In this paper,a cooperative localization algorithm for autonomous underwater vehicles(AUVs)is proposed.A"parallel"model is adopted to describe the cooperative localization problem instead of the traditional&...In this paper,a cooperative localization algorithm for autonomous underwater vehicles(AUVs)is proposed.A"parallel"model is adopted to describe the cooperative localization problem instead of the traditional"leader-follower"model,and a linear programming associated with convex optimization method is used to deal with the problem.After an unknown-but-bounded model for sensor noise is assumed,bearing and range measurements can be modeled as linear constraints on the configuration space of the AUVs.Merging these constraints induces a convex polyhedron representing the set of all configurations consistent with the sensor measurements.Estimates for the uncertainty in the position of a single AUV or the relative positions of two or more nodes can then be obtained by projecting this polyhedron onto appropriate subspaces of the configuration space.Two different optimization algorithms are given to recover the uncertainty region according to the number of the AUVs.Simulation results are presented for a typical localization example of the AUV formation.The results show that our positioning method offers a good localization accuracy,although a small number of low-cost sensors are needed for each vehicle,and this validates that it is an economical and practical positioning approach compared with the traditional approach.展开更多
The stabilization problem of linear time-varying systems with both state and input constraints is considered. Sufficient conditions for the existence of the solution to this problem are derived and a gain-switched（ga...The stabilization problem of linear time-varying systems with both state and input constraints is considered. Sufficient conditions for the existence of the solution to this problem are derived and a gain-switched（gain-scheduled） state feedback control scheme is built to stabilize the constrained timevarying system. The design problem is transformed to a series of convex feasibility problems which can be solved efficiently. A design example is given to illustrate the effect of the proposed algorithm.展开更多
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 navig...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.展开更多
Frequency diverse array multiple-input multiple-output(FDA-MIMO)radar has gained considerable research attention due to its ability to effectively counter active repeater deception jamming in complex electromagnetic e...Frequency diverse array multiple-input multiple-output(FDA-MIMO)radar has gained considerable research attention due to its ability to effectively counter active repeater deception jamming in complex electromagnetic environments.The effectiveness of interference suppression by FDA-MIMO is limited by the inherent range-angle coupling issue in the FDA beampattern.Existing literature primarily focuses on control methods for FDA-MIMO radar beam direction under the assumption of static beampatterns,with insufficient exploration of techniques for managing nonstationary beam directions.To address this gap,this paper initially introduces the FDA-MIMO signal model and the calculation formula for the FDA-MIMO array output using the minimum variance distortionless response(MVDR)beamformer.Building on this,the problem of determining the optimal frequency offset for the FDA is rephrased as a convex optimization problem,which is then resolved using the cuckoo search(CS)algorithm.Simulations confirm the effectiveness of the proposed approach,showing that the frequency offsets obtained through the CS algorithm can create a dot-shaped beam direction at the target location while effectively suppressing interference signals within the mainlobe.展开更多
Level of repair analysis（LORA） is an important method of maintenance decision for establishing systems of operation and maintenance in the equipment development period. Currently, the research on equipment of repair...Level of repair analysis（LORA） is an important method of maintenance decision for establishing systems of operation and maintenance in the equipment development period. Currently, the research on equipment of repair level focuses on economic analysis models which are used to optimize costs and rarely considers the maintenance time required by the implementation of the maintenance program. In fact, as to the system requiring high mission complete success, the maintenance time is an important factor which has a great influence on the availability of equipment systems. Considering the relationship between the maintenance time and the spares stocks level, it is obvious that there are contradictions between the maintenance time and the cost. In order to balance these two factors, it is necessary to build an optimization LORA model. To this end, the maintenance time representing performance characteristic is introduced, and on the basis of spares stocks which is traditionally regarded as a decision variable, a decision variable of repair level is added, and a multi-echelon multiindenture（MEMI） optimization LORA model is built which takes the best cost-effectiveness ratio as the criterion, the expected number of backorder（EBO） as the objective function and the cost as the constraint. Besides, the paper designs a convex programming algorithm of multi-variable for the optimization model, provides solutions to the non-convex objective function and methods for improving the efficiency of the algorithm. The method provided in this paper is proved to be credible and effective according to the numerical example and the simulation result.展开更多
The minimization problem of time delays in networked control system （NCS） is concered, which is a hot area of such research field. First, some analysis and comments on time-delayed NCS model listed in previous work ...The minimization problem of time delays in networked control system （NCS） is concered, which is a hot area of such research field. First, some analysis and comments on time-delayed NCS model listed in previous work are given. Then, time delay minimization problem based on average behavior of network queuing delay is presented. Under fixed routing scheme and certain optimization performance indexes, the delay minimization problem is translated into convex optimization problem. And the solution of the delay minimization problems is attained through optimized allocation of flow rates among network links.展开更多
