Erik Blasch - Academia.edu (original) (raw)

Papers by Erik Blasch

arXiv (Cornell University), May 1, 2018

Due to the progressive expansion of public mobile networks and the dramatic growth of the number ... more Due to the progressive expansion of public mobile networks and the dramatic growth of the number of wireless users in recent years, researchers are motivated to study the radio propagation in urban environments and develop reliable and fast path loss prediction models. During last decades, different types of propagation models are developed for urban scenario path loss predictions such as the Hata model and the COST 231 model. In this paper, the path loss prediction model is thoroughly investigated using machine learning approaches. Different non-linear feature selection methods are deployed and investigated to reduce the computational complexity. The simulation results are provided to demonstratethe validity of the machine learning based path loss prediction engine, which can correctly determine the signal propagation in a wireless urban setting.

To achieve a reliable communication link, a robust radio frequency (RF) communication system shou... more To achieve a reliable communication link, a robust radio frequency (RF) communication system should be designed for the ability to predict the system performance in the intended environment prior to the network deployment is critical. In this paper, a comprehensive method to analyze the capacity of a ground-to-air/air-to-air communication RF links in both urban and rural areas is presented. Communication link analysis performed using a systems-level method provides a better understanding of the ground-to-air/air-to-air communication link, and moreover, the propagation model can be used for other system designs in similar scenarios.

2022 IEEE Aerospace Conference (AERO), Mar 5, 2022

IntechOpen eBooks, Apr 14, 2021

A pursuit-evasion (PE) orbital game approach for space situational awareness (SSA) is presented t... more A pursuit-evasion (PE) orbital game approach for space situational awareness (SSA) is presented to deal with imperfect measurements and informational uncertainties. In the two-sided optimization problem, a pursuer (an observer or sensor) will use the sensor resource to minimize the uncertainty (modeled by entropy) while an evader (a space object being tracked) will maximize it by performing space maneuvers. Since the cost and opportunity cost of sensor resources, pursuer will make decisions on the when to use these resources. The proposed PE approach provides a method to solve the SSA problem, where the evader will exploit the sensing and tracking model to confuse the opponent by corrupting their tracking estimates, while the pursuer wants to efficiently decrease the tracking uncertainties. A numerical simulation scenario with one space based space surveillance (SBSS) satellite as a pursuer and one geosynchronous (GEO) satellite as an evader is simulated to demonstrate the PE orbital game approach. The GEO applies the continuous low-thrust such as the Ion thrust in maneuvers. An add-on module is developed for SGP4/SDP4 algorithms to propagate the satellites with maneuvers. The GEO maneuvering strategies and on-off measurement controls for SBSS are obtained from the Nash equilibrium of the PE game.

Wide Area Motion Imagery (WAMI) are usually taken from unmaned air vehicles at low frame rates, a... more Wide Area Motion Imagery (WAMI) are usually taken from unmaned air vehicles at low frame rates, and having very wide ground coverage. These images serve as rich source for many applications like surveillance, urban planing and traffic monitoring. Thus, understanding WAMI imagery exploitation has been gaining more interest recent years. In this paper, we focus on estimating the pose of vehicles in WAMI imagery. The difficulty of this task lies in that a vehicle only occupies a very small low-contrast region with confusing visual appearance in a WAMI image, which raises a serious problem for conventional approaches based on low-level image cues or priors. In this paper, we tackle this problem by adopting deep learning approach, using deep Convolutional Neural Networks (CNN) to learn the pose of vehicles in WAMI images. The proposed deep convolutional network based pose estimation exceeds baseline by 31.5%. Furthermore, we analyzed the effect of different level of context information on the estimation accuracy.

