Vahram Stepanyan, Stefan Schuet, Kalmanje Krishnakumar
In this paper we consider migration problem for Data and Reasoning Fabric (DRF)-enabled airspace operations assuming a fixed cloud/edge infrastructure with allocated computing, storage, and power resources, where cloud/edge servers and communication stations are in a wired connected network, while vehicles use a wireless network for communication. The objective is to automatically select the best location for the requested service execution, which achieves minimum cost while satisfying the user quality of service (QoS) and available resources constraints. To this end, estimates of the response time, consumed energy, and total cost are defined for each potential compute location. A mixed-integer linear program is then formulated and solved to identify optimal compute locations given QoS constraints and network infrastructure limitations, with worst-case vehicle positioning. The approach is applied to trajectory replanning use case to avoid a collision with an emergency vehicle in real time.
{"title":"Optimal Service Migration for Data and Reasoning Fabric","authors":"Vahram Stepanyan, Stefan Schuet, Kalmanje Krishnakumar","doi":"10.2514/1.i011120","DOIUrl":"https://doi.org/10.2514/1.i011120","url":null,"abstract":"In this paper we consider migration problem for Data and Reasoning Fabric (DRF)-enabled airspace operations assuming a fixed cloud/edge infrastructure with allocated computing, storage, and power resources, where cloud/edge servers and communication stations are in a wired connected network, while vehicles use a wireless network for communication. The objective is to automatically select the best location for the requested service execution, which achieves minimum cost while satisfying the user quality of service (QoS) and available resources constraints. To this end, estimates of the response time, consumed energy, and total cost are defined for each potential compute location. A mixed-integer linear program is then formulated and solved to identify optimal compute locations given QoS constraints and network infrastructure limitations, with worst-case vehicle positioning. The approach is applied to trajectory replanning use case to avoid a collision with an emergency vehicle in real time.","PeriodicalId":50260,"journal":{"name":"Journal of Aerospace Information Systems","volume":"35 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134956794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study proposes a data-driven fault diagnosis for multicopter unmanned aerial vehicles that uses the principal direction vector of inertial measurement unit (IMU) sensor signals calculated by principal component analysis. The main idea comes from the fact that a normal sphere-shaped distribution of the sensor data changes to a specific elliptical shape under a certain thrust fault situation. The fault diagnosis is based on classification and regression using supervised learning with the gyroscope and accelerometer datasets of an IMU. We analyze the performance of the proposed approach by depending on different learning algorithms. To verify the diagnostic performance, ground experiments with a hexacopter on the gimbaled jig are performed for various cases of damaged propellers. Then, the applicability of the proposed data-driven fault diagnosis is confirmed by analyzing the accuracy of the fault’s location and degree.
{"title":"Data-Driven Diagnosis of Multicopter Thrust Fault Using Supervised Learning with Inertial Sensors","authors":"Taegyun Kim, Seungkeun Kim, Hyo-Sang Shin","doi":"10.2514/1.i011256","DOIUrl":"https://doi.org/10.2514/1.i011256","url":null,"abstract":"This study proposes a data-driven fault diagnosis for multicopter unmanned aerial vehicles that uses the principal direction vector of inertial measurement unit (IMU) sensor signals calculated by principal component analysis. The main idea comes from the fact that a normal sphere-shaped distribution of the sensor data changes to a specific elliptical shape under a certain thrust fault situation. The fault diagnosis is based on classification and regression using supervised learning with the gyroscope and accelerometer datasets of an IMU. We analyze the performance of the proposed approach by depending on different learning algorithms. To verify the diagnostic performance, ground experiments with a hexacopter on the gimbaled jig are performed for various cases of damaged propellers. Then, the applicability of the proposed data-driven fault diagnosis is confirmed by analyzing the accuracy of the fault’s location and degree.","PeriodicalId":50260,"journal":{"name":"Journal of Aerospace Information Systems","volume":"47 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136371337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dynamic targeting (DT) is an emerging concept in which data from a lookahead instrument are used to intelligently reconfigure and point a primary instrument to enhance science return. For example, in the smart ice hunting radar (Smart Ice Cloud Sensing project), a forward-looking radiometer is used to detect deep convective ice storms, which are then targeted using a radar. In other concepts, forward-looking sensors are used to detect clouds so that a primary sensor can avoid them. To this end, we have developed several algorithms from operations research and an artificial intelligence/heuristic search to point/reconfigure the dynamic instrument. We present simulation studies of DT for these concepts and benchmark these algorithms to show that DT is a powerful tool with the potential to significantly improve instrument science yield.