A direction of arrival（DOA） estimation algorithm is proposed using the concept of sparse representation. In particular, a new sparse signal representation model called the smoothed covariance vector（SCV） is establ...A direction of arrival（DOA） estimation algorithm is proposed using the concept of sparse representation. In particular, a new sparse signal representation model called the smoothed covariance vector（SCV） is established, which is constructed using the lower left diagonals of the covariance matrix. DOA estimation is then achieved from the SCV by sparse recovering, where two distinguished error limit estimation methods of the constrained optimization are proposed to make the algorithms more robust. The algorithm shows robust performance on DOA estimation in a uniform array, especially for coherent signals. Furthermore, it significantly reduces the computational load compared with those algorithms based on multiple measurement vectors（MMVs）. Simulation results validate the effectiveness and efficiency of the proposed algorithm.展开更多
Owing to the portability,cheapness and flexible deployment,the unmanned aerial vehicle-based radar and communication coexistence(RCC)systems are widely adopted in Internet of Things applications.A joint power,bandwidt...Owing to the portability,cheapness and flexible deployment,the unmanned aerial vehicle-based radar and communication coexistence(RCC)systems are widely adopted in Internet of Things applications.A joint power,bandwidth,and subchannel allocation(JPBSA)strategy is proposed for a RCC network,aiming to optimize resource utilization under mutual spectrum interference.The Cram er-Rao lower bound(CRLB)is employed to assess the target localization accuracy.The optimization model is formulated as minimizing the sum of weighted predicted CRLBs while satisfying the communication data rate requirements and constraints of power and bandwidth budget.It is shown that the JPBSA problem falls into the mixed-integer programming problem.Even worse,the three variables are coherent in the objective function and constraints.A four-phase alternating optimization framework(FPAOF)is developed to address this issue.The FPAOF incorporates the joint convexification of radar and communication power allocation via Taylor approximation,bandwidth upper bound adaptation,and the opportunistic spectrum access-based method for subchannel allocation.Numerical evaluations demonstrate the proposed strategy's superiority in terms of localization accuracy improvement and computational tractability in comparison to state-of-the-art methods.The findings also indicate the superiority of using CRLB as the optimization metric than the signal-to-interference-plus-noise and mutual information.展开更多
基金Supported by National High Technology Research and Development Program of China(863 Program)(2007AA809502C)National Natural Science Foundation of China(50979093)Program for New Century Excellent Talents in University(NCET-06-0877)
文摘In this paper,a cooperative localization algorithm for autonomous underwater vehicles(AUVs)is proposed.A"parallel"model is adopted to describe the cooperative localization problem instead of the traditional"leader-follower"model,and a linear programming associated with convex optimization method is used to deal with the problem.After an unknown-but-bounded model for sensor noise is assumed,bearing and range measurements can be modeled as linear constraints on the configuration space of the AUVs.Merging these constraints induces a convex polyhedron representing the set of all configurations consistent with the sensor measurements.Estimates for the uncertainty in the position of a single AUV or the relative positions of two or more nodes can then be obtained by projecting this polyhedron onto appropriate subspaces of the configuration space.Two different optimization algorithms are given to recover the uncertainty region according to the number of the AUVs.Simulation results are presented for a typical localization example of the AUV formation.The results show that our positioning method offers a good localization accuracy,although a small number of low-cost sensors are needed for each vehicle,and this validates that it is an economical and practical positioning approach compared with the traditional approach.
基金supported by the National Natural Science Foundation of China(6132106261503100)the China Postdoctoral Science Foundation(2014M550189)
文摘The stabilization problem of linear time-varying systems with both state and input constraints is considered. Sufficient conditions for the existence of the solution to this problem are derived and a gain-switched（gain-scheduled） state feedback control scheme is built to stabilize the constrained timevarying system. The design problem is transformed to a series of convex feasibility problems which can be solved efficiently. A design example is given to illustrate the effect of the proposed algorithm.
基金supported by the National Natural Science Foundation of China(61803357)。
文摘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.
基金supported by the National Natural Science Foundation of China(61503408)。
文摘Frequency diverse array multiple-input multiple-output(FDA-MIMO)radar has gained considerable research attention due to its ability to effectively counter active repeater deception jamming in complex electromagnetic environments.The effectiveness of interference suppression by FDA-MIMO is limited by the inherent range-angle coupling issue in the FDA beampattern.Existing literature primarily focuses on control methods for FDA-MIMO radar beam direction under the assumption of static beampatterns,with insufficient exploration of techniques for managing nonstationary beam directions.To address this gap,this paper initially introduces the FDA-MIMO signal model and the calculation formula for the FDA-MIMO array output using the minimum variance distortionless response(MVDR)beamformer.Building on this,the problem of determining the optimal frequency offset for the FDA is rephrased as a convex optimization problem,which is then resolved using the cuckoo search(CS)algorithm.Simulations confirm the effectiveness of the proposed approach,showing that the frequency offsets obtained through the CS algorithm can create a dot-shaped beam direction at the target location while effectively suppressing interference signals within the mainlobe.
