With the explosive growth of network technologies, insider attacks have become a major concern to... more With the explosive growth of network technologies, insider attacks have become a major concern to business operations that largely rely on computer networks. To better detect insider attacks that marginally manipulate network traffic over time, and to recover the system from attacks, in this paper we implement a temporal-based detection scheme using the sequential hypothesis testing technique. Two hypothetical states are considered: the null hypothesis that the collected information is from benign historical traffic and the alternative hypothesis that the network is under attack. The objective of such a detection scheme is to recognize the change within the shortest time by comparing the two defined hypotheses. In addition, once the attack is detected, a server migration-based system recovery scheme can be triggered to recover the system to the state prior to the attack. To understand mitigation of insider attacks, a multi-functional web display of the detection analysis was developed for real-time analytic. Experiments using real-world traffic traces evaluate the effectiveness of Detection System and Recovery (DeSyAR) scheme. The evaluation data validates the detection scheme based on sequential hypothesis testing and the server migration-based system recovery scheme can perform well in effectively detecting insider attacks and recovering the system under attack.
With the emergence of ultra-wideband technologies, the confluence of information needs and commun... more With the emergence of ultra-wideband technologies, the confluence of information needs and communication timeliness has posed more challenges to develop design approaches for generating waveforms with characteristics such as high resistance of interference and low probability of interception. In this paper, a signal decomposition-based derivation (SDD) approach for waveform design is proposed by leveraging the controlled variational model decomposition. A source signal with wideband and nonstationary characteristics is used for generating the base waveforms which is band-limited around a center frequency. The waveform design can be performed by combining the base waveforms to obtain the designed wideband waveforms for communication and radar systems. Simulations are conducted to verify the effectiveness of the designed SDD wideband waveform. A typical communication system with additive white Gaussian noise (AWGN) channel is simulated with the SDD wideband waveform design. The results show that generated waveform with the SDD approach can be used for secure communications.
Space object classification is desired for space situational awareness to be able to discern resi... more Space object classification is desired for space situational awareness to be able to discern resident space object (RSO) characteristics, behaviors, and perspective changes. Due to the limited sensing resources and observations, it is challenging for space object classification to be responsive to unfolding and unexpected events. Many machine learning algorithms are already used to classify space objects based on various sensor observations from radar and telescope. In this paper, the use of deep neural networks (DNN) is proposed to classify space objects due to DNN robust performance in many classification tasks, such as face recognition and object recognition. This paper explores DNN using light curve data. Conventional classification algorithms, such as k nearest neighbor (k-NN), are implemented and compared to the proposed DNN based classification algorithms, including the popular convolutional neural network (CNN) and the recurrent neural network (RNN), in terms of accuracy. Inherent advantages and disadvantages of the deep neural network based classification algorithms are summarized and the potential for future space object classification tasks is analyzed and postulated.
Space superiority requires space protection and space situational awareness (SSA), which rely on ... more Space superiority requires space protection and space situational awareness (SSA), which rely on rapid and accurate space object behavioral and operational intent discovery. The presence of adversaries in addition to real-time and hidden information constraints greatly complicates the decision-making process in controlling both ground-based and spacebased surveillance assets. This paper develops and implements a solution called Adaptive Markov Inference Game Optimization (AMIGO) for rapid discovery of satellite behaviors. AMIGO is an adaptive feedback game theoretic approach. AMIGO gets information from sensors about the relations between the resident space objects (RSOs) of interest and ground and space surveillance assets (GSAs). The relations are determined by both the RSOs and GSAs. Therefore, AMIGO represents the situation as a game instead of a control problem. The game reasoning utilizes data level fusion, stochastic modeling/propagation, and RSO detection/tracking to predict the future RSOs-GSAs relations. The game engine also supports optional space pattern dictionary/semantic rules for adaptive transition matrices in the Markov game. If no existing pattern dictionary is available, AMIGO builds an initial one and revises it during the game reasoning. The outputs of the AMIGO reasoning include two kinds of control methods: processing of GSA measurements and localization of RSOs. The two sets form a game equilibrium, one for surveillance asset management and the other for the estimation of RSO behaviors. Numerical simulations and visualizations demonstrate the performance of AMIGO.