{"title":"Dynamic Targeting to Improve Earth Science Missions","authors":"Alberto Candela, Jason Swope, Steve A. Chien","doi":"10.2514/1.i011233","DOIUrl":"https://doi.org/10.2514/1.i011233","url":null,"abstract":"Dynamic targeting (DT) is an emerging concept in which data from a lookahead instrument are used to intelligently reconfigure and point a primary instrument to enhance science return. For example, in the smart ice hunting radar (Smart Ice Cloud Sensing project), a forward-looking radiometer is used to detect deep convective ice storms, which are then targeted using a radar. In other concepts, forward-looking sensors are used to detect clouds so that a primary sensor can avoid them. To this end, we have developed several algorithms from operations research and an artificial intelligence/heuristic search to point/reconfigure the dynamic instrument. We present simulation studies of DT for these concepts and benchmark these algorithms to show that DT is a powerful tool with the potential to significantly improve instrument science yield.","PeriodicalId":50260,"journal":{"name":"Journal of Aerospace Information Systems","volume":"69 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134995916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, the problem of microsatellites-based adaptive cooperative game attitude takeover control for a failed spacecraft is investigated. Specifically, a manned microsatellite (leader) and a team of autonomous microsatellites (followers) are ordered to cooperate to complete the attitude control task in an optimal way, in which the control strategy and the cost function (or intent) of the leader are unknown to the followers. Based on the differential game (DG) theory, the microsatellites-based attitude takeover control problem is formulated as a cooperative DG, in which each microsatellite has the individual cost function. A key problem is that the followers must infer the leader’s intent first, that is, retrieve the weighting matrix of the cost function of the leader. To achieve this, a composite adaptive law is introduced for each follower to estimate the feedback gain matrix of the leader by using system state data and the cost functions of other followers; based on this, the leader’s intent is inferred online by minimizing a residual error. Then, the cooperative game control law of each follower is designed by itself, and the Pareto equilibrium of the DG system is achieved. Finally, the effectiveness of the proposed leader–followers adaptive cooperative game control method is verified by a simulation study.
{"title":"Attitude Takeover Control of Failed Spacecraft via Leader–Followers Adaptive Cooperative Game","authors":"Huai-Ning Wu, Mi Wang","doi":"10.2514/1.i011242","DOIUrl":"https://doi.org/10.2514/1.i011242","url":null,"abstract":"In this paper, the problem of microsatellites-based adaptive cooperative game attitude takeover control for a failed spacecraft is investigated. Specifically, a manned microsatellite (leader) and a team of autonomous microsatellites (followers) are ordered to cooperate to complete the attitude control task in an optimal way, in which the control strategy and the cost function (or intent) of the leader are unknown to the followers. Based on the differential game (DG) theory, the microsatellites-based attitude takeover control problem is formulated as a cooperative DG, in which each microsatellite has the individual cost function. A key problem is that the followers must infer the leader’s intent first, that is, retrieve the weighting matrix of the cost function of the leader. To achieve this, a composite adaptive law is introduced for each follower to estimate the feedback gain matrix of the leader by using system state data and the cost functions of other followers; based on this, the leader’s intent is inferred online by minimizing a residual error. Then, the cooperative game control law of each follower is designed by itself, and the Pareto equilibrium of the DG system is achieved. Finally, the effectiveness of the proposed leader–followers adaptive cooperative game control method is verified by a simulation study.","PeriodicalId":50260,"journal":{"name":"Journal of Aerospace Information Systems","volume":"180 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136371512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Although swarms of unmanned aerial vehicles have received much attention in the last few years, adversarial swarms (that is, competitive swarm-versus-swarm games) have been less well studied. In this paper, we demonstrate a deep reinforcement learning method to train a policy of fixed-wing aircraft agents to leverage hand-scripted tactics to exploit force concentration advantage and within-team coordination opportunities to destroy, or destroy, as many opponent team members as possible while preventing teammates from being attrited. The efficacy of agents using the policy network trained using the proposed method outperform teams utilizing only one of the handcrafted baseline tactics in [Formula: see text]-vs-[Formula: see text] engagements for [Formula: see text] as small as two and as large as 64 as well as learner teams trained to vary their yaw rate actions, even when the trained team’s agents’ sensor range and teammate partnership possibility is constrained.