Acta Astronautica, Oct 1, 2021

Abstract The capability accurately and timely detect a resident space object (RSO) maneuver is a ... more Abstract The capability accurately and timely detect a resident space object (RSO) maneuver is a critical task for monitoring space activities. This paper presents a data-driven Gaussian Binary RSO Maneuver Detection (GaBRSOMD) method to detect whether or not there is a maneuver between two tracks of the same RSO on different orbital paths. Using an in-house simulated space catalog environment, the Gaussian Binary Classification (GBC) method is used to detect three types of maneuvers: the maneuver is due to a small impulsive velocity change; a low thrust, and the maneuver is due to either an impulsive or a low thrust which is unknown a priori. Numerical results demonstrate that the proposed GBC model can achieve high accuracy in detecting all three cases of maneuvers. The paper further demonstrates that the GBC approach is robust to noisy data and has advantages over an AutoEncoder method and the classical Principal Component Analysis (PCA) method. Hence, the proposed GBC has great potential for making quick detection decision with both high accuracy and precision.

Future Internet, Nov 28, 2022

2022 IEEE Aerospace Conference (AERO), Mar 5, 2022

When applying the Disruption Tolerant Networking (DTN) technique to satellite communications (SAT... more When applying the Disruption Tolerant Networking (DTN) technique to satellite communications (SATCOM) with significant long delays, two problems result. First, to enhance the communication efficiency, Performance Enhancing Proxies (PEPs) used in satellite communications need to be integrated with DTN around SATCOM links, and the interoperability between DTN and PEP should be developed. Second, all data moving from a red core (secure intranet) to a black core (unsecured public network) should be encrypted using High Assurance Internet Protocol Encryption (HAIPE) devices. To solve the encryption problem, a TCP over TCP solution was proposed, which encodes original TCP flow information from HAIPE, and then reconstructs new TCP streams and encapsulates HAIPE-encrypted original TCP packets in them. These new TCP streams can be natively handled by PEPs and thus the full TCP performance can be achieved. However, the TCP over TCP solution requires special mechanisms to deal with the interaction between the congestion control of the inner and outer TCP links. To achieve congestion goals, this paper develops a throughput system model, and provides an analysis of the impacts of TCP retransmission. Our analysis shows a throughput reduction when both inner and outer TCP react to packet loss. Possible solutions are also proposed using delay shaping to remove the congestion control of the TCP tunnel. An analysis is provided to explain the mechanisms behind our solutions, and experiment results are also provided to support our design.

ABSTRACT For many operational equipment systems, both reliability and survivability are measures ... more ABSTRACT For many operational equipment systems, both reliability and survivability are measures of effectiveness over the performance of the individual mechanical and electrical parts. Many equipment parts can be modeled individually for their operational reliability performance due to physical constraints. Reliability has traditionally been assessed from physical attacks that result in failures; however, in real-world analysis, there are cases of non-physical attacks. A survivability analysis is an aggregate of the system-level operational sustainability over the reliability of all the components. With the information age, many equipment parts are “intelligent” that include sophisticated reasoning methods that are subject to non-physical cyber attacks. Developing a model that incorporates both the physical and non-physical attacks for reliability and survivability is important for determining system-level effectiveness. For sustained operations, we need to incorporate information fusion over physical and non-physical (e.g. cyber attacks) failures to determine a system's reliability and survivability. In this paper, we develop a method of fusing reliability estimates in both continuous (component model) and discrete analysis (component attack model) for a component survivability analysis.

2022 IEEE Aerospace Conference (AERO), Mar 5, 2022

This paper presents an anti-jamming Global Positioning System (GPS) receiver antenna testing syst... more This paper presents an anti-jamming Global Positioning System (GPS) receiver antenna testing system. The system is composed of a set of six circular rails with different radii that are installed to emulate GPS satellite orbits, a set of GPS antennas each carried by a trolley that can move on the rails to emulate GPS satellites, a trolley movement controller to emulate the GPS satellite constellation propagation, and a multi-channel GPS simulation system that provides GPS signal and GPS satellite state position information. The GPS receiver antenna under test is at the center of the rails. As the GPS antennas carried by trolleys move on the rail to emulate the GPS satellite constellation propagation, the GPS receiver antenna under test receives the emulated GPS signals. The GPS signals’ arrival direction is almost the same as that coming from real GPS satellites. The anti-jamming GPS receiver antenna testing system can emulate a GPS satellite constellation with multiple GPS satellites; with high emulation accuracy (in both GPS signal phase and satellite angular position with respect to the GPS receiver antenna under test); requiring only a single phase calibration at the beginning of each test; and can support a 4 hours test / emulation.

