF) fault diagnosis model. First, the datafeatures required for WSN node fault diagnosis are extracted. Second, thequality factors of input attributes are introduced and calculated. Third, themodel inference process with an attribute quality factor is designed. Fourth,the projection covariance matrix adaptation evolution strategy (P-CMA-ES)algorithm is used to optimize the model’s initial parameters. Finally, the effectivenessof the proposed model is verified by comparing the commonly usedfault diagnosis methods for WSN nodes with the BRB method consideringstatic attribute reliability (BRB-Sr). The experimental results show that BRBSAQFcan reduce the influence of unreliable data features. The self-adaptivequality factor calculation method is more reasonable and accurate than thestatic attribute reliability method.

运载火箭软件系统安全性分析

At present,the number,scale and complexity of launch vehicle software systems have increased dramatically.Software is no longer a simple stack of independent configuration items,but a tightly integrated complex distributed software system.How to improve the safety of complex launch vehicle software systems has become a new topic under launch vehicle development.This paper introduces in detail the contents and processes of software system safety analysis based on use case of the Long March 5B launch vehicle with innovations including analysis methods for key sequences,key events,failure modes and countermeasures.

Control System Design for an Unmanned Helicopter to Track a Ground Target

Due to potential wide applications,the problem of utilizing an unmanned helicopter to track a ground target has become one of the most active research directions in related areas.However,in most cases,it is possible for a dynamic target to implement evasive actions with strong maneuverability,such as a sudden turn during high-speed movement,to flee from the tracker,which then brings much difficulty for the design of tracking control systems.Currently,most research on this field focuses on utilizing a ground mobile robot to track a high-speed target.Unfortunately,it is very difficult to extend those developed methods to airborne applications due to much more complex dynamices of UAV-target relative motion.This study investiages thoroughly for the problem of using an unmanned helicopter to track a ground target,with particular emphasis on the avoidance of tracking failure caused by the evasive maneuvers of dynamic targets.Specifically,a novel control scheme,which consists of an innovative target tracking controller and a classical flight controller,is proposed for the helicopter-target tracking problem.Wherein,the tracking controller,whose design is the focus of the paper,aims to utilize the motion information of the helicopter and the dynamic target to construct a suitable trajectory for the helicopter,so that when it flies along this trajectory,the relative pose between the helicopter and the dynamic target will be kept consant.When designing the target tracking controller,a novel coordinate transformation is firstly introduced to convert the tracking system into a more compact form convenient for control law design,the desired velocities for the helicopter is then proposed with consideration of the dynamic constraint.The stability of the closed-loop system is finally analyzed by Lyapunov techniques.Based on Matlab/Simulink environment,two groups of simulation are conducted for the helicopter-target tracking control system where the target moves along a linear path and takes a sudden turn during high-speed movement,respectively.As shown by the simulation results,both the distance error and the pointing error are bounded during the tracking process,and they are convergent to zero when the target moves straightly.Moreover,the tracking performance can be adjusted properly to avoid tracking failure due to evasive maneuvers of the target,so as to guarantee superior tracking performance for all kinds of dynamic targets.

Multiconstraint adaptive three-dimensional guidance law using convex optimization

The traditional guidance law only guarantees the accuracy of attacking a target. However, the look angle and acceleration constraints are indispensable in applications. A new adaptive three-dimensional proportional navigation(PN) guidance law is proposed based on convex optimization. Decomposition of the three-dimensional space is carried out to establish threedimensional kinematic engagements. The constraints and the performance index are disposed by using the convex optimization method. PN guidance gains can be obtained by solving the optimization problem. This solution is more rapid and programmatic than the traditional method and provides a foundation for future online guidance methods, which is of great value for engineering applications.

Control allocation for a class of morphing aircraft with integer constraints based on Lévy flight

Aiming at tracking control of a class of innovative control effector(ICE) aircraft with distributed arrays of actuators, this paper proposes a control allocation scheme based on the Lévy flight.Different from the conventional aircraft control allocation problem,the particular characteristic of actuators makes the actuator control command totally subject to integer constraints. In order to tackle this problem, first, the control allocation problem is described as an integer programming problem with two desired objectives. Then considering the requirement of real-time, a metaheuristic algorithm based on the Lévy flight is introduced to tackling this problem. In order to improve the searching efficiency, several targeted and heuristic strategies including variable step length and inherited population initialization according to feedback and so on are designed. Moreover, to prevent the incertitude of the metaheuristic algorithm and ensure the flight stability, a guaranteed control strategy is designed. Finally, a time-varying simulation model is introduced to verifying the effectiveness of the proposed scheme. The contrastive simulation results indicate that the proposed scheme achieves superior tracking performance with appropriate actuator dynamics and computational time, and the improvements for efficiency are active and the parameter settings are reasonable.