The paper presents a novel directional mesh network (DMN) design that can distribute the limited ... more The paper presents a novel directional mesh network (DMN) design that can distribute the limited radio spectrum resources more efficiently for a DMN by applying artificial intelligence machine learning (ML) techniques. The proposed DMN framework analyzes time-sensitive signal data close to the signal source using fog computing with different types of ML techniques. Depending on the computational capabilities of the fog nodes, different feature extraction methods such as energy detection, match filter, and cyclostationary detection are selected to optimize spectrum allocation. The proposed system also takes the antenna power gain into consideration, which can further reduce probability of detection and interference of the DMN system. Also, the computing nodes send the periodic signal summary which is much smaller than the original signal to the cloud so that the overall system spectrum source allocation strategies are dynamically updated. Instead of just detecting the spectrum holes for secondary users to transmit the signal, the proposed system can optimize the signal transmission path from the cloud to the end user under the interference and relay constraints. The distributed nodes can further improve the strategy based on the sensing information from the fog. Applying fog computing, the system is more adaptive to the local environment and robust to spectrum changes. It will significantly improve the network reliability, resiliency, and flexibility. Designing the proposed system doesn't necessary need change much of the current communication network platform. As most of the signal data is processed at the fog level, it further strengthens the system security by reducing the communication burden of the communications network.
The dynamic data-driven applications systems (DDDAS) paradigm is meant to inject measurements int... more The dynamic data-driven applications systems (DDDAS) paradigm is meant to inject measurements into the execution model for enhanced systems performance. One area off interest in DDDAS is for space situation awareness (SSA). For SSA, data is collected about the space environment to determine object motions, environments, and model updates. Dynamically coupling between the data and models enhances the capabilities of each system by complementing models with data for system control, execution, and sensor management. The paper overviews some of the recent developments in SSA made possible from DDDAS techniques which are for object detection, resident space object tracking, atmospheric models for enhanced sensing, cyber protection, and information management.
MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM)
The spectrum sharing problems call for the solutions to compress the uplink space-ground link sys... more The spectrum sharing problems call for the solutions to compress the uplink space-ground link system (SGLS) spectrum and minimize the interference to the neighboring bandwidth. In this paper, single sideband (SSB) and double-sideband (DSB) enabled hybrid waveforms are proposed to reduce the bandwidth and improve the spectrum efficiency of SGLS. A hybrid modulation (HM) structure is designed to generate the double sideband command signals and single sideband telemetry and ranging signals. At the receiver side, a Costas loop-based carrier synchronization supports the demodulation of hybrid waveforms. Numerical results show the implementation losses associated with the hybrid modulation-demodulation processes are negligible. The hybrid waveforms cut the required bandwidth in half, while giving essentially the same bit error rate (BER) performance as traditional DSB waveforms.
The number of sensors for space situational awareness is limited, while a large number of space o... more The number of sensors for space situational awareness is limited, while a large number of space objects are in the catalogue. To efficiently and flexibly use a limited number of sensors, we propose to use the consensus-based distributed sensor management (CSM) algorithm for space object tracking. With CSM, information exchange is only required between neighbors, which avoids the limitations in centralized sensor management algorithms. The auction algorithm is used to select tracking tasks and the consensus algorithm is used to achieve agreements between different sensors. Typical scenarios, which use multiple Electro-Optical (EO) sensors to track multiple objects, are used to demonstrate the effectiveness of the proposed consensus-based auction algorithm (CBAA) algorithm.