Recent advances in cloud computing pose interesting capabilities for information fusion which hav... more Recent advances in cloud computing pose interesting capabilities for information fusion which have similar requirements of big data computations. With a cloud enabled environment, information fusion systems could be conducted over vast amounts of entities across multiple databases. In order to properly implement information fusion in a cloud, information management, system design, and real-time execution must be considered. In this chapter, three aspects of current developments integrating low/high-level information fusion (LLIF/HLIF) and cloud computing are discussed: (1) agent-based service architectures, (2) ontologies, and (3) metrics (timeliness, confidence, and security). We introduce the Cloud- Enabled Bayes Network (CEBN) for wide area motion imagery target tracking and identification. The Google Fusion Tables service is also selected as a case study to illustrate commercial cloud-based information fusion applications.

The dynamic data-driven applications system (DDDAS) paradigm uses the controlled measurement data... more The dynamic data-driven applications system (DDDAS) paradigm uses the controlled measurement data to update models. When big data streams are available, signal processing can be used for situation awareness. However, many times, the physical world prevents sensor measurements availability which provides an opportunity to use heterogeneous sensor information; but methods are needed for data normalization, sampling alignment, and data mining. In this paper, we highlight a joint nonlinear manifold learning approach to that incorporates advances in machine learning, data fusion, and model-based simulation propagators. The context of the situation monitoring includes a multiple moving targets, a video sensor, and distributed signal sensors. The challenge is to determine the moving emitter amongst a cluttered scene during an experiment with large data streams. Resolving the signature triangulation of the emitter with that of the video sensor is both a nonlinear tracking problem as well as data learning issue. The paper presents the joint nonlinear manifold learning approach that is theoretically developed such sensor models could not only be the tracking scenario; but that of structure health monitoring as well as internet of things (IoT) examples. The theoretical analysis, heterogeneous data set, machine learning, and results are presented to showcase the importance of real-time DDDAS modeling updates of kinematic models.

arXiv (Cornell University), May 1, 2018

Due to the progressive expansion of public mobile networks and the dramatic growth of the number ... more Due to the progressive expansion of public mobile networks and the dramatic growth of the number of wireless users in recent years, researchers are motivated to study the radio propagation in urban environments and develop reliable and fast path loss prediction models. During last decades, different types of propagation models are developed for urban scenario path loss predictions such as the Hata model and the COST 231 model. In this paper, the path loss prediction model is thoroughly investigated using machine learning approaches. Different non-linear feature selection methods are deployed and investigated to reduce the computational complexity. The simulation results are provided to demonstratethe validity of the machine learning based path loss prediction engine, which can correctly determine the signal propagation in a wireless urban setting.

To achieve a reliable communication link, a robust radio frequency (RF) communication system shou... more To achieve a reliable communication link, a robust radio frequency (RF) communication system should be designed for the ability to predict the system performance in the intended environment prior to the network deployment is critical. In this paper, a comprehensive method to analyze the capacity of a ground-to-air/air-to-air communication RF links in both urban and rural areas is presented. Communication link analysis performed using a systems-level method provides a better understanding of the ground-to-air/air-to-air communication link, and moreover, the propagation model can be used for other system designs in similar scenarios.

2022 IEEE Aerospace Conference (AERO), Mar 5, 2022

IntechOpen eBooks, Apr 14, 2021

A pursuit-evasion (PE) orbital game approach for space situational awareness (SSA) is presented t... more A pursuit-evasion (PE) orbital game approach for space situational awareness (SSA) is presented to deal with imperfect measurements and informational uncertainties. In the two-sided optimization problem, a pursuer (an observer or sensor) will use the sensor resource to minimize the uncertainty (modeled by entropy) while an evader (a space object being tracked) will maximize it by performing space maneuvers. Since the cost and opportunity cost of sensor resources, pursuer will make decisions on the when to use these resources. The proposed PE approach provides a method to solve the SSA problem, where the evader will exploit the sensing and tracking model to confuse the opponent by corrupting their tracking estimates, while the pursuer wants to efficiently decrease the tracking uncertainties. A numerical simulation scenario with one space based space surveillance (SBSS) satellite as a pursuer and one geosynchronous (GEO) satellite as an evader is simulated to demonstrate the PE orbital game approach. The GEO applies the continuous low-thrust such as the Ion thrust in maneuvers. An add-on module is developed for SGP4/SDP4 algorithms to propagate the satellites with maneuvers. The GEO maneuvering strategies and on-off measurement controls for SBSS are obtained from the Nash equilibrium of the PE game.

