Transactions of Nanjing University of Aeronautics & Astronautics
Volume 38,Issue 4,2021 Table of Contents
2021, 38(4):1-2.
DOI: 10.16356/j.1005-1120.2021.04.001
Abstract:
2021, 38(4):535-544.
DOI: 10.16356/j.1005-1120.2021.04.001
Abstract:
Icing is an important factor threatening aircraft flight safety. According to the requirements of airworthiness regulations， aircraft icing safety assessment is needed to be carried out based on the ice shapes formed under different icing conditions. Due to the complexity of the icing process， the rapid assessment of ice shape remains an important challenge. In this paper， an efficient prediction model of aircraft icing is established based on the deep belief network （DBN） and the stacked auto-encoder （SAE）， which are all deep neural networks. The detailed network structures are designed and then the networks are trained according to the samples obtained by the icing numerical computation. After that the model is applied on the ice shape evaluation of NACA0012 airfoil. The results show that the model can accurately capture the nonlinear behavior of aircraft icing and thus make an excellent ice shape prediction. The model provides an important tool for aircraft icing analysis.
Icing is an important factor threatening aircraft flight safety. According to the requirements of airworthiness regulations， aircraft icing safety assessment is needed to be carried out based on the ice shapes formed under different icing conditions. Due to the complexity of the icing process， the rapid assessment of ice shape remains an important challenge. In this paper， an efficient prediction model of aircraft icing is established based on the deep belief network （DBN） and the stacked auto-encoder （SAE）， which are all deep neural networks. The detailed network structures are designed and then the networks are trained according to the samples obtained by the icing numerical computation. After that the model is applied on the ice shape evaluation of NACA0012 airfoil. The results show that the model can accurately capture the nonlinear behavior of aircraft icing and thus make an excellent ice shape prediction. The model provides an important tool for aircraft icing analysis.
2021, 38(4):545-559.
DOI: 10.16356/j.1005-1120.2021.04.002
Abstract:
Performance parameter prediction technology is the core research content of aeroengine health management， and more and more machine learning algorithms have been applied in the field. Regularized extreme learning machine （RELM） is one of them. However， the regularization parameter determination of RELM consumes computational resources， which makes it unsuitable in the field of aeroengine performance parameter prediction with a large amount of data. This paper uses the forward and backward segmentation （FBS） algorithms to improve the RELM performance， and introduces an adaptive step size determination method and an improved solution mechanism to obtain a new machine learning algorithm. While maintaining good generalization， the new algorithm is not sensitive to regularization parameters， which greatly saves computing resources. The experimental results on the public data sets prove the above conclusions. Finally， the new algorithm is applied to the prediction of aero-engine performance parameters， and the excellent prediction performance is achieved.
Performance parameter prediction technology is the core research content of aeroengine health management， and more and more machine learning algorithms have been applied in the field. Regularized extreme learning machine （RELM） is one of them. However， the regularization parameter determination of RELM consumes computational resources， which makes it unsuitable in the field of aeroengine performance parameter prediction with a large amount of data. This paper uses the forward and backward segmentation （FBS） algorithms to improve the RELM performance， and introduces an adaptive step size determination method and an improved solution mechanism to obtain a new machine learning algorithm. While maintaining good generalization， the new algorithm is not sensitive to regularization parameters， which greatly saves computing resources. The experimental results on the public data sets prove the above conclusions. Finally， the new algorithm is applied to the prediction of aero-engine performance parameters， and the excellent prediction performance is achieved.
2021, 38(4):560-570.
DOI: 10.16356/j.1005-1120.2021.04.003
Abstract:
The libration control problem of space tether system（STS） for post-capture of payload is studied. The process of payload capture will cause tether swing and deviation from the nominal position， resulting in the failure of capture mission. Due to unknown inertial parameters after capturing the payload， an adaptive optimal control based on policy iteration is developed to stabilize the uncertain dynamic system in the post-capture phase. By introducing integral reinforcement learning （IRL） scheme， the algebraic Riccati equation （ARE） can be online solved without known dynamics. To avoid computational burden from iteration equations， the online implementation of policy iteration algorithm is provided by the least-squares solution method. Finally， the effectiveness of the algorithm is validated by numerical simulations.