{"title":"Coordinating Team Tactics for Swarm-Versus-Swarm Adversarial Games","authors":"Laura G. Strickland, Matthew C. Gombolay","doi":"10.2514/1.i011226","DOIUrl":"https://doi.org/10.2514/1.i011226","url":null,"abstract":"Although swarms of unmanned aerial vehicles have received much attention in the last few years, adversarial swarms (that is, competitive swarm-versus-swarm games) have been less well studied. In this paper, we demonstrate a deep reinforcement learning method to train a policy of fixed-wing aircraft agents to leverage hand-scripted tactics to exploit force concentration advantage and within-team coordination opportunities to destroy, or destroy, as many opponent team members as possible while preventing teammates from being attrited. The efficacy of agents using the policy network trained using the proposed method outperform teams utilizing only one of the handcrafted baseline tactics in [Formula: see text]-vs-[Formula: see text] engagements for [Formula: see text] as small as two and as large as 64 as well as learner teams trained to vary their yaw rate actions, even when the trained team’s agents’ sensor range and teammate partnership possibility is constrained.","PeriodicalId":50260,"journal":{"name":"Journal of Aerospace Information Systems","volume":"112 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135373357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Strategic missions to orbit celestial bodies have primarily considered spacecraft trajectories as a two-step process: capture of the spacecraft within the gravitational influence of the body, followed by in-orbit maneuvers. Moreover, a priori maneuver planning approaches using Earth-based measurements tend to generate motion plans that have little scope of replanning, especially when the spacecraft is in the body’s vicinity. Fine-grained motion plans that respond to mission conditions require a detailed understanding of the gravitational forces around the body, which can provide essential information about the body. Our research focuses on a problem variant where the orbital maneuvers are designed to continually refine the onboard gravitational model of the body while simultaneously using the model to perform increasingly smoother orbital maneuvers. We develop a receding horizon approach. Starting with a (low-fidelity) gravity model created from Earth-based observations, the gravity model is continually updated as the spacecraft experiences varying gravitational forces. The updated model is simultaneously and continually used to replan the craft’s trajectory, ensuring that successive maneuvers respect the most up-to-date gravity model. The motion plan eventually attains a near-stable orbital motion. Such an approach has the potential to expand to autonomous missions to improve the mapping and exploration of smaller bodies.
{"title":"Simultaneous Motion Replanning and Gravity Model Refinement near Small Solar System Bodies","authors":"Aditya Savio Paul, Michael Otte","doi":"10.2514/1.i011200","DOIUrl":"https://doi.org/10.2514/1.i011200","url":null,"abstract":"Strategic missions to orbit celestial bodies have primarily considered spacecraft trajectories as a two-step process: capture of the spacecraft within the gravitational influence of the body, followed by in-orbit maneuvers. Moreover, a priori maneuver planning approaches using Earth-based measurements tend to generate motion plans that have little scope of replanning, especially when the spacecraft is in the body’s vicinity. Fine-grained motion plans that respond to mission conditions require a detailed understanding of the gravitational forces around the body, which can provide essential information about the body. Our research focuses on a problem variant where the orbital maneuvers are designed to continually refine the onboard gravitational model of the body while simultaneously using the model to perform increasingly smoother orbital maneuvers. We develop a receding horizon approach. Starting with a (low-fidelity) gravity model created from Earth-based observations, the gravity model is continually updated as the spacecraft experiences varying gravitational forces. The updated model is simultaneously and continually used to replan the craft’s trajectory, ensuring that successive maneuvers respect the most up-to-date gravity model. The motion plan eventually attains a near-stable orbital motion. Such an approach has the potential to expand to autonomous missions to improve the mapping and exploration of smaller bodies.","PeriodicalId":50260,"journal":{"name":"Journal of Aerospace Information Systems","volume":"70 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134995915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents the estimation method for uncertain parameters in flight vehicles, especially missile systems, based on physics-informed neural networks (PINNs) augmented with a novel integration-based loss. The proposed method identifies four types of structured uncertainty: burnout time, rocket motor tilt angle, location of the center of pressure, and control fin bias, which significantly affect the missile performance. In the estimation framework, as neural networks (NNs) are updated, these uncertainties are also identified simultaneously because they are also included in the structure of NNs. After testing 100 simulation data, the average estimation errors are within 1% of the mean value for each type of uncertainty. The methodology is able to identify the parameters despite noise corruption in the time-series data. Compared with the conventional PINNs, adding the new loss based on the integration of differential equations yields a more reliable estimation performance for all types of uncertainty. This approach can be effective for complex systems and ill-posed inverse problems, which makes it applicable to other aerospace systems.