Wide Area Motion Imagery (WAMI) are usually taken from unmaned air vehicles at low frame rates, a... more Wide Area Motion Imagery (WAMI) are usually taken from unmaned air vehicles at low frame rates, and having very wide ground coverage. These images serve as rich source for many applications like surveillance, urban planing and traffic monitoring. Thus, understanding WAMI imagery exploitation has been gaining more interest recent years. In this paper, we focus on estimating the pose of vehicles in WAMI imagery. The difficulty of this task lies in that a vehicle only occupies a very small low-contrast region with confusing visual appearance in a WAMI image, which raises a serious problem for conventional approaches based on low-level image cues or priors. In this paper, we tackle this problem by adopting deep learning approach, using deep Convolutional Neural Networks (CNN) to learn the pose of vehicles in WAMI images. The proposed deep convolutional network based pose estimation exceeds baseline by 31.5%. Furthermore, we analyzed the effect of different level of context information on the estimation accuracy.

Acta Astronautica, Oct 1, 2021

Abstract The capability accurately and timely detect a resident space object (RSO) maneuver is a ... more Abstract The capability accurately and timely detect a resident space object (RSO) maneuver is a critical task for monitoring space activities. This paper presents a data-driven Gaussian Binary RSO Maneuver Detection (GaBRSOMD) method to detect whether or not there is a maneuver between two tracks of the same RSO on different orbital paths. Using an in-house simulated space catalog environment, the Gaussian Binary Classification (GBC) method is used to detect three types of maneuvers: the maneuver is due to a small impulsive velocity change; a low thrust, and the maneuver is due to either an impulsive or a low thrust which is unknown a priori. Numerical results demonstrate that the proposed GBC model can achieve high accuracy in detecting all three cases of maneuvers. The paper further demonstrates that the GBC approach is robust to noisy data and has advantages over an AutoEncoder method and the classical Principal Component Analysis (PCA) method. Hence, the proposed GBC has great potential for making quick detection decision with both high accuracy and precision.

Future Internet, Nov 28, 2022

2022 IEEE Aerospace Conference (AERO), Mar 5, 2022

When applying the Disruption Tolerant Networking (DTN) technique to satellite communications (SAT... more When applying the Disruption Tolerant Networking (DTN) technique to satellite communications (SATCOM) with significant long delays, two problems result. First, to enhance the communication efficiency, Performance Enhancing Proxies (PEPs) used in satellite communications need to be integrated with DTN around SATCOM links, and the interoperability between DTN and PEP should be developed. Second, all data moving from a red core (secure intranet) to a black core (unsecured public network) should be encrypted using High Assurance Internet Protocol Encryption (HAIPE) devices. To solve the encryption problem, a TCP over TCP solution was proposed, which encodes original TCP flow information from HAIPE, and then reconstructs new TCP streams and encapsulates HAIPE-encrypted original TCP packets in them. These new TCP streams can be natively handled by PEPs and thus the full TCP performance can be achieved. However, the TCP over TCP solution requires special mechanisms to deal with the interaction between the congestion control of the inner and outer TCP links. To achieve congestion goals, this paper develops a throughput system model, and provides an analysis of the impacts of TCP retransmission. Our analysis shows a throughput reduction when both inner and outer TCP react to packet loss. Possible solutions are also proposed using delay shaping to remove the congestion control of the TCP tunnel. An analysis is provided to explain the mechanisms behind our solutions, and experiment results are also provided to support our design.