The libration control problem of space tether system（STS） for post-capture of payload is studied. The process of payload capture will cause tether swing and deviation from the nominal position， resulting in the failure of capture mission. Due to unknown inertial parameters after capturing the payload， an adaptive optimal control based on policy iteration is developed to stabilize the uncertain dynamic system in the post-capture phase. By introducing integral reinforcement learning （IRL） scheme， the algebraic Riccati equation （ARE） can be online solved without known dynamics. To avoid computational burden from iteration equations， the online implementation of policy iteration algorithm is provided by the least-squares solution method. Finally， the effectiveness of the algorithm is validated by numerical simulations.
2021, 38(4):571-586.
DOI: 10.16356/j.1005-1120.2021.04.004
Abstract:
The airport apron scene contains rich contextual information about the spatial position relationship. Traditional object detectors only considered visual appearance and ignored the contextual information. In addition， the detection accuracy of some categories in the apron dataset was low. Therefore， an improved object detection method using spatial-aware features in apron scenes called SA-FRCNN is presented. The method uses graph convolutional networks to capture the relative spatial relationship between objects in the apron scene， incorporating this spatial context into feature learning. Moreover， an attention mechanism is introduced into the feature extraction process， with the goal to focus on the spatial position and key features， and distance-IoU loss is used to achieve a more accurate regression. The experimental results show that the mean average precision of the apron object detection based on SA-FRCNN can reach 95.75%， and the detection effect of some hard-to-detect categories has been significantly improved. The proposed method effectively improves the detection accuracy on the apron dataset， which has a leading advantage over other methods.
The airport apron scene contains rich contextual information about the spatial position relationship. Traditional object detectors only considered visual appearance and ignored the contextual information. In addition， the detection accuracy of some categories in the apron dataset was low. Therefore， an improved object detection method using spatial-aware features in apron scenes called SA-FRCNN is presented. The method uses graph convolutional networks to capture the relative spatial relationship between objects in the apron scene， incorporating this spatial context into feature learning. Moreover， an attention mechanism is introduced into the feature extraction process， with the goal to focus on the spatial position and key features， and distance-IoU loss is used to achieve a more accurate regression. The experimental results show that the mean average precision of the apron object detection based on SA-FRCNN can reach 95.75%， and the detection effect of some hard-to-detect categories has been significantly improved. The proposed method effectively improves the detection accuracy on the apron dataset， which has a leading advantage over other methods.
2021, 38(4):587-596.
DOI: 10.16356/j.1005-1120.2021.04.005
Abstract:
Single object tracking based on deep learning has achieved the advanced performance in many applications of computer vision. However， the existing trackers have certain limitations owing to deformation， occlusion， movement and some other conditions. We propose a siamese attentional dense network called SiamADN in an end-to-end offline manner， especially aiming at unmanned aerial vehicle （UAV） tracking. First， it applies a dense network to reduce vanishing-gradient， which strengthens the features transfer. Second， the channel attention mechanism is involved into the Densenet structure， in order to focus on the possible key regions. The advance corner detection network is introduced to improve the following tracking process. Extensive experiments are carried out on four mainly tracking benchmarks as OTB-2015， UAV123， LaSOT and VOT. The accuracy rate on UAV123 is 78.9%， and the running speed is 32 frame per second （FPS）， which demonstrates its efficiency in the practical real application.
Single object tracking based on deep learning has achieved the advanced performance in many applications of computer vision. However， the existing trackers have certain limitations owing to deformation， occlusion， movement and some other conditions. We propose a siamese attentional dense network called SiamADN in an end-to-end offline manner， especially aiming at unmanned aerial vehicle （UAV） tracking. First， it applies a dense network to reduce vanishing-gradient， which strengthens the features transfer. Second， the channel attention mechanism is involved into the Densenet structure， in order to focus on the possible key regions. The advance corner detection network is introduced to improve the following tracking process. Extensive experiments are carried out on four mainly tracking benchmarks as OTB-2015， UAV123， LaSOT and VOT. The accuracy rate on UAV123 is 78.9%， and the running speed is 32 frame per second （FPS）， which demonstrates its efficiency in the practical real application.