Sensors and Systems for Space Applications XII, 2019
Advancements in artificial intelligence, information communication, and systems design are potent... more Advancements in artificial intelligence, information communication, and systems design are potential for autonomous systems emerging for space situation awareness (SSA) architectures. Examples of architecture designs are autonomy in motion (AIM) for dynamic data assessment systems (e.g., robotics) and autonomy at rest (AAR) for static data collection systems (e.g., surveillance). However, there is a need for data architectures which are tailored to the SSA missions, which necessitates autonomy in use (AIU). AIU requires pragmatic use of message passing and data flow architectures, contextual and theoretic modeling, and user and information fusion. Information fusion provides methods for data aggregation, correlation, and temporal assessment and awareness. Together, AIU accesses the dynamic data for autonomy in change (AIC), information fusion from AAR in order to make AIM real-time decisions. The paper discusses issues for space situation awareness directions focusing on autonomy in use.
Synchronization plays an important role in wireless communication systems when tracking a phase-s... more Synchronization plays an important role in wireless communication systems when tracking a phase-shift keying (PSK) signal, especially when the initial frequency error is comparable to the loop bandwidth. In order to improve frequency acquisition, an automatic frequency control (AFC) augmentation is used. This paper presents a composite AFC/Costas loop by combining both the AFC loop with a phase-locked loop (PLL) Costas loop for carrier frequency recovery. Therefore, pull-in from both frequency and phase errors is feasible using the composite AFC/Costas loop. The AFC/Costas loop combination filter coefficient setting is evaluated by a theoretic analysis. Improved frequency and phase acquisition can be realized by changing the first order AFC/Costas loop to the second order. First, the structure of the composite AFC/Costas loop is shown. This structure makes use of phase detectors to obtain the phase differences between the received signal and reference signal, where the phase differences can be used to generate the phase and frequency control signals. Difference equations are proposed to describe the composite AFC/Costas loop. Then, the theoretic analysis for both frequency and phase control are derived in a linearized model of the composite loop. The phase error variance is simulated to show the performance of the composite AFC/Costas loop. Moreover, the frequency and phase synchronization performance for different signal to noise ratio (SNR) and loop filter bandwidth are shown to demonstrate the effectiveness of the composite AFC/Costas loop. Finally, the Quadrature Phase Shift Keying (QPSK) data is demodulated and decoded through the composite AFC/Costas loop. Extensive simulations are implemented to show that the demodulated data matches the transmitted data, which proves that differential QPSK can effectively reduce the phase ambiguity and increase frequency pull-in range, especially for the low SNR region (Eb/N0 < 3 dB). The proposed composite AFC/Costas loop sheds insights on the design of frequency and phase synchronization in wireless communication systems.
2015 18th International Conference on Information Fusion (Fusion), 2015
With the rapid development of sensor technology, multiple sensors are often available in many eng... more With the rapid development of sensor technology, multiple sensors are often available in many engineering applications, such as space object tracking. How to effectively use multiple sensor information is the key to achieving accurate space object state information. In this paper, the information weighted consensus (IWC) strategy is deployed to solve the cooperative sensor tracking problem. In addition, the information theoretic method and the repeated consensus, are used to detect a malfunctioned sensor and overcome the problem of noisy links in the cooperative tracking scenario. The proposed algorithms are demonstrated by a typical space object tracking problem using multiple sensors. The results indicate that: 1) The proposed algorithms can obtain stable estimation results when there is malfunctioned node in the network, 2) can mitigate the effect of noisy links, and 3) achieves close performance to the result with perfect communication links and known malfunctioned node. The res...
With the explosive growth of network technologies, insider attacks have become a major concern to... more With the explosive growth of network technologies, insider attacks have become a major concern to business operations that largely rely on computer networks. To better detect insider attacks that marginally manipulate network traffic over time, and to recover the system from attacks, in this paper we implement a temporal-based detection scheme using the sequential hypothesis testing technique. Two hypothetical states are considered: the null hypothesis that the collected information is from benign historical traffic and the alternative hypothesis that the network is under attack. The objective of such a detection scheme is to recognize the change within the shortest time by comparing the two defined hypotheses. In addition, once the attack is detected, a server migration-based system recovery scheme can be triggered to recover the system to the state prior to the attack. To understand mitigation of insider attacks, a multi-functional web display of the detection analysis was developed for real-time analytic. Experiments using real-world traffic traces evaluate the effectiveness of Detection System and Recovery (DeSyAR) scheme. The evaluation data validates the detection scheme based on sequential hypothesis testing and the server migration-based system recovery scheme can perform well in effectively detecting insider attacks and recovering the system under attack.
