This paper addresses the problem of sensor search scheduling in the complicated space environment faced by the low-earth orbit constellation.Several search scheduling methods based on the commonly used information gai...This paper addresses the problem of sensor search scheduling in the complicated space environment faced by the low-earth orbit constellation.Several search scheduling methods based on the commonly used information gain are compared via simulations first.Then a novel search scheduling method in the scenarios of uncertainty observation is proposed based on the global Shannon information gain and beta density based uncertainty model.Simulation results indicate that the beta density model serves a good option for solving the problem of target acquisition in the complicated space environments.展开更多
Responsive orbits have exhibited advantages in emergencies for their excellent responsiveness and coverage to targets.Generally,there are several conflicting metrics to trade in the orbit design for responsive space.A...Responsive orbits have exhibited advantages in emergencies for their excellent responsiveness and coverage to targets.Generally,there are several conflicting metrics to trade in the orbit design for responsive space.A special multiple-objective genetic algorithm,namely the Nondominated Sorting Genetic AlgorithmⅡ(NSGAⅡ),is used to design responsive orbits.This algorithm has considered the conflicting metrics of orbits to achieve the optimal solution,including the orbital elements and launch programs of responsive vehicles.Low-Earth fast access orbits and low-Earth repeat coverage orbits,two subtypes of responsive orbits,can be designed using NSGAI under given metric tradeoffs,number of vehicles,and launch mode.By selecting the optimal solution from the obtained Pareto fronts,a designer can process the metric tradeoffs conveniently in orbit design.Recurring to the flexibility of the algorithm,the NSGAI promotes the responsive orbit design further.展开更多
Low Earth orbit(LEO)satellite networks exhibit distinct characteristics,e.g.,limited resources of individual satellite nodes and dynamic network topology,which have brought many challenges for routing algorithms.To sa...Low Earth orbit(LEO)satellite networks exhibit distinct characteristics,e.g.,limited resources of individual satellite nodes and dynamic network topology,which have brought many challenges for routing algorithms.To satisfy quality of service(QoS)requirements of various users,it is critical to research efficient routing strategies to fully utilize satellite resources.This paper proposes a multi-QoS information optimized routing algorithm based on reinforcement learning for LEO satellite networks,which guarantees high level assurance demand services to be prioritized under limited satellite resources while considering the load balancing performance of the satellite networks for low level assurance demand services to ensure the full and effective utilization of satellite resources.An auxiliary path search algorithm is proposed to accelerate the convergence of satellite routing algorithm.Simulation results show that the generated routing strategy can timely process and fully meet the QoS demands of high assurance services while effectively improving the load balancing performance of the link.展开更多
针对低轨卫星星地通信高动态信道特点,采用正交时频空(Orthogonal Time Frequency Space, OTFS)调制方式,提出一种低导频开销、高精度的两阶段信道估计方法,实现对时延、多普勒频移和信道增益3个参数的精细估计。所提TP-CSIE(Two Phase ...针对低轨卫星星地通信高动态信道特点,采用正交时频空(Orthogonal Time Frequency Space, OTFS)调制方式,提出一种低导频开销、高精度的两阶段信道估计方法,实现对时延、多普勒频移和信道增益3个参数的精细估计。所提TP-CSIE(Two Phase Channel State Information Estimation)方案采用时域训练序列为导频结构,解决时延-多普勒(Delay-Doppler, DD)域嵌入式导频方案在高动态星地链路下导频开销过大的问题。由于DD域信道的固有稀疏性,OTFS信道估计问题被转化为稀疏信号的恢复问题。在算法第一阶段,选用稀疏信号恢复算法进行信道参数的初始估计,利用重叠相加法获得部分先验信息以提高压缩采样匹配追踪(Compressive Sampling Matching Pursuit, CoSAMP)算法的准确性。在算法第二阶段,设计增强型旋转不变子空间算法实现信道参数的准确估计。仿真结果表明,与现有方案相比,所提算法归一化均方误差性能约有7 dB性能的提升,误码率性能约有10 dB的提升。展开更多
In low Earth orbit(LEO)satellite networks,on-board energy resources of each satellite are extremely limited.And with the increase of the node number and the traffic transmis-sion pressure,the energy consumption in the...In low Earth orbit(LEO)satellite networks,on-board energy resources of each satellite are extremely limited.And with the increase of the node number and the traffic transmis-sion pressure,the energy consumption in the networks presents uneven distribution.To achieve energy balance in networks,an energy consumption balancing optimization algorithm of LEO networks based on distance energy factor(DEF)is proposed.The DEF is defined as the function of the inter-satellite link dis-tance and the cumulative network energy consumption ratio.According to the minimum sum of DEF on inter-satellite links,an energy consumption balancing algorithm based on DEF is pro-posed,which can realize dynamic traffic transmission optimiza-tion of multiple traffic services.It can effectively reduce the energy consumption pressure of core nodes with high energy consumption in the network,make full use of idle nodes with low energy consumption,and optimize the energy consumption dis-tribution of the whole network according to the continuous itera-tions of each traffic service flow.Simulation results show that,compared with the traditional shortest path algorithm,the pro-posed method can improve the balancing performance of nodes by 75%under certain traffic pressure,and realize the optimiza-tion of energy consumption balancing of the whole network.展开更多
基金supported by the National Defense Pre-research Foundation (9140A21041110KG0148)
文摘This paper addresses the problem of sensor search scheduling in the complicated space environment faced by the low-earth orbit constellation.Several search scheduling methods based on the commonly used information gain are compared via simulations first.Then a novel search scheduling method in the scenarios of uncertainty observation is proposed based on the global Shannon information gain and beta density based uncertainty model.Simulation results indicate that the beta density model serves a good option for solving the problem of target acquisition in the complicated space environments.