基金supported by the National Natural Science Foundation of China(6110413261304148)
文摘Level of repair analysis（LORA） is an important method of maintenance decision for establishing systems of operation and maintenance in the equipment development period. Currently, the research on equipment of repair level focuses on economic analysis models which are used to optimize costs and rarely considers the maintenance time required by the implementation of the maintenance program. In fact, as to the system requiring high mission complete success, the maintenance time is an important factor which has a great influence on the availability of equipment systems. Considering the relationship between the maintenance time and the spares stocks level, it is obvious that there are contradictions between the maintenance time and the cost. In order to balance these two factors, it is necessary to build an optimization LORA model. To this end, the maintenance time representing performance characteristic is introduced, and on the basis of spares stocks which is traditionally regarded as a decision variable, a decision variable of repair level is added, and a multi-echelon multiindenture（MEMI） optimization LORA model is built which takes the best cost-effectiveness ratio as the criterion, the expected number of backorder（EBO） as the objective function and the cost as the constraint. Besides, the paper designs a convex programming algorithm of multi-variable for the optimization model, provides solutions to the non-convex objective function and methods for improving the efficiency of the algorithm. The method provided in this paper is proved to be credible and effective according to the numerical example and the simulation result.
基金This project was supported by the National Natural Science Foundation of China (60274014) Specialized Research Fund forthe Doctoral Programof Higher Education (20020487006) China Education Ministry’s Key Laboratory Foundation for Intelli-gent Manufacture Technology (I mstsu-2002 -03) .
文摘The minimization problem of time delays in networked control system （NCS） is concered, which is a hot area of such research field. First, some analysis and comments on time-delayed NCS model listed in previous work are given. Then, time delay minimization problem based on average behavior of network queuing delay is presented. Under fixed routing scheme and certain optimization performance indexes, the delay minimization problem is translated into convex optimization problem. And the solution of the delay minimization problems is attained through optimized allocation of flow rates among network links.
基金supported by the National Natural Science Foundation of China(6127130061405150)
文摘A direction of arrival（DOA） estimation algorithm is proposed using the concept of sparse representation. In particular, a new sparse signal representation model called the smoothed covariance vector（SCV） is established, which is constructed using the lower left diagonals of the covariance matrix. DOA estimation is then achieved from the SCV by sparse recovering, where two distinguished error limit estimation methods of the constrained optimization are proposed to make the algorithms more robust. The algorithm shows robust performance on DOA estimation in a uniform array, especially for coherent signals. Furthermore, it significantly reduces the computational load compared with those algorithms based on multiple measurement vectors（MMVs）. Simulation results validate the effectiveness and efficiency of the proposed algorithm.
基金supported by the National Natural Science Foundation of China under Grant Nos.62571544,62071482,62471348Shaanxi Association of Science and Technology Youth Talent Support Program Project,No.20230137Innovative Talents Cultivate Program for Technology Innovation Team of Shaanxi Province Under Grant No.2024RS-CXTD-08。
文摘Owing to the portability,cheapness and flexible deployment,the unmanned aerial vehicle-based radar and communication coexistence(RCC)systems are widely adopted in Internet of Things applications.A joint power,bandwidth,and subchannel allocation(JPBSA)strategy is proposed for a RCC network,aiming to optimize resource utilization under mutual spectrum interference.The Cram er-Rao lower bound(CRLB)is employed to assess the target localization accuracy.The optimization model is formulated as minimizing the sum of weighted predicted CRLBs while satisfying the communication data rate requirements and constraints of power and bandwidth budget.It is shown that the JPBSA problem falls into the mixed-integer programming problem.Even worse,the three variables are coherent in the objective function and constraints.A four-phase alternating optimization framework(FPAOF)is developed to address this issue.The FPAOF incorporates the joint convexification of radar and communication power allocation via Taylor approximation,bandwidth upper bound adaptation,and the opportunistic spectrum access-based method for subchannel allocation.Numerical evaluations demonstrate the proposed strategy's superiority in terms of localization accuracy improvement and computational tractability in comparison to state-of-the-art methods.The findings also indicate the superiority of using CRLB as the optimization metric than the signal-to-interference-plus-noise and mutual information.