ABSTRACT For many operational equipment systems, both reliability and survivability are measures ... more ABSTRACT For many operational equipment systems, both reliability and survivability are measures of effectiveness over the performance of the individual mechanical and electrical parts. Many equipment parts can be modeled individually for their operational reliability performance due to physical constraints. Reliability has traditionally been assessed from physical attacks that result in failures; however, in real-world analysis, there are cases of non-physical attacks. A survivability analysis is an aggregate of the system-level operational sustainability over the reliability of all the components. With the information age, many equipment parts are “intelligent” that include sophisticated reasoning methods that are subject to non-physical cyber attacks. Developing a model that incorporates both the physical and non-physical attacks for reliability and survivability is important for determining system-level effectiveness. For sustained operations, we need to incorporate information fusion over physical and non-physical (e.g. cyber attacks) failures to determine a system's reliability and survivability. In this paper, we develop a method of fusing reliability estimates in both continuous (component model) and discrete analysis (component attack model) for a component survivability analysis.

2022 IEEE Aerospace Conference (AERO), Mar 5, 2022

This paper presents an anti-jamming Global Positioning System (GPS) receiver antenna testing syst... more This paper presents an anti-jamming Global Positioning System (GPS) receiver antenna testing system. The system is composed of a set of six circular rails with different radii that are installed to emulate GPS satellite orbits, a set of GPS antennas each carried by a trolley that can move on the rails to emulate GPS satellites, a trolley movement controller to emulate the GPS satellite constellation propagation, and a multi-channel GPS simulation system that provides GPS signal and GPS satellite state position information. The GPS receiver antenna under test is at the center of the rails. As the GPS antennas carried by trolleys move on the rail to emulate the GPS satellite constellation propagation, the GPS receiver antenna under test receives the emulated GPS signals. The GPS signals’ arrival direction is almost the same as that coming from real GPS satellites. The anti-jamming GPS receiver antenna testing system can emulate a GPS satellite constellation with multiple GPS satellites; with high emulation accuracy (in both GPS signal phase and satellite angular position with respect to the GPS receiver antenna under test); requiring only a single phase calibration at the beginning of each test; and can support a 4 hours test / emulation.

Recent advances in cloud computing pose interesting capabilities for information fusion which hav... more Recent advances in cloud computing pose interesting capabilities for information fusion which have similar requirements of big data computations. With a cloud enabled environment, information fusion systems could be conducted over vast amounts of entities across multiple databases. In order to properly implement information fusion in a cloud, information management, system design, and real-time execution must be considered. In this chapter, three aspects of current developments integrating low/high-level information fusion (LLIF/HLIF) and cloud computing are discussed: (1) agent-based service architectures, (2) ontologies, and (3) metrics (timeliness, confidence, and security). We introduce the Cloud- Enabled Bayes Network (CEBN) for wide area motion imagery target tracking and identification. The Google Fusion Tables service is also selected as a case study to illustrate commercial cloud-based information fusion applications.

The dynamic data-driven applications system (DDDAS) paradigm uses the controlled measurement data... more The dynamic data-driven applications system (DDDAS) paradigm uses the controlled measurement data to update models. When big data streams are available, signal processing can be used for situation awareness. However, many times, the physical world prevents sensor measurements availability which provides an opportunity to use heterogeneous sensor information; but methods are needed for data normalization, sampling alignment, and data mining. In this paper, we highlight a joint nonlinear manifold learning approach to that incorporates advances in machine learning, data fusion, and model-based simulation propagators. The context of the situation monitoring includes a multiple moving targets, a video sensor, and distributed signal sensors. The challenge is to determine the moving emitter amongst a cluttered scene during an experiment with large data streams. Resolving the signature triangulation of the emitter with that of the video sensor is both a nonlinear tracking problem as well as data learning issue. The paper presents the joint nonlinear manifold learning approach that is theoretically developed such sensor models could not only be the tracking scenario; but that of structure health monitoring as well as internet of things (IoT) examples. The theoretical analysis, heterogeneous data set, machine learning, and results are presented to showcase the importance of real-time DDDAS modeling updates of kinematic models.

– A human presented with a variety of displays is expected to fuse data to obtain information. An... more – A human presented with a variety of displays is expected to fuse data to obtain information. An effective presentation of information would assist the human in fusing data. This paper describes a multisensor-multisource information decision making tool that was designed to augment human cognitive fusion.