{"title":"Identification of Uncertain Parameter in Flight Vehicle Using Physics-Informed Deep Learning","authors":"Kyung-Mi Na, Chang-Hun Lee","doi":"10.2514/1.i011269","DOIUrl":"https://doi.org/10.2514/1.i011269","url":null,"abstract":"This paper presents the estimation method for uncertain parameters in flight vehicles, especially missile systems, based on physics-informed neural networks (PINNs) augmented with a novel integration-based loss. The proposed method identifies four types of structured uncertainty: burnout time, rocket motor tilt angle, location of the center of pressure, and control fin bias, which significantly affect the missile performance. In the estimation framework, as neural networks (NNs) are updated, these uncertainties are also identified simultaneously because they are also included in the structure of NNs. After testing 100 simulation data, the average estimation errors are within 1% of the mean value for each type of uncertainty. The methodology is able to identify the parameters despite noise corruption in the time-series data. Compared with the conventional PINNs, adding the new loss based on the integration of differential equations yields a more reliable estimation performance for all types of uncertainty. This approach can be effective for complex systems and ill-posed inverse problems, which makes it applicable to other aerospace systems.","PeriodicalId":50260,"journal":{"name":"Journal of Aerospace Information Systems","volume":"216 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134907266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Motion primitives enable fast planning for complex and dynamic environments. Adversarial environments pose a particularly challenging and unpredictable scenario. Motion-primitive-based planners have the potential to provide benefit in these types of environments. The key challenge is to design a library of maneuvers that effectively capture the necessary capabilities of the vehicle. This work presents a primitive-based game tree search to solve adversarial games in continuous state and action spaces and applies a reinforcement learning framework to autonomously generate effective primitives for the given task. The results demonstrate the ability of the learning framework to produce maneuvers necessary for competing against adversaries. Furthermore, we propose a method for learning a model to estimate the state-dependent value of each motion primitives and demonstrate how to incorporate this model to increase planning performance under time constraints. Additionally, we compare our primitive-based algorithm against forward simulated methods from existing literature and highlight the benefits of motion primitives.
{"title":"Leveraging Machine Learning for Generating and Utilizing Motion Primitives in Adversarial Environments","authors":"Zachary C. Goddard, Rithesh Rajasekar, Madhumita Mocharla, Garrett Manaster, Kyle Williams, Anirban Mazumdar","doi":"10.2514/1.i011283","DOIUrl":"https://doi.org/10.2514/1.i011283","url":null,"abstract":"Motion primitives enable fast planning for complex and dynamic environments. Adversarial environments pose a particularly challenging and unpredictable scenario. Motion-primitive-based planners have the potential to provide benefit in these types of environments. The key challenge is to design a library of maneuvers that effectively capture the necessary capabilities of the vehicle. This work presents a primitive-based game tree search to solve adversarial games in continuous state and action spaces and applies a reinforcement learning framework to autonomously generate effective primitives for the given task. The results demonstrate the ability of the learning framework to produce maneuvers necessary for competing against adversaries. Furthermore, we propose a method for learning a model to estimate the state-dependent value of each motion primitives and demonstrate how to incorporate this model to increase planning performance under time constraints. Additionally, we compare our primitive-based algorithm against forward simulated methods from existing literature and highlight the benefits of motion primitives.","PeriodicalId":50260,"journal":{"name":"Journal of Aerospace Information Systems","volume":"47 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135016607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As the architecture of aircraft cockpit panels becomes more complicated and more flight data are placed onto the panels, trainee pilots require more time during flight training to learn and comprehend flight information. This problem lengthens flight training time and raises costs. This paper proposes a mechanism for prefetching and pushing flight information to facilitate flight training for trainee pilots. This paper addresses the challenges of a high quantity of data and the chaotic time-series relationship between distinct data in flight sequence data by building a migration workflow model in the aircraft cockpit environment and getting flight data with shorter time intervals. Then the flight data are input into the Multilayer Perceptron Long Short-Term Memory (MLP-LSTM) prediction algorithm, which generates the prompt operation information and prediction information by analyzing the current flight data and predicting flight data of next stage. A case study of the takeoff stage is given. The experimental results of the prediction algorithm are given, which prove that the time-series flight data refined by the migration workflow model and MLP-LSTM algorithm have a better prediction effect compared with the LSTM algorithm.