2021, 38(4):597-606.
DOI: 10.16356/j.1005-1120.2021.04.006
Abstract:
As a prospective component of the future air transportation system， unmanned aerial vehicles （UAVs） have attracted enormous interest in both academia and industry. However， small UAVs are barely supervised in the current situation. Crash accidents or illegal airspace invading caused by these small drones affect public security negatively. To solve this security problem， we use the back-propagation neural network （BPNN）， the support-vector machine （SVM）， and the k-nearest neighbors （KNN） method to detect and classify the non-cooperative drones at the edge of the flight restriction zone based on the cepstrum of the radio frequency （RF） signal of the drone’s downlink. The signal from five various amateur drones and ambient wireless devices are sampled in an electromagnetic clean environment. The detection and classification algorithm based on the cepstrum properties is conducted. Results of the outdoor experiments suggest the proposed workflow and methods are sufficient to detect non-cooperative drones with an average accuracy of around 90%. The mainstream downlink protocols of amateur drones can be classified effectively as well.
As a prospective component of the future air transportation system， unmanned aerial vehicles （UAVs） have attracted enormous interest in both academia and industry. However， small UAVs are barely supervised in the current situation. Crash accidents or illegal airspace invading caused by these small drones affect public security negatively. To solve this security problem， we use the back-propagation neural network （BPNN）， the support-vector machine （SVM）， and the k-nearest neighbors （KNN） method to detect and classify the non-cooperative drones at the edge of the flight restriction zone based on the cepstrum of the radio frequency （RF） signal of the drone’s downlink. The signal from five various amateur drones and ambient wireless devices are sampled in an electromagnetic clean environment. The detection and classification algorithm based on the cepstrum properties is conducted. Results of the outdoor experiments suggest the proposed workflow and methods are sufficient to detect non-cooperative drones with an average accuracy of around 90%. The mainstream downlink protocols of amateur drones can be classified effectively as well.
2021, 38(4):607-615.
DOI: 10.16356/j.1005-1120.2021.04.007
Abstract:
It is particular important to identify the pattern of communication signal quickly and accurately at the airport terminal area with the increasing number of radio equipments. A signal modulation pattern recognition method based on compressive sensing and improved residual network is proposed in this work. Firstly， the compressive sensing method is introduced in the signal preprocessing process to discard the redundant components for sampled signals. And the compressed measurement signals are taken as the input of the network. Furthermore， based on a scaled exponential linear units activation function， the residual unit and the residual network are constructed in this work to solve the problem of long training time and indistinguishable sample similar characteristics. Finally， the global residual is introduced into the training network to guarantee the convergence of the network. Simulation results show that the proposed method has higher recognition efficiency and accuracy compared with the state-of-the-art deep learning methods.
It is particular important to identify the pattern of communication signal quickly and accurately at the airport terminal area with the increasing number of radio equipments. A signal modulation pattern recognition method based on compressive sensing and improved residual network is proposed in this work. Firstly， the compressive sensing method is introduced in the signal preprocessing process to discard the redundant components for sampled signals. And the compressed measurement signals are taken as the input of the network. Furthermore， based on a scaled exponential linear units activation function， the residual unit and the residual network are constructed in this work to solve the problem of long training time and indistinguishable sample similar characteristics. Finally， the global residual is introduced into the training network to guarantee the convergence of the network. Simulation results show that the proposed method has higher recognition efficiency and accuracy compared with the state-of-the-art deep learning methods.
2021, 38(4):616-624.
