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Detection and Classification of Drones Through Acoustic Features Using a Spike-Based Reservoir Computer for Low Power Applications 41

Detection and Classification of Drones Through Acoustic Feat...

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41st IEEE/AIAA Digital Avionics Systems Conference, DASC 2022

作者： Henderson, Alex Yakopcic, Chris Harbour, Steven Taha, Tarek M. Dayton Engineering Advanced Projects Lab Southwest Research Institute Beavercreek United States University of Dayton Department of Electrical and Computer Engineering Dayton United States

ISBN: (数字)9781665486071

ISBN: (纸本)9781665486071

Over the past few decades, the use of drones for applications such as defense, reconnaissance, agriculture, law enforcement, and others has dramatically increased. Although drones are useful for these applications, they can also be utilized to perform malicious activities, thus compromising the safety and integrity of physical infrastructures. To address this issue, various techniques have been developed to detect and identify drones, including radar, visual analysis, and radio-frequency signal processing. Furthermore, deep learning algorithms based on the recognition of acoustic drone features have been proposed to automate the detection process and overcome the current limitations in modern drone detection systems. This paper presents an auditory drone detection and identification system based on the sparse, event-driven communication nature of Spiking Neural Networks (SNNs). We investigate the use of a spiking reservoir computing model, known as a Liquid State Machine (LSM), that offers a computationally light alternative to the deep learning approaches of previous works. The LSM based auditory drone detection and identification system is demonstrated on a publicly available acoustic drone dataset, achieving an accuracy of 97.13% and 93.25% on the detection and identification tasks, respectively. To the best of our knowledge, this work presents the first spike-based implementation of an auditory drone recognition system. Moreover, this paper highlights the potential for low size, weight, and power neuromorphic hardware deployment for drone applications that may be limited to energy-constrained environments. © 2022 IEEE.

关键词： Drones

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Toward Airworthiness Certification for Artificial Intelligence (AI) in Aerospace Systems 41

Toward Airworthiness Certification for Artificial Intelligen...

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41st IEEE/AIAA Digital Avionics Systems Conference, DASC 2022

作者： Henderson, Alex Harbour, Steven Cohen, Kelly Southwest Research Institute Dayton Engineering Advanced Projects Lab Beavercreek United States University of Cincinnati Department of Aerospace Engineering Cincinnati United States

ISBN: (数字)9781665486071

ISBN: (纸本)9781665486071

Boeing 'produced an airplane that had an unsafe condition' and admitted full responsibility for the second of its 737 MAX plane crash in Ethiopia in October 2018, killing 189 people. A major cause was found to be new flight control software, based on the AI (Artificial Intelligence) system called Maneuvering Characteristics Augmentation System (MCAS) which needed to be modified. It appears that the MCAS was originally designed as a limited AI system aimed at nose correction based on wind resistance and force sensors. However, in 2012, Boeing expanded that the MCAS and these changes, coupled with inadequate pilot training made it more vulnerable to malfunctioning. These unfortunate developments have further underscored the urgency to address airworthiness given the rise in autonomous systems and their integration into the aerospace domain. While there is an increased interest in effectively managing/regulating the AI certification problem for isolated complex functions, there is little work done on the certification of AI for aircraft that jointly evaluates System of Systems (SoS) integration in tandem with the human machine team dynamics involved in the engineering and deployment of AI-based systems. As a result, the development of airworthiness certification criteria will lag the development of state-of-the-art intelligent systems. This paper presents various considerations to enable the development of certification criteria for AI when consulting aerospace system design guidance and standards, including MIL-HDBK-516C, DO-178, and FAA's Airworthiness Certification Regulations. To allow for the validation and verification of AI-based systems to be safe, a systems engineering model is proposed to facilitate certification that includes an AI certification profile, verifiable implementation measures and a continuous process flow to ensure airworthiness that cannot be verified through traditional, test-based means. Furthermore, safety assurance approaches are identified

关键词： Machine learning

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Human Vs. Autonomous Agents: Drone racing and Obstacle Avoidance 41

Human Vs. Autonomous Agents: Drone racing and Obstacle Avoid...

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41st IEEE/AIAA Digital Avionics Systems Conference, DASC 2022

作者： Lochtefeld, Joelle Schlager, Stephen Bryan, Samuel Harbour, Steven Colter, Jameson Sinclair College Department of Unmanned Aeriel Systems DaytonOH45402 United States Southwest Research Institute Dayton Engineering Advanced Projects Lab BeavercreekOH45431 United States

ISBN: (数字)9781665486071

ISBN: (纸本)9781665486071

With the increase of autonomy in aircraft systems over the past decade, the question of whether autonomous agents provide better performance when compared to human operators is of utmost importance. Current research using machine learning for obstacle avoidance in small Unmanned Aircraft Systems has gained extensive attention in recent years, but who is faster, the human or the machine? This study leverages programmable coding languages using Unmanned Aircraft Systems (UAS) to navigate a fabricated obstacle course environment. By applying various methods of intelligent agent training, human pilots were compared to the performance of the automated system. The Artificial Intelligence (AI) training ensured that the automated flight path and speed for the Unmanned Aircraft System were optimized for the course environment. We collected a dataset from a sample size of 12 participants, including zero knowledge of flight training, to experienced pilots. The findings of this work aimed to show the performance comparison, including Key System Attributes and Key Performance Parameters, by considering drone speed, pilot skill level, and course crashes between man and machine to determine which is a safer and faster solution. © 2022 IEEE.

关键词： Autonomous agents

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Blockchain-Enabled Federated Learning with Neuromorphic Edge Devices for Drone Identification and Flight Mode Detection

Blockchain-Enabled Federated Learning with Neuromorphic Edge...

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Conference on Digital Avionics Systems (DASC)

作者： Alex Henderson Chris Yakopcic Jamison Colter Steven Harbour Tarek Taha Dayton Engineering Advanced Projects Lab Southwest Research Institute Beavercreek OH Department of Electrical and Computer Engineering University of Dayton Dayton OH

Unmanned aerial vehicles (UAVs), also known as drones, are expected to play an integral role in next-generation wireless avionics networks. To help secure these networks from malicious activity, machine learning (ML) based approaches have been proposed to identify drones and their flight modes via direct radio frequency signals. These approaches frequently rely on cloudcentric methods for training, which present serious concerns, such as privacy leakage, resource burden, and undesirable latency. In response to these concerns, a distributed ML paradigm known as federated learning (FL) has been proposed that enables multiple drones to collaboratively train ML models by only exchanging model parameters and not the raw data itself. Unfortunately, the conventional FL framework is strongly dependent on a centralized aggregation server with multiple resource-constrained edge devices, making it susceptible to poisoning attacks and poor network utilization. Recently, it has been found that blockchain technology (BT) holds significant promise for securing and storing data in edge applications with high levels of trust. However, the expanding chain of security blocks consumes a significant amount of computing power, thus limiting its scalability. To bridge the gap between computational efficiency and security, we propose a blockchain-empowered and energy-efficient FL framework with neuromorphic edge devices. To assess the effectiveness of our proposed framework we perform a drone identification and flight mode detection task with a spiking neural network (SNN). Finally, we compare our neuromorphic approach with competitive alternatives to validate the energy and performance gains of our system.

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Real-time Vision-based Control of SWaP-constrained Flight System with Intel Loihi 2

Real-time Vision-based Control of SWaP-constrained Flight Sy...

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Conference on Digital Avionics Systems (DASC)

作者： Steven Harbour Ben Sears Stephen Schlager Matt Kinnison John Sublette Alex Henderson Southwest Research Institute Dayton Engineering