随着飞机座舱仪表板结构的复杂化，大量的飞行数据被放置在仪表板上，受训飞行员在飞行训练中需要更多的时间来学习和理解飞行信息。这个问题延长了飞行训练时间，增加了成本。本文提出了一种预获取和推送飞行信息的机制，以方便见习飞行员的飞行训练。本文通过建立飞机座舱环境下的迁移工作流模型，以较短的时间间隔获取飞行数据，解决了飞行序列数据量大、不同数据间时间序列关系混乱等问题。然后将飞行数据输入多层感知机长短期记忆(Multilayer Perceptron Long - short - Memory, MLP-LSTM)预测算法，该算法通过分析当前飞行数据并预测下一阶段的飞行数据，生成提示操作信息和预测信息。给出了起飞阶段的实例分析。给出了预测算法的实验结果，证明了迁移工作流模型和MLP-LSTM算法对时间序列飞行数据的预测效果优于LSTM算法。
{"title":"Prefetch and Push Method of Flight Information Based on Migration Workflow","authors":"Tao Xu, Youchao Sun","doi":"10.2514/1.i011197","DOIUrl":"https://doi.org/10.2514/1.i011197","url":null,"abstract":"As the architecture of aircraft cockpit panels becomes more complicated and more flight data are placed onto the panels, trainee pilots require more time during flight training to learn and comprehend flight information. This problem lengthens flight training time and raises costs. This paper proposes a mechanism for prefetching and pushing flight information to facilitate flight training for trainee pilots. This paper addresses the challenges of a high quantity of data and the chaotic time-series relationship between distinct data in flight sequence data by building a migration workflow model in the aircraft cockpit environment and getting flight data with shorter time intervals. Then the flight data are input into the Multilayer Perceptron Long Short-Term Memory (MLP-LSTM) prediction algorithm, which generates the prompt operation information and prediction information by analyzing the current flight data and predicting flight data of next stage. A case study of the takeoff stage is given. The experimental results of the prediction algorithm are given, which prove that the time-series flight data refined by the migration workflow model and MLP-LSTM algorithm have a better prediction effect compared with the LSTM algorithm.","PeriodicalId":50260,"journal":{"name":"Journal of Aerospace Information Systems","volume":"26 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135266574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The calibration of charge coupled device arrays is commonly conducted using dark frames. Nonabsolute calibration techniques only measure the relative response of the detectors. A recent attempt at creating a procedure for calibrating a photodetector using the underlying Poisson nature of the photodetection statistics that relied on a nonlinear model was shown to be successful but was highly susceptible to the readout noise present in the measurement. This effort produced the nonlinear statistical nonuniformity calibration (NLSNUC) algorithm, which demonstrated an ability to better model the output of photodetector array elements than similar techniques that relied on a linear model. In this paper, a modified three-point NLSNUC photocalibration procedure is defined that requires only first and second moments of the measurements and allows the response to be modeled using a nonlinear function over the dynamic range of the detector. The modified NLSNUC technique is applied to image data containing a light source with a known output power. Estimates of the number of photoelectrons measured by the detector are shown to be superior to those obtained by the original NLSNUC algorithm as well as other statistical calibration techniques that do not utilize a calibrated light source.
{"title":"Improved Nonlinear Statistical Photocalibration of Photodetectors Without Calibrated Light Sources","authors":"Stephen C. Cain","doi":"10.2514/1.i011211","DOIUrl":"https://doi.org/10.2514/1.i011211","url":null,"abstract":"The calibration of charge coupled device arrays is commonly conducted using dark frames. Nonabsolute calibration techniques only measure the relative response of the detectors. A recent attempt at creating a procedure for calibrating a photodetector using the underlying Poisson nature of the photodetection statistics that relied on a nonlinear model was shown to be successful but was highly susceptible to the readout noise present in the measurement. This effort produced the nonlinear statistical nonuniformity calibration (NLSNUC) algorithm, which demonstrated an ability to better model the output of photodetector array elements than similar techniques that relied on a linear model. In this paper, a modified three-point NLSNUC photocalibration procedure is defined that requires only first and second moments of the measurements and allows the response to be modeled using a nonlinear function over the dynamic range of the detector. The modified NLSNUC technique is applied to image data containing a light source with a known output power. Estimates of the number of photoelectrons measured by the detector are shown to be superior to those obtained by the original NLSNUC algorithm as well as other statistical calibration techniques that do not utilize a calibrated light source.","PeriodicalId":50260,"journal":{"name":"Journal of Aerospace Information Systems","volume":"875 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135884630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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