DOI: 10.16356/j.1005-1120.2021.04.008
Abstract:
The homogeneity analysis of multi-airport system can provide important decision-making support for the route layout and cooperative operation. Existing research seldom analyzes the homogeneity of multi-airport system from the perspective of route network analysis， and the attribute information of airport nodes in the airport route network is not appropriately integrated into the airport network. In order to solve this problem， a multi-airport system homogeneity analysis method based on airport attribute network representation learning is proposed. Firstly， the route network of a multi-airport system with attribute information is constructed. If there are flights between airports， an edge is added between airports， and regional attribute information is added for each airport node. Secondly， the airport attributes and the airport network vector are represented respectively. The airport attributes and the airport network vector are embedded into the unified airport representation vector space by the network representation learning method， and then the airport vector integrating the airport attributes and the airport network characteristics is obtained. By calculating the similarity of the airport vectors， it is convenient to calculate the degree of homogeneity between airports and the homogeneity of the multi-airport system. The experimental results on the Beijing-Tianjin-Hebei multi-airport system show that， compared with other existing algorithms， the homogeneity analysis method based on attributed network representation learning can get more consistent results with the current situation of Beijing-Tianjin-Hebei multi-airport system.
The homogeneity analysis of multi-airport system can provide important decision-making support for the route layout and cooperative operation. Existing research seldom analyzes the homogeneity of multi-airport system from the perspective of route network analysis， and the attribute information of airport nodes in the airport route network is not appropriately integrated into the airport network. In order to solve this problem， a multi-airport system homogeneity analysis method based on airport attribute network representation learning is proposed. Firstly， the route network of a multi-airport system with attribute information is constructed. If there are flights between airports， an edge is added between airports， and regional attribute information is added for each airport node. Secondly， the airport attributes and the airport network vector are represented respectively. The airport attributes and the airport network vector are embedded into the unified airport representation vector space by the network representation learning method， and then the airport vector integrating the airport attributes and the airport network characteristics is obtained. By calculating the similarity of the airport vectors， it is convenient to calculate the degree of homogeneity between airports and the homogeneity of the multi-airport system. The experimental results on the Beijing-Tianjin-Hebei multi-airport system show that， compared with other existing algorithms， the homogeneity analysis method based on attributed network representation learning can get more consistent results with the current situation of Beijing-Tianjin-Hebei multi-airport system.
2021, 38(4):625-633.
DOI: 10.16356/j.1005-1120.2021.04.009
Abstract:
The rapid growth of air traffic has continuously increased the workload of controllers， which has become an important factor restricting sector capacity. If similar traffic scenes can be identified， the historical decision-making experience may be used to help controllers decide control strategies quickly. Considering that there are many traffic scenes and it is hard to label them all， in this paper， we propose an active SVM metric learning （ASVM2L） algorithm to measure and identify the similar traffic scenes. First of all， we obtain some traffic scene samples correctly labeled by experienced air traffic controllers. We design an active sampling strategy based on voting difference to choose the most valuable unlabeled samples and label them. Then the metric matrix of all the labeled samples is learned and used to complete the classification of traffic scenes. We verify the effectiveness of ASVM2L on standard data sets， and then use it to measure and classify the traffic scenes on the historical air traffic data set of the Central South Sector of China. The experimental results show that， compared with other existing methods， the proposed method can use the information of traffic scene samples more thoroughly and achieve better classification performance under limited labeled samples.
The rapid growth of air traffic has continuously increased the workload of controllers， which has become an important factor restricting sector capacity. If similar traffic scenes can be identified， the historical decision-making experience may be used to help controllers decide control strategies quickly. Considering that there are many traffic scenes and it is hard to label them all， in this paper， we propose an active SVM metric learning （ASVM2L） algorithm to measure and identify the similar traffic scenes. First of all， we obtain some traffic scene samples correctly labeled by experienced air traffic controllers. We design an active sampling strategy based on voting difference to choose the most valuable unlabeled samples and label them. Then the metric matrix of all the labeled samples is learned and used to complete the classification of traffic scenes. We verify the effectiveness of ASVM2L on standard data sets， and then use it to measure and classify the traffic scenes on the historical air traffic data set of the Central South Sector of China. The experimental results show that， compared with other existing methods， the proposed method can use the information of traffic scene samples more thoroughly and achieve better classification performance under limited labeled samples.