With the emergence of ultra-wideband technologies, the confluence of information needs and commun... more With the emergence of ultra-wideband technologies, the confluence of information needs and communication timeliness has posed more challenges to develop design approaches for generating waveforms with characteristics such as high resistance of interference and low probability of interception. In this paper, a signal decomposition-based derivation (SDD) approach for waveform design is proposed by leveraging the controlled variational model decomposition. A source signal with wideband and nonstationary characteristics is used for generating the base waveforms which is band-limited around a center frequency. The waveform design can be performed by combining the base waveforms to obtain the designed wideband waveforms for communication and radar systems. Simulations are conducted to verify the effectiveness of the designed SDD wideband waveform. A typical communication system with additive white Gaussian noise (AWGN) channel is simulated with the SDD wideband waveform design. The results show that generated waveform with the SDD approach can be used for secure communications.
Space object classification is desired for space situational awareness to be able to discern resi... more Space object classification is desired for space situational awareness to be able to discern resident space object (RSO) characteristics, behaviors, and perspective changes. Due to the limited sensing resources and observations, it is challenging for space object classification to be responsive to unfolding and unexpected events. Many machine learning algorithms are already used to classify space objects based on various sensor observations from radar and telescope. In this paper, the use of deep neural networks (DNN) is proposed to classify space objects due to DNN robust performance in many classification tasks, such as face recognition and object recognition. This paper explores DNN using light curve data. Conventional classification algorithms, such as k nearest neighbor (k-NN), are implemented and compared to the proposed DNN based classification algorithms, including the popular convolutional neural network (CNN) and the recurrent neural network (RNN), in terms of accuracy. Inherent advantages and disadvantages of the deep neural network based classification algorithms are summarized and the potential for future space object classification tasks is analyzed and postulated.
Space superiority requires space protection and space situational awareness (SSA), which rely on ... more Space superiority requires space protection and space situational awareness (SSA), which rely on rapid and accurate space object behavioral and operational intent discovery. The presence of adversaries in addition to real-time and hidden information constraints greatly complicates the decision-making process in controlling both ground-based and spacebased surveillance assets. This paper develops and implements a solution called Adaptive Markov Inference Game Optimization (AMIGO) for rapid discovery of satellite behaviors. AMIGO is an adaptive feedback game theoretic approach. AMIGO gets information from sensors about the relations between the resident space objects (RSOs) of interest and ground and space surveillance assets (GSAs). The relations are determined by both the RSOs and GSAs. Therefore, AMIGO represents the situation as a game instead of a control problem. The game reasoning utilizes data level fusion, stochastic modeling/propagation, and RSO detection/tracking to predict the future RSOs-GSAs relations. The game engine also supports optional space pattern dictionary/semantic rules for adaptive transition matrices in the Markov game. If no existing pattern dictionary is available, AMIGO builds an initial one and revises it during the game reasoning. The outputs of the AMIGO reasoning include two kinds of control methods: processing of GSA measurements and localization of RSOs. The two sets form a game equilibrium, one for surveillance asset management and the other for the estimation of RSO behaviors. Numerical simulations and visualizations demonstrate the performance of AMIGO.