ESO-based robust predictive control of lunar module with fuel sloshing dynamics

An extended-state-observer(ESO) based predictive control scheme is proposed for the autopilot of lunar landing.The slosh fuel masses exert forces and torques on the rigid body of lunar module(LM),such disturbances will dramatically undermine the stability of autopilot system.The fuel sloshing dynamics and uncertainties due to the time-varying parameters are considered as a generalized disturbance which is estimated by an ESO from the measured attitude signals and the control input signals.Then a continuous-time predictive controller driven by the estimated states and disturbances is designed to obtain the virtual control input,which is allocated to the real control actuators according to a deadband logic.The 6-DOF simulation results reveal the effectiveness of the proposed method when dealing with the fuel sloshing dynamics and parameter perturbations.

Fault detection and optimization for networked control systems with uncertain time-varying delay

The observer-based robust fault detection filter design and optimization for networked control systems （NOSs） with uncer- tain time-varying delays are addressed. The NCSs with uncertain time-varying delays are modeled as parameter-uncertain systems by the matrix theory. Based on the model, an observer-based residual generator is constructed and the sufficient condition for the existence of the desired fault detection filter is derived in terms of the linear matrix inequality. Furthermore, a time domain opti- mization approach is proposed to improve the performance of the fault detection system. To prevent the false alarms, a new thresh- old function is established, and the solution of the optimization problem is given by using the singular value decomposition （SVD） of the matrix. A numerical example is provided to illustrate the effectiveness of the proposed approach.

Trajectory optimization of a reentry vehicle based on artificial emotion memory optimization

The trajectory optimization of an unpowered reentry vehicle via artificial emotion memory optimization(AEMO)is discussed.Firstly,reentry dynamics are established based on multiple constraints and parameterized control variables with finite dimensions are designed.If the constraint is not satisfied,a distance measure and an adaptive penalty function are used to address this scenario.Secondly,AEMO is introduced to solve the trajectory optimization problem.Based on the theories of biology and cognition,the trial solutions based on emotional memory are established.Three search strategies are designed for realizing the random search of trial solutions and for avoiding becoming trapped in a local minimum.The states of the trial solutions are determined according to the rules of memory enhancement and forgetting.As the iterations proceed,the trial solutions with poor quality will gradually be forgotten.Therefore,the number of trial solutions is decreased,and the convergence of the algorithm is accelerated.Finally,a numerical simulation is conducted,and the results demonstrate that the path and terminal constraints are satisfied and the method can realize satisfactory performance.

Asynchronous H_∞ Control for Unmanned Aerial Vehicles: Switched Polytopic System Approach

This study is concerned with the H∞control for the full-envelope unmanned aerial vehicles(UAVs) in the presence of missing measurements and external disturbances. With the dramatic parameter variations in large flight envelope and the locally overlapped switching laws in flight, the system dynamics is modeled as a locally overlapped switched polytopic system to reduce designing conservatism and solving complexity. Then,considering updating lags of controller s switching signals and the weighted coefficients of the polytopic subsystems induced by missing measurements, an asynchronous H∞control method is proposed such that the system is stable and a desired disturbance attenuation level is satisfied. Furthermore, the sufficient existing conditions of the desired switched parameter-dependent H∞controller are derived in the form of linear matrix inequality(LMIs) by combining the switched parameter-dependent Lyapunov function method and average dwell time method.Finally, a numerical example based on a highly maneuverable technology(Hi MAT) vehicle is given to verify the validity of the proposed method.

新一代运载火箭自主控制技术

This paper introduces the autonomous control technologies for a new generation launch vehicle for guidance and attitude control.Based on the iterative guidance mode(IGM)of Long March launch vehicles,the autonomous compensation IGM(ACIGM)for the terminal attitude deviation during the coasting phase is proposed.Considering the characteristics of large static instability and weak bearing capacity,the attitude control technology based on active disturbance rejection control(ADRC)and a control method based on an accelerometer are proposed.Targeting at non-fatal failures that may occur during flights,autonomous guidance reconstruction technology,nozzle fault diagnosis and reconstruction technology in the coasting phase are studied.Some of the autonomous control technologies proposed in this paper have achieved good control results as seen through flight verification.

The Research on Key Technologies of Chinese Heavy-Lift Launch Vehicle Control System

This paper reviewed the development of control technology in domestic and foreign launch vehicle(LV), and based on which, the key technologies of control system for Chinese heavy-lift launch vehicles were proposed. A dynamic on-line trajectory planning technique was discussed to meet the demand of guidance control under complex constraints, and model based identification and adaptive control technology was suggested to deal with the control problems caused by model uncertainty and disturbance, and an integrated avionics system based on high speed communication was put forward for module integration and distributed control, and FBG based real time flight control was also discussed. Moreover, other key technologies, such as wireless interconnection, wireless power transfer, and temporal and spatial partitioning operating system, are both briefly introduced for the application in control systems. These studies will lead to breakthroughs in autonomous flight control in LV, and provide technical support for more long-term deep space explorations.