文摘Responsive orbits have exhibited advantages in emergencies for their excellent responsiveness and coverage to targets.Generally,there are several conflicting metrics to trade in the orbit design for responsive space.A special multiple-objective genetic algorithm,namely the Nondominated Sorting Genetic AlgorithmⅡ(NSGAⅡ),is used to design responsive orbits.This algorithm has considered the conflicting metrics of orbits to achieve the optimal solution,including the orbital elements and launch programs of responsive vehicles.Low-Earth fast access orbits and low-Earth repeat coverage orbits,two subtypes of responsive orbits,can be designed using NSGAI under given metric tradeoffs,number of vehicles,and launch mode.By selecting the optimal solution from the obtained Pareto fronts,a designer can process the metric tradeoffs conveniently in orbit design.Recurring to the flexibility of the algorithm,the NSGAI promotes the responsive orbit design further.
基金National Key Research and Development Program(2021YFB2900604)。
文摘Low Earth orbit(LEO)satellite networks exhibit distinct characteristics,e.g.,limited resources of individual satellite nodes and dynamic network topology,which have brought many challenges for routing algorithms.To satisfy quality of service(QoS)requirements of various users,it is critical to research efficient routing strategies to fully utilize satellite resources.This paper proposes a multi-QoS information optimized routing algorithm based on reinforcement learning for LEO satellite networks,which guarantees high level assurance demand services to be prioritized under limited satellite resources while considering the load balancing performance of the satellite networks for low level assurance demand services to ensure the full and effective utilization of satellite resources.An auxiliary path search algorithm is proposed to accelerate the convergence of satellite routing algorithm.Simulation results show that the generated routing strategy can timely process and fully meet the QoS demands of high assurance services while effectively improving the load balancing performance of the link.
文摘针对低轨卫星星地通信高动态信道特点,采用正交时频空(Orthogonal Time Frequency Space, OTFS)调制方式,提出一种低导频开销、高精度的两阶段信道估计方法,实现对时延、多普勒频移和信道增益3个参数的精细估计。所提TP-CSIE(Two Phase Channel State Information Estimation)方案采用时域训练序列为导频结构,解决时延-多普勒(Delay-Doppler, DD)域嵌入式导频方案在高动态星地链路下导频开销过大的问题。由于DD域信道的固有稀疏性,OTFS信道估计问题被转化为稀疏信号的恢复问题。在算法第一阶段,选用稀疏信号恢复算法进行信道参数的初始估计,利用重叠相加法获得部分先验信息以提高压缩采样匹配追踪(Compressive Sampling Matching Pursuit, CoSAMP)算法的准确性。在算法第二阶段,设计增强型旋转不变子空间算法实现信道参数的准确估计。仿真结果表明,与现有方案相比,所提算法归一化均方误差性能约有7 dB性能的提升,误码率性能约有10 dB的提升。
基金supported by the National Key Research and Development Program(2021YFB2900604).
文摘In low Earth orbit(LEO)satellite networks,on-board energy resources of each satellite are extremely limited.And with the increase of the node number and the traffic transmis-sion pressure,the energy consumption in the networks presents uneven distribution.To achieve energy balance in networks,an energy consumption balancing optimization algorithm of LEO networks based on distance energy factor(DEF)is proposed.The DEF is defined as the function of the inter-satellite link dis-tance and the cumulative network energy consumption ratio.According to the minimum sum of DEF on inter-satellite links,an energy consumption balancing algorithm based on DEF is pro-posed,which can realize dynamic traffic transmission optimiza-tion of multiple traffic services.It can effectively reduce the energy consumption pressure of core nodes with high energy consumption in the network,make full use of idle nodes with low energy consumption,and optimize the energy consumption dis-tribution of the whole network according to the continuous itera-tions of each traffic service flow.Simulation results show that,compared with the traditional shortest path algorithm,the pro-posed method can improve the balancing performance of nodes by 75%under certain traffic pressure,and realize the optimiza-tion of energy consumption balancing of the whole network.