The paper presents a novel directional mesh network (DMN) design that can distribute the limited ... more The paper presents a novel directional mesh network (DMN) design that can distribute the limited radio spectrum resources more efficiently for a DMN by applying artificial intelligence machine learning (ML) techniques. The proposed DMN framework analyzes time-sensitive signal data close to the signal source using fog computing with different types of ML techniques. Depending on the computational capabilities of the fog nodes, different feature extraction methods such as energy detection, match filter, and cyclostationary detection are selected to optimize spectrum allocation. The proposed system also takes the antenna power gain into consideration, which can further reduce probability of detection and interference of the DMN system. Also, the computing nodes send the periodic signal summary which is much smaller than the original signal to the cloud so that the overall system spectrum source allocation strategies are dynamically updated. Instead of just detecting the spectrum holes for secondary users to transmit the signal, the proposed system can optimize the signal transmission path from the cloud to the end user under the interference and relay constraints. The distributed nodes can further improve the strategy based on the sensing information from the fog. Applying fog computing, the system is more adaptive to the local environment and robust to spectrum changes. It will significantly improve the network reliability, resiliency, and flexibility. Designing the proposed system doesn't necessary need change much of the current communication network platform. As most of the signal data is processed at the fog level, it further strengthens the system security by reducing the communication burden of the communications network.
The dynamic data-driven applications systems (DDDAS) paradigm is meant to inject measurements int... more The dynamic data-driven applications systems (DDDAS) paradigm is meant to inject measurements into the execution model for enhanced systems performance. One area off interest in DDDAS is for space situation awareness (SSA). For SSA, data is collected about the space environment to determine object motions, environments, and model updates. Dynamically coupling between the data and models enhances the capabilities of each system by complementing models with data for system control, execution, and sensor management. The paper overviews some of the recent developments in SSA made possible from DDDAS techniques which are for object detection, resident space object tracking, atmospheric models for enhanced sensing, cyber protection, and information management.
MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM)
The spectrum sharing problems call for the solutions to compress the uplink space-ground link sys... more The spectrum sharing problems call for the solutions to compress the uplink space-ground link system (SGLS) spectrum and minimize the interference to the neighboring bandwidth. In this paper, single sideband (SSB) and double-sideband (DSB) enabled hybrid waveforms are proposed to reduce the bandwidth and improve the spectrum efficiency of SGLS. A hybrid modulation (HM) structure is designed to generate the double sideband command signals and single sideband telemetry and ranging signals. At the receiver side, a Costas loop-based carrier synchronization supports the demodulation of hybrid waveforms. Numerical results show the implementation losses associated with the hybrid modulation-demodulation processes are negligible. The hybrid waveforms cut the required bandwidth in half, while giving essentially the same bit error rate (BER) performance as traditional DSB waveforms.
The number of sensors for space situational awareness is limited, while a large number of space o... more The number of sensors for space situational awareness is limited, while a large number of space objects are in the catalogue. To efficiently and flexibly use a limited number of sensors, we propose to use the consensus-based distributed sensor management (CSM) algorithm for space object tracking. With CSM, information exchange is only required between neighbors, which avoids the limitations in centralized sensor management algorithms. The auction algorithm is used to select tracking tasks and the consensus algorithm is used to achieve agreements between different sensors. Typical scenarios, which use multiple Electro-Optical (EO) sensors to track multiple objects, are used to demonstrate the effectiveness of the proposed consensus-based auction algorithm (CBAA) algorithm.
Sensors and Systems for Space Applications XII, 2019
Advancements in artificial intelligence, information communication, and systems design are potent... more Advancements in artificial intelligence, information communication, and systems design are potential for autonomous systems emerging for space situation awareness (SSA) architectures. Examples of architecture designs are autonomy in motion (AIM) for dynamic data assessment systems (e.g., robotics) and autonomy at rest (AAR) for static data collection systems (e.g., surveillance). However, there is a need for data architectures which are tailored to the SSA missions, which necessitates autonomy in use (AIU). AIU requires pragmatic use of message passing and data flow architectures, contextual and theoretic modeling, and user and information fusion. Information fusion provides methods for data aggregation, correlation, and temporal assessment and awareness. Together, AIU accesses the dynamic data for autonomy in change (AIC), information fusion from AAR in order to make AIM real-time decisions. The paper discusses issues for space situation awareness directions focusing on autonomy in use.