2021, 38(4):634-645.
DOI: 10.16356/j.1005-1120.2021.04.010
Abstract:
In order to improve the accuracy and stability of terminal traffic flow prediction in convective weather， a multi-input deep learning （MICL） model is proposed. On the basis of previous studies， this paper expands the set of weather characteristics affecting the traffic flow in the terminal area， including weather forecast data and Meteorological Report of Aerodrome Conditions （METAR） data. The terminal airspace is divided into smaller areas based on function and the weather severity index （WSI） characteristics extracted from weather forecast data are established to better quantify the impact of weather. MICL model preserves the advantages of the convolution neural network （CNN） and the long short-term memory （LSTM） model， and adopts two channels to input WSI and METAR information， respectively， which can fully reflect the temporal and spatial distribution characteristics of weather in the terminal area. Multi-scene experiments are designed based on the real historical data of Guangzhou Terminal Area operating in typical convective weather. The results show that the MICL model has excellent performance in mean squared error （MSE）， root MSE （RMSE）， mean absolute error （MAE） and other performance indicators compared with the existing machine learning models or deep learning models， such as K-nearest neighbor （KNN）， support vector regression （SVR）， CNN and LSTM. In the forecast period ranging from 30 min to 6 h， the MICL model has the best prediction accuracy and stability.
In order to improve the accuracy and stability of terminal traffic flow prediction in convective weather， a multi-input deep learning （MICL） model is proposed. On the basis of previous studies， this paper expands the set of weather characteristics affecting the traffic flow in the terminal area， including weather forecast data and Meteorological Report of Aerodrome Conditions （METAR） data. The terminal airspace is divided into smaller areas based on function and the weather severity index （WSI） characteristics extracted from weather forecast data are established to better quantify the impact of weather. MICL model preserves the advantages of the convolution neural network （CNN） and the long short-term memory （LSTM） model， and adopts two channels to input WSI and METAR information， respectively， which can fully reflect the temporal and spatial distribution characteristics of weather in the terminal area. Multi-scene experiments are designed based on the real historical data of Guangzhou Terminal Area operating in typical convective weather. The results show that the MICL model has excellent performance in mean squared error （MSE）， root MSE （RMSE）， mean absolute error （MAE） and other performance indicators compared with the existing machine learning models or deep learning models， such as K-nearest neighbor （KNN）， support vector regression （SVR）， CNN and LSTM. In the forecast period ranging from 30 min to 6 h， the MICL model has the best prediction accuracy and stability.
2021, 38(4):646-655.
DOI: 10.16356/j.1005-1120.2021.04.011
Abstract:
Accident causation analysis is of great importance for accident prevention. In order to improve the aviation safety， a new analysis method of aviation accident causation based on complex network theory is proposed in this paper. Through selecting 257 accident investigation reports， 45 causative factors and nine accident types are obtained by the three-level coding process of the grounded theory， and the interaction of these factors is analyzed based on the “2-4” model. Accordingly， the aviation accident causation network is constructed based on complex network theory which has scale-free characteristics and small-world properties， the characteristics of causative factors are analyzed by the topology of the network， and the key causative factors of the accidents are identified by the technique for order of preference by similarity to ideal solution （TOPSIS） method. The comparison results show that the method proposed in this paper has the advantages of independent of expert experience， quantitative analysis of accident causative factors and statistical analysis of a lot of accident data， and it has better applicability and advancement.
Accident causation analysis is of great importance for accident prevention. In order to improve the aviation safety， a new analysis method of aviation accident causation based on complex network theory is proposed in this paper. Through selecting 257 accident investigation reports， 45 causative factors and nine accident types are obtained by the three-level coding process of the grounded theory， and the interaction of these factors is analyzed based on the “2-4” model. Accordingly， the aviation accident causation network is constructed based on complex network theory which has scale-free characteristics and small-world properties， the characteristics of causative factors are analyzed by the topology of the network， and the key causative factors of the accidents are identified by the technique for order of preference by similarity to ideal solution （TOPSIS） method. The comparison results show that the method proposed in this paper has the advantages of independent of expert experience， quantitative analysis of accident causative factors and statistical analysis of a lot of accident data， and it has better applicability and advancement.
