Genshe Chen

Intelligent Fusion Technology, Inc.

The rapid growth of the Unmanned Aerial Vehicles (UAVs) market has led to increased reliance on UAV technologies across various application domains such as aerial Internet of Things (IoT). However, the vulnerability of UAV infrastructures to security threats and attacks poses significant risks. The discovery of robust security measures is particula...

The joint detection and classification of RF signals has been a critical problem in the field of wideband RF spectrum sensing. Recent advancements in deep learning models have revolutionized this field, remarkably through the application of state-of-the-art computer vision algorithms such as YOLO (You Only Look Once) and DETR (Detection Transformer...

The proliferation of unmanned aerial vehicle (UAV) networks is increasing, driven by their capacity to deliver automated services tailored to the varied demands of numerous smart city applications. Trust, security, and privacy remain paramount in the public domain. Traditional centralized network designs fall short of ensuring device authentication...

In this paper, we present a comprehensive study on the application of YOLOv8, a state-of-the-art computer vision (CV) model, to the challenging problem of joint detection and classification of signals in a highly dynamic and congested RF environment. Using our synthetic RF datasets, we explored three different scenarios with congested communication...

The proliferation of Unmanned Aerial Vehicle (UAV) networks is increasing, driven by their capacity to deliver automated services tailored to the varied demands of numerous smart city applications. Trust, security, and privacy remain paramount in the public domain. Traditional centralized network designs fall short of ensuring device authentication...

The mobility and versatility of unmanned aerial systems (UASs) make them valuable platforms in distributed cooperative beamforming (DCB) applications, where high-precision time synchronization and positioning, navigation, and timing (PNT) are required. UAS with PNT can quickly respond to changing situations and provide temporary coverage in remote...

This paper discusses using ground-based imagery to determine the attitude of a flying projectile assuming prior knowledge of its external geometry. It presents a segmentation-based approach to follow the object and evaluates it quantitatively with simulated data and qualitatively with both simulated and real data. Two experimental cases are conside...

This paper presents a model predictive automatic gain control (AGC) approach for satellite communications under partial-time partial-band additive white Gaussian noise (AWGN) jamming. In particular, autoregressive integrated moving average (ARIMA) models are first fitted to data collected from different environments and then employed to compute gai...

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, advantages of multimodal sensing and machine learning (ML) enhance real-time...

Over the past decade, there has been a remarkable acceleration in the evolution of smart cities and intelligent spaces, driven by breakthroughs in technologies such as the Internet of Things (IoT), edge–fog–cloud computing, and machine learning (ML)/artificial intelligence (AI). As society begins to harness the full potential of these smart environ...

Unmanned Aerial Vehicles (UAVs) have become indispensable components in the modern Internet of Things (IoT) ecosystem and are increasingly popular for various applications, including delivery, transporting, inspection, and mapping. However, the reliability, security, and privacy of UAV devices are among the public’s top concerns as they operate clo...

Deploying multiple Unmanned Aerial Systems (UASs) is beneficial for applications that survey large regions and require cooperative redundancy. Range-only cooperative navigation has been proposed to enhance positioning precision by exchanging navigation information, especially in Global Navigation Satellite Systems (GNSS)-denied environments. Howeve...

Radio frequency (RF) characterization is important to increase communication opportunities in a limited spectrum for aerospace platform performance. We measured propagation patterns of the RF signal radiated from an omnidirectional and vertically polarized antenna over imperfect ground. A comprehensive measurement program was conducted to acquire k...

Minimum Geometric Dilution of Precision (GDOP) is desired for a high accuracy in the localization of an unknown node. The optimal placements for the access nodes (or sensing nodes) to achieve the minimum GDOP can be configured by combinations of symmetric cones or regular polyhedrons. The minimum GDOP occurs at the tips of the cones or center of th...

Over the past decade, there has been a remarkable acceleration in the evolution of smart cities and intelligent spaces, driven by breakthroughs in technologies such as the Internet of Things (IoT), edge-fog-cloud computing, and machine learning (ML)/Artificial Intelligence (AI). As society begins to harness the full potential of these smart environ...

Deploying multiple Unmanned Aerial Systems (UASs) is beneficial for applications that survey large regions and require cooperative redundancy. Range-only cooperative navigation has been proposed to enhance positioning precision by exchanging navigation information, especially in Global Navigation Satellite Systems (GNSS)-denied environments. Howeve...

We developed a low-phase-noise, high-sensitivity linear-frequency-modulated continuous-wave (LFMCW) airborne radar for counter-UAS (unmanned aerial system) applications. It is low in size, weight, power, and cost (SWaP-C) (0.5 kg with batteries, operating for > 4 hours with 2 AA-size batteries) and mountable on a small UAS. It detects small drones...

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...