Synchronization plays an important role in wireless communication systems when tracking a phase-s... more Synchronization plays an important role in wireless communication systems when tracking a phase-shift keying (PSK) signal, especially when the initial frequency error is comparable to the loop bandwidth. In order to improve frequency acquisition, an automatic frequency control (AFC) augmentation is used. This paper presents a composite AFC/Costas loop by combining both the AFC loop with a phase-locked loop (PLL) Costas loop for carrier frequency recovery. Therefore, pull-in from both frequency and phase errors is feasible using the composite AFC/Costas loop. The AFC/Costas loop combination filter coefficient setting is evaluated by a theoretic analysis. Improved frequency and phase acquisition can be realized by changing the first order AFC/Costas loop to the second order. First, the structure of the composite AFC/Costas loop is shown. This structure makes use of phase detectors to obtain the phase differences between the received signal and reference signal, where the phase differences can be used to generate the phase and frequency control signals. Difference equations are proposed to describe the composite AFC/Costas loop. Then, the theoretic analysis for both frequency and phase control are derived in a linearized model of the composite loop. The phase error variance is simulated to show the performance of the composite AFC/Costas loop. Moreover, the frequency and phase synchronization performance for different signal to noise ratio (SNR) and loop filter bandwidth are shown to demonstrate the effectiveness of the composite AFC/Costas loop. Finally, the Quadrature Phase Shift Keying (QPSK) data is demodulated and decoded through the composite AFC/Costas loop. Extensive simulations are implemented to show that the demodulated data matches the transmitted data, which proves that differential QPSK can effectively reduce the phase ambiguity and increase frequency pull-in range, especially for the low SNR region (Eb/N0 < 3 dB). The proposed composite AFC/Costas loop sheds insights on the design of frequency and phase synchronization in wireless communication systems.
2015 18th International Conference on Information Fusion (Fusion), 2015
With the rapid development of sensor technology, multiple sensors are often available in many eng... more With the rapid development of sensor technology, multiple sensors are often available in many engineering applications, such as space object tracking. How to effectively use multiple sensor information is the key to achieving accurate space object state information. In this paper, the information weighted consensus (IWC) strategy is deployed to solve the cooperative sensor tracking problem. In addition, the information theoretic method and the repeated consensus, are used to detect a malfunctioned sensor and overcome the problem of noisy links in the cooperative tracking scenario. The proposed algorithms are demonstrated by a typical space object tracking problem using multiple sensors. The results indicate that: 1) The proposed algorithms can obtain stable estimation results when there is malfunctioned node in the network, 2) can mitigate the effect of noisy links, and 3) achieves close performance to the result with perfect communication links and known malfunctioned node. The res...
—In this paper, spectral unmixing methods, which are extensively used in hyperspectral imaging ar... more —In this paper, spectral unmixing methods, which are extensively used in hyperspectral imaging area, are proposed for classification and abundance fraction (concentration) estimation of chemical and biological agents that exist in the mixture form. Several government-furnished datasets, which were collected through the infrared spectrum method, were thoroughly analyzed. Two similarity measures—the spectral angle mapper and spectral information divergence—were investigated in order to provide a quantitative comparison basis with respect to the performance of the applied spectral unmixing methods in the existence of similar and distinct agents. The use of the similarity measures provided valuable information about the signature characteristics of the agents, which led to a better understanding about the capabilities of the investigated methods. The orthogonal subspace projection (OSP) method was investigated as the first unmixing, classification , and abundance estimation technique. It was observed that the OSP method provided good results when the number of agents in the database was small and was composed of distinct agents. However, when the number of agents was incremented by adding agents that share similar characteristics, the abundance estimation accuracy gradually degraded in addition to generating negative abundance fraction estimates. The second investigated unmixing method was called nonnegatively constrained least squares (NCLS). The results and analyses indicated that the NCLS method outperformed the OSP approach by providing considerably more accurate fraction estimates while at the same time not generating any negative fraction estimates; thus, the use of the NCLS method was found to be promising in detection and abundance fraction estimation of chemical and biological agents that exist in the form of mixtures. In addition, efficient implementation of NCLS has resulted in much lower computations than the conventional OSP implementation. Index Terms—Biological agent detection, chemical agent detection , nonnegatively constrained least squares (NCLS), orthogonal subspace projection (OSP).