2021, 38(4):656-670.
DOI: 10.16356/j.1005-1120.2021.04.012
Abstract:
With the rapid growth of global air traffic， flight delays are increasingly serious. Convective weather is one of the influential causes for flight delays， which has affected the sustainable development of civil aviation industry and became a social problem. If it can be predicted that whether a weather-related flight diverts， participants in air traffic activities can coordinate the scheduling， and flight delays can be reduced greatly. In this paper， the weather avoidance prediction model （WAPM） is proposed to find the relationship between weather and flight trajectories， and predict whether a future flight diverts based on historical flight data. First， given the large amount of weather data， the principal component analysis is used to reduce the ten dimensional weather indicators to extract 90% information. Second， the support vector machine is adopted to predict whether the flight diverts by determining the hyperparameters c and γ of the radial basis function. Finally， the performance of the proposed model is evaluated by prediction accuracy， precision， recall and F1， and compared with the methods of the k nearest neighbor （kNN）， the logistic regression（LR）， the random forest（RF） and the deep neural networks （DNNs）. WAPM’s accuracy is 5.22%， 2.63%， 2.26% and 1.03% greater than those of kNN， LR， RF and DNNs， respectively； WAPM’s precision is 6.79%， 5.19%， 4.37% and 3.21% greater than those of kNN， LR， RF and DNNs， respectively； WAPM’s recall is 4.05%， 1.05%， 0.04% greater than those of kNN， LR， and RF， respectively， and 1.38% lower than that of the DNNs； and F1 of WAPM is 5.28%， 1.69%， 1.98% and 0.68% greater than those of kNN， LR， RF and DNNs， respectively.
With the rapid growth of global air traffic， flight delays are increasingly serious. Convective weather is one of the influential causes for flight delays， which has affected the sustainable development of civil aviation industry and became a social problem. If it can be predicted that whether a weather-related flight diverts， participants in air traffic activities can coordinate the scheduling， and flight delays can be reduced greatly. In this paper， the weather avoidance prediction model （WAPM） is proposed to find the relationship between weather and flight trajectories， and predict whether a future flight diverts based on historical flight data. First， given the large amount of weather data， the principal component analysis is used to reduce the ten dimensional weather indicators to extract 90% information. Second， the support vector machine is adopted to predict whether the flight diverts by determining the hyperparameters c and γ of the radial basis function. Finally， the performance of the proposed model is evaluated by prediction accuracy， precision， recall and F1， and compared with the methods of the k nearest neighbor （kNN）， the logistic regression（LR）， the random forest（RF） and the deep neural networks （DNNs）. WAPM’s accuracy is 5.22%， 2.63%， 2.26% and 1.03% greater than those of kNN， LR， RF and DNNs， respectively； WAPM’s precision is 6.79%， 5.19%， 4.37% and 3.21% greater than those of kNN， LR， RF and DNNs， respectively； WAPM’s recall is 4.05%， 1.05%， 0.04% greater than those of kNN， LR， and RF， respectively， and 1.38% lower than that of the DNNs； and F1 of WAPM is 5.28%， 1.69%， 1.98% and 0.68% greater than those of kNN， LR， RF and DNNs， respectively.
2021, 38(4):671-684.