Generating Periodic Orbits for Explorations of Elongated Asteroids

A practical method is proposed to search for periodic orbits of elongated asteroids.The method obtains required initial variables of periodic orbits by using the rotating mass dipole with appropriate parameters,and then implement local iterations to obtain the real orbits over an asteroid in the polyhedral model.In this paper the dipole and polyhedral models,and list detailed procedures of the searching method are introduced.A planar single lobe orbit is presented to evaluate the effectiveness of the method,with the asteroid 216 Kleopatra of the triple asteroid system as a representative elongated body.By applying the above method,ten families of periodic orbits around Kleopatra are identified and discussed with respect to their orbital stabilities and periods.One sample of the sombrero orbit is checked by calculating 1000 hours to examine its orbital behavior.Besides the above orbits,the intriguing head-surrounding orbit is also analyzed.

Angle Compensation and Asymmetry Effect of Light Diffracted by Millimeter Liquid Surface Slosh Wave

The angle compensation method is adopted to detect sloshing waves by laser diffraction, in the case that the wavelength of the sloshing waves is much greater than that of the incident light. The clear diffraction pattern is observed to be of asymmetry, involving orders, position and interval of the diffraction spots that are discovered during the light grazing incidence. It is found that the larger the angle of incidence is, the more obvious the asymmetry is. The higher the negative diffraction orders are, the smaller the intervals between spots are. On the contrary~ in the positive region, the higher the diffraction orders are, the larger the spot intervals are. The positive interval is larger than that of the same negative diffraction order. If the incident angle reaches 1.558 rad in the experiment, all positive diffraction orders completely vanish. Based on the mechanism of phase modulation and with the Fourier transform method, the relations between the incident angle and position, interval spaces, and orders of diffraction spots are derived theoretically. The theoretical calculations are compared with the experimental data, and the comparison shows that the theoretical calculations are in good agreement with the experimental measurement.

Robust PolInSAR optimal interferogram estimation method based on generalized scattering vector

For the polarimetric synthetic aperture radar interferometry (PoIInSAR) processing, it is necessary to coregister all the images, including the coregistration of polarimetric SAR images and the coregistration of interferometric SAR images. Otherwise, the performance of the estimated optimal interferograms will be deteriorated. A generalized scattering vector (GSV) model is proposed to execute the PoIInSAR optimal interferograms estimation. The generalized scattering vector is constructed by the Pauli scattering vectors of the processing pixel and the surrounding pixels. Even though there are coregistration errors, all the polarimetric information of the current processing pixel is entirely included in the generalized scattering vector. Therefore, the GSV-based method can automatically recover the optimal scattering mechanisms of the processing pixel with coregistration errors either in interferoemetric channels or polarimetric channels. Theoretical analysis and processing results of simulated PoISARPro data and real PALSAR data validate the effectiveness and correctness of the proposed method.

Recognition of vortex structures in turbulent refractive index field using wavelet transformation

When the light wave propagates through the hypersonic flow field, the aero-optic distortion happens. It is necessary to recognize the vortex structure for studying the light propagation model. A new vortex structure recognition method is proposed in this paper. Firstly, the refractive index field, which is transformed from the turbulent density field, is changed to gray scale images with a- bundant texture information equivalently. Then, wavelet transform is performed to decompose these images and the entropy values of the wavelet base coefficients are calculated. Comparing the entropy value to a given threshold, the modules with lower entropy are considered to be the large-scale vortex modules while those with higher entropy are small-scale vortex modules. The computer simulation results show that the proposed method is valid to recognize the vortex structures. This paper provides basis for investigation on the optics propagation model in terms of the turbulence vortex structures.

Non-identical residual learning for image enhancement via dynamic multi-level perceptual loss

Residual learning based deep generative networks have achieved promising performance in image enhancement.However,due to the large color gap between a low-quality image and its highquality version,the identical mapping in conventional residual learning cannot explore the elaborate detail differences,resulting in color deviations and texture losses in enhanced images.To address this issue,an innovative non-identical residual learning architecture is proposed,which views image enhancement as two complementary branches,namely a holistic color adjustment branch and a finegrained residual generation branch.In the holistic color adjustment,an adjusting map is calculated for each input low-quality image,in order to regulate the low-quality image to the high-quality representation in an overall way.In the fine-grained residual generation branch,a novel attention-aware recursive network is designed to generate residual images.This design can alleviate the overfitting problem by reusing parameters and promoting the network’s adaptability for different input conditions.In addition,a novel dynamic multi-level perceptual loss based on the error feedback ideology is proposed.Consequently,the proposed network can be dynamically optimized by the hybrid perceptual loss provided by a well-trained VGG,so as to improve the perceptual quality of enhanced images in a guided way.Extensive experiments conducted on publicly available datasets demonstrate the state-of-the-art performance of the proposed method.