The strategy of data fusion has been applied in threat prediction and situation awareness. The te... more The strategy of data fusion has been applied in threat prediction and situation awareness. The terminology has been standardized by the Joint Directors of Laboratories (JDL) in the form of a so-called " JDL Data-Fusion Model. " Higher levels of the model call for prediction of future development and awareness of the development of a situation. It is known that the Bayesian Network is an insightful approach to determine optimal strategies against an asymmetric adversarial opponent. However, it lacks the essential ad-versarial decision processes perspective. In this paper, a data-fusion approach for asymmetric-threat detection and prediction based on advanced knowledge infrastructure and stochastic (Markov) game theory is proposed. Asymmetric and adaptive threats are detected and grouped by intelligent agent and Hierarchical Entity Aggrega-tion in level-two fusion and their intents are predicted by a decentralized Markov (stochastic) game model with deception in level-three fusion. We have evaluated the feasibility of the advanced data fusion algorithm and its effectiveness through extensive simulations.
— In this paper, we compare several nonlinear filtering methods, namely, extended Kalman filter (... more — In this paper, we compare several nonlinear filtering methods, namely, extended Kalman filter (EKF), unscented filter (UF), particle filter (PF), and linear minimum mean square error (LMMSE) filter for a ballistic target tracking problem. We cast EKF and UF into a general linear recur-sive estimation framework and reveal their pros and cons. We pinpoint using the LMMSE filter for possible analytical solutions rather than starting with approximations such as system linearization or unscented transform. We compare the performance of EKF, UF, LMMSE filter and Gaussian PF for a ballistic target tracking problem. The estimation accuracy is also compared with the posterior Cramer-Rao lower bound (PCRLB). Our simulation results confirm that the LMMSE filter outperforms EKF and UF in terms of tracking accuracy, filter credibility and robustness against the sensitivity to filter initial condition. Its accuracy is slightly worse than that of Gaussian PF but with much lower computational load. We conclude that the LMMSE filter is preferred for the ballistic target tracking problem being studied.
For ballistic target tracking using radar measurements in the polar or spherical coordinates, var... more For ballistic target tracking using radar measurements in the polar or spherical coordinates, various nonlinear filters have been studied. Previous work often assumes that the ballistic coefficient of a missile target is known to the filter, which is unrealistic in practice. In this paper, we study the ballistic target tracking problem with unknown ballistic coefficient. We propose a general scheme to handle nonlinear systems with a nuisance parameter. The interacting multiple model (IMM) algorithm is employed and for each model the linear minimum mean square error (LMMSE) filter is used. Although we assume that the nuisance parameter is random and time invariant, our approach can be extended to time varying case. A useful property of the model transition probability matrix (TPM) is studied which provides a viable way to tune the model probability. In simulation studies, we illustrate the design of the TPM and compare the proposed method with another two IMM-based algorithms where the extended Kalman filter (EKF) and the unscented filter (UF) are used for each model, respectively. We conclude that the IMM-LMMSE filter is preferred for the problem being studied.