DOI: 10.16356/j.1005-1120.2021.04.013
Abstract:
In order to meet the needs of collaborative decision making， considering the different demands of air traffic control units， airlines， airports and passengers in various traffic scenarios， the joint scheduling problem of arrival and departure flights is studied systematically. According to the matching degree of capacity and flow， it is determined that the traffic state of arrival / departure operation in a certain period is peak or off-peak. The demands of all parties in each traffic state are analyzed， and the mathematical models of arrival / departure flight scheduling in each traffic state are established. Aiming at the four kinds of joint operation traffic scenarios of arrival and departure， the corresponding bi-level programming models for joint scheduling of arrival and departure flights are established， respectively， and the elitism genetic algorithm is designed to solve the models. The results show that： Compared with the first-come-first-served method， in the scenarios of arrival peak & departure off-peak and arrival peak & departure peak， the departure flight equilibrium satisfaction is improved， and the runway occupation time of departure flight flow is reduced by 38.8%. In the scenarios of arrival off-peak & departure off-peak and departure peak & arrival off-peak， the arrival flight equilibrium delay time is significantly reduced， the departure flight equilibrium satisfaction is improved by 77.6%， and the runway occupation time of departure flight flow is reduced by 46.6%. Compared with other four kinds of strategies， the optimal scheduling method can better balance fairness and efficiency， so the scheduling results are more reasonable.
In order to meet the needs of collaborative decision making， considering the different demands of air traffic control units， airlines， airports and passengers in various traffic scenarios， the joint scheduling problem of arrival and departure flights is studied systematically. According to the matching degree of capacity and flow， it is determined that the traffic state of arrival / departure operation in a certain period is peak or off-peak. The demands of all parties in each traffic state are analyzed， and the mathematical models of arrival / departure flight scheduling in each traffic state are established. Aiming at the four kinds of joint operation traffic scenarios of arrival and departure， the corresponding bi-level programming models for joint scheduling of arrival and departure flights are established， respectively， and the elitism genetic algorithm is designed to solve the models. The results show that： Compared with the first-come-first-served method， in the scenarios of arrival peak & departure off-peak and arrival peak & departure peak， the departure flight equilibrium satisfaction is improved， and the runway occupation time of departure flight flow is reduced by 38.8%. In the scenarios of arrival off-peak & departure off-peak and departure peak & arrival off-peak， the arrival flight equilibrium delay time is significantly reduced， the departure flight equilibrium satisfaction is improved by 77.6%， and the runway occupation time of departure flight flow is reduced by 46.6%. Compared with other four kinds of strategies， the optimal scheduling method can better balance fairness and efficiency， so the scheduling results are more reasonable.
2021, 38(4):685-694.
DOI: 10.16356/j.1005-1120.2021.04.014
Abstract:
The problem of two-dimensional direction of arrival （2D-DOA） estimation for uniform planar arrays （UPAs） is investigated by employing the reduced-dimensional （RD） polynomial root finding technique and 2D multiple signal classification （2D-MUSIC） algorithm. Specifically， based on the relationship between the noise subspace and steering vectors， we first construct 2D root polynomial for 2D-DOA estimates and then prove that the 2D polynomial function has infinitely many solutions. In particular， we propose a computationally efficient algorithm， termed RD-ROOT-MUSIC algorithm， to obtain the true solutions corresponding to targets by RD technique， where the 2D root-finding problem is substituted by two one-dimensional （1D） root-finding operations. Finally， accurate 2D-DOA estimates can be obtained by a sample pairing approach. In addition， numerical simulation results are given to corroborate the advantages of the proposed algorithm.
The problem of two-dimensional direction of arrival （2D-DOA） estimation for uniform planar arrays （UPAs） is investigated by employing the reduced-dimensional （RD） polynomial root finding technique and 2D multiple signal classification （2D-MUSIC） algorithm. Specifically， based on the relationship between the noise subspace and steering vectors， we first construct 2D root polynomial for 2D-DOA estimates and then prove that the 2D polynomial function has infinitely many solutions. In particular， we propose a computationally efficient algorithm， termed RD-ROOT-MUSIC algorithm， to obtain the true solutions corresponding to targets by RD technique， where the 2D root-finding problem is substituted by two one-dimensional （1D） root-finding operations. Finally， accurate 2D-DOA estimates can be obtained by a sample pairing approach. In addition， numerical simulation results are given to corroborate the advantages of the proposed algorithm.
2021, 38(4):695-703.
