Genshe Chen

Intelligent Fusion Technology, Inc.

This chapter describes an innovative design and implementation approach of a ground-based pre-distorter framework using machine learning and artificial intelligence (ML-AI) technology for high power amplifier (HPA) pre-distortion. The ML-AI technology enabler proposed is a combined multi-objective reinforce learning-and-adaptive neural network (MOR...

In modern security situations, tracking multiple human objects in real-time within challenging urban environments is a critical capability for enhancing situational awareness, minimizing response time, and increasing overall operational effectiveness. Tracking multiple entities enables informed decision-making, risk mitigation, and the safeguarding...

In modern security situations, tracking multiple human objects in real-time within challenging urban environments is a critical capability for enhancing situational awareness, minimizing response time, and increasing overall operational effectiveness. Tracking multiple entities enables informed decision-making, risk mitigation, and the safeguarding...

This paper presents a proportional-integral-derivative (PID)-based automatic gain control (AGC) approach for satellite communications attacked by partial-time partial-band additive white Gaussian noise (AWGN) jamming. The analysis based on the stochastic model predictive control (SMPC) shows that the AGC performance depends on the accurate characte...

Low Probability of Intercept (LPI) radar waveform recognition is one of the crucial functions in the electronic intelligence systems. Advances in artificial intelligence promote the performance of the LPI waveform recognition with various signal features defined with analytical expressions. However, noisy LPI waveform recognition is still a challen...

One of the main challenges for the safety validation of autonomous driving vehicles lies in the influence of weather phenomena. As each of the main sensors, namely LIDAR, radar, and cameras increases its sensitivity to detect smaller objects faster and hence be able to drive autonomously at higher speeds, the possible influence of environmental per...

Rapid advancements in the fifth generation (5G) communication technology and mobile edge computing (MEC) paradigm have led to the proliferation of unmanned aerial vehicles (UAV) in urban air mobility (UAM) networks, which provide intelligent services for diversified smart city scenarios. Meanwhile, the widely deployed Internet of drones (IoD) in sm...

The rapid development of three-dimensional (3D) acquisition technology based on 3D sensors provides a large volume of data, which are often represented in the form of point clouds. Point cloud representation can preserve the original geometric information along with associated attributes in a 3D space. Therefore, it has been widely adopted in many...

The rapid development of three-dimensional (3D) acquisition technology based on 3D sensors provides a large volume of data, which is often represented in the form of point clouds. Point cloud representation can preserve the original geometric information along with associated attributes in a 3D space. Therefore, it has been widely adopted in many s...

Rapid advancements in the fifth generation (5G) communication technology and mobile edge computing (MEC) paradigm lead to the proliferation of unmanned aerial vehicles (UAV) in urban air mobility (UAM) networks, which provide intelligent services for diversified smart city scenarios. Meanwhile, the widely deployed internet of drones (IoD) in smart...

Named Data Networking (NDN) is a new data centered networking paradigm. Unlike internet Protocol (IP) based networking that relies on end-to-end network connection over communication networks, NDN is data centered. NDN data packets with unique names can be stored at any network nodes, and retrieved from the network with corresponding interest packe...

The Dynamic Data Driven Applications System (DDDAS) paradigm incorporates forward estimation with inverse modeling, augmented with contextual information. For cooperative infrared (IR) and radio-frequency (RF) based automatic target detection and recognition (ATR) systems, the advantages of multimodal sensing and machine learning (ML) enhance real-...

Space protection and SSA require rapid and accurate space object behavioral and operational intent discovery. The problem of behaviorally evasive intent identification is challenging and complicated. The satellite maneuver detection and classification is the first step of space behavior discovery. With exiting capabilities based on anomality detect...

This review provides a comprehensive review of past and existing works on 5G systems with a laser focus on 5G Satellite Integration (SATis5) for commercial and defense applications. The holistic survey approach is used to gain an in-depth understanding of 5G-Terrestrial Network (5G-TN), 5G-Non-Terrestrial Network (5G-NTN), SATis5 testbeds, and proj...

Distributed sparse arrays, consisting of multiple subarrays, facilitate a higher number of degrees of freedom and enhanced direction-of-arrival (DOA) estimation performance beyond what is offered by single uniform linear arrays. When the array elements in each subarray are sparsely located, the covariance matrix is sparse with missing entries. Cova...

Given the significant technological advances over the past few years, autonomous vehicles are gradually entering the industrialization stage. Light detection and ranging (LiDAR) sensors are seeing increased use in autonomous vehicles. However, the final implementation of the technology remains undetermined because major automotive manufacturers hav...

Light detection and ranging (Lidar) imaging systems are being increasingly used in autonomous vehicles. However, the final technology implementation is still undetermined as major automotive manufacturers are only starting to select providers for data collection units that can be introduced in commercial vehicles. Currently, testing for autonomous...

To detect anomaly for RSOs accurately and timely is critical to protect the long-term sustainability of space activities, including Space Situational Awareness (SSA) and Space Traffic Management (STM). In this paper, we explore a new data-driven framework based on deep autoencoder for RSOs' anomaly detection. A novel two-input autoencoder model is...

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-hous...

Mobile Edge Computing (MEC) is a key technology to support the emerging low-latency Internet of Things (IoT) applications. With computing servers deployed at the network edge, the computational tasks generated by mobile users can be offloaded to these MEC servers and executed there with low latency. Meanwhile, with the ever-increasing number of mob...

Current Artificial Intelligence (AI) machine learning approaches perform well with similar sensors for data collection, training, and testing. The ability to learn and analyze data from multiple sources would enhance capabilities for Artificial Intelligence (AI) systems. This paper presents a deep learning-based multi-source self-correcting approac...

Motion imagery interpretability is commonly represented by the Video National Imagery Interpretability Rating Scale (VNIIRS), which is a subjective metric based on human analysts’ visual assessment. Therefore, VNIIRS is a very time-consuming task. This paper presents the development of a fully automated motion imagery interpretability prediction, c...

Space situational awareness (SSA) is needed to control satellite movement and space pervasiveness, which relies on quick and precise space object behavioral classification and discovery. Perhaps the biggest obstacle in adopting machine learning (ML) techniques and evaluating their performance in SSA applications is the lack of large, labeled datase...

The chapter presents a game theoretic training model enabling a deep learning solution for rapid discovery of satellite behaviors from collected sensor data. The solution has two parts, namely, Part 1 and Part 2. Part 1 is a PE game model that enables data augmentation method, and Part 2 uses convolutional neural networks (CNNs) for satellite behav...

Advancement in artificial intelligence (AI) and machine learning (ML), dynamic data driven application systems (DDDAS), and hierarchical cloud-fog-edge computing paradigm provide opportunities for enhancing multi-domain systems performance. As one example that represents multi-domain scenario, a ``fly-by-feel'' system utilizes DDDAS framework to su...

Advancement in artificial intelligence (AI) and machine learning (ML), dynamic data driven application systems (DDDAS), and hierarchical cloud-fog-edge computing paradigm provide opportunities for enhancing multi-domain systems performance. As one example that represents multi-domain scenario, a "fly-by-feel" system utilizes DDDAS framework to supp...

Recent research has demonstrated improved performance of a brain-computer interface (BCI) using fusion based approaches. This paper proposes a novel decision-making selector (DMS) to integrate classification decisions of different frequency recognition methods based on canonical correlation analysis (CCA) which were used in decoding steady state vi...