The strategy of data fusion has been applied in threat prediction and situation awareness and the... more The strategy of data fusion has been applied in threat prediction and situation awareness and the terminology has been standardized by the Joint Directors of Laboratories (JDL) in the form of a so-called JDL Data Fusion Model, which currently called DFIG model. Higher levels of the DFIG model callfor prediction offuture development and awareness of the development of a situation. It is known that Bayesian Network is an insightful approach to determine optimal strategies against asymmetric adversarial opponent. However, it lacks the essential adversarial decision processes perspective. In this paper, a highly innovative data-fusion framework for asymmetric-threat detection and prediction based on advanced knowledge infrastructure and stochastic (Markov) game theory is proposed. In particular, asymmetric and adaptive threats are detected and grouped by intelligent agent and Hierarchical Entity Aggregation in Level 2 and their intents are predicted by a decentralized Markov (stochastic) game model with deception in Level 3. We have verified that our proposed algorithms are scalable, stable, and perform satisfactorily according to the situation awareness performance metric.
It is well known that civilians often play an active role in wars. That is, they are not just pas... more It is well known that civilians often play an active role in wars. That is, they are not just passively static but might purposefully take actions to help one side in a battle to minimize their losses or achieve some political purpose. Unfortunately, existing game theoretic models usually do not consider this situation, even though collateral damage has been considered in a paper on a two-player game model. In this paper, a three-player attrition-type discrete time dynamic game model is formulated, in which there are two opposing forces and one civilian player that might be either neutral or slightly biased. We model the objective functions, control strategies of different players, and identify the associated constraints on the control and state variables. Existing attrition-like state space models can be regarded as a special case of the model proposed in this paper. An example scenario and extensive simulations illustrate possible applications of this model, and comparative discussions further clarify the benefits.
In this paper, we first propose a general architecture for path planning and mission planning for... more In this paper, we first propose a general architecture for path planning and mission planning for multiple unmanned platforms that may include aircraft and robots. Second, a novel path planning method based on Pareto Foraging has been implemented and evaluated. Our Pareto solution serves as a reference trajectory for the Foraging algorithm, which further refines the reference path. Third, extensive experiments and comparative studies have been carried out. In particular, we compared our algorithm with the Voronoi diagram and Dijkstra's algorithm
Coordinated mission planning is one of the core steps to effectively exploit the capabilities of ... more Coordinated mission planning is one of the core steps to effectively exploit the capabilities of cooperative control of multiple UAVs. In this paper, we develop an effective team composition and tasking mechanism and an optimal team dynamics and tactics algorithm for mission planning under a hierarchical game theoretic framework. Our knowledge/experience based on static non-cooperative and non-zero Nash games are used for team composition and tasking to schedule tasks at the mission level and allocate resources associated with these tasks. Our event based dynamic non-cooperative (Nash) game is used for team dynamics and tactics to assign targets and decide the optimal salvo size for each aerial platform to achieve the minimum remaining platforms of red and the maximum remaining platforms of Blue at the end of a battle. A cooperative jamming deployment method has been developed to maximize the total probability of survival of Blue aerial platforms. A simulation software package has been developed with connectivity to the Boeing OEP (Open Experimental Platform) to demonstrate the performance of our proposed algorithms. Simulations have verified that our proposed algorithms are scalable, stable, and satisfactory in performance.
—In an adversarial military environment, it is important to efficiently and promptly predict the ... more —In an adversarial military environment, it is important to efficiently and promptly predict the enemy's tactical intent from lower level spatial and temporal information. In this paper, we propose a decentralized Markov game (MG) theoretic approach to estimate the belief of each possible enemy Course of Action (ECOA), which is utilized to model the adversary intents. It has the following advantages: 1) It is decentralized. Each cluster or team makes decisions mostly based on local information. We put more autonomies in each group allowing for more flexibilities; 2) A Markov Decision Process (MDP) can effectively model the uncertainties in the noisy military environment; 3) It is a game model with three players: red force (enemies), blue force (friendly forces), and white force (neutral objects); 4) Correlated-Q Reinforcement Learning is integrated. With the consideration that actual value functions are not normally known and they must be estimated, we integrate correlated-Q learning concept in our game approach to dynamically adjust the payoffs function of each player. A simulation software package has been developed to demonstrate the performance of our proposed algorithms. Simulations have verified that our proposed algorithms are scalable, stable, and satisfactory in performance. 1 2
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Papers by Genshe Chen