DOI: 10.16356/j.1005-1120.2021.04.015
Abstract:
Geomagnetic orbit determination fits for nanosatellites which pursue low cost and high-density ratio， but one of its disadvantages is the poor position accuracy introduced by magnetic bias. Here， a new method， named the fuzzy regulating unscented Kalman filter （FRUKF）， is proposed. The magnetic bias is regarded as a random walk model， and a fuzzy regulator is designed to estimate the magnetic bias more accurately. The input of the regulator is the derivative of magnetic bias estimated from unscented Kalman filter （UKF）. According to the fuzzy rule， the process noise covariance is adaptively determined. The FRUKF is evaluated using the real-flight data of the SWARM-A. The experimental results show that the root-mean-square （RMS） position error is 3.1 km and the convergence time is shorter than the traditional way.
Geomagnetic orbit determination fits for nanosatellites which pursue low cost and high-density ratio， but one of its disadvantages is the poor position accuracy introduced by magnetic bias. Here， a new method， named the fuzzy regulating unscented Kalman filter （FRUKF）， is proposed. The magnetic bias is regarded as a random walk model， and a fuzzy regulator is designed to estimate the magnetic bias more accurately. The input of the regulator is the derivative of magnetic bias estimated from unscented Kalman filter （UKF）. According to the fuzzy rule， the process noise covariance is adaptively determined. The FRUKF is evaluated using the real-flight data of the SWARM-A. The experimental results show that the root-mean-square （RMS） position error is 3.1 km and the convergence time is shorter than the traditional way.
2021, 38(4):704-712.
DOI: 10.16356/j.1005-1120.2021.04.016
Abstract:
The reconstruction of spacecraft cluster based on local information and distributed strategy is investigated. Each spacecraft is an intelligent individual that can detect information within a limited range and can determine its behavior based on surrounding information. The objective of the cluster is to achieve the formation reconstruction with minimum fuel consumption. Based on the principle of dual pulse rendezvous maneuver， three target selection strategies are designed for collision avoidance. Strategy-1 determines the target point’s attribution according to the target’s distance when the target point conflicts and uses a unit pulse to avoid a collision. Strategy-2 changes the collision avoidance behavior. When two spacecraft meet more than once， the strategy switches the target points of the two spacecraft. In Strategy-3， the spacecraft closer to the target has higher priority in target allocation. Strategy-3 also switches the target points when two spacecraft encounter more than once. The three strategies for a given position， different completion times， and random position are compared. Numerical simulations show that all three strategies can accomplish the spacecraft cluster's reconfiguration under the specified requirements. Strategy-3 is better than Strategy-1 in all simulation cases in the sense of less fuel consumption with different completion times and given location， and it is more effective than Strategy-2 in most of the completion time. With a random initial position and given time， Strategy-3 is better than Strategy-1 in about 70% of the cases and more stable.
The reconstruction of spacecraft cluster based on local information and distributed strategy is investigated. Each spacecraft is an intelligent individual that can detect information within a limited range and can determine its behavior based on surrounding information. The objective of the cluster is to achieve the formation reconstruction with minimum fuel consumption. Based on the principle of dual pulse rendezvous maneuver， three target selection strategies are designed for collision avoidance. Strategy-1 determines the target point’s attribution according to the target’s distance when the target point conflicts and uses a unit pulse to avoid a collision. Strategy-2 changes the collision avoidance behavior. When two spacecraft meet more than once， the strategy switches the target points of the two spacecraft. In Strategy-3， the spacecraft closer to the target has higher priority in target allocation. Strategy-3 also switches the target points when two spacecraft encounter more than once. The three strategies for a given position， different completion times， and random position are compared. Numerical simulations show that all three strategies can accomplish the spacecraft cluster's reconfiguration under the specified requirements. Strategy-3 is better than Strategy-1 in all simulation cases in the sense of less fuel consumption with different completion times and given location， and it is more effective than Strategy-2 in most of the completion time. With a random initial position and given time， Strategy-3 is better than Strategy-1 in about 70% of the cases and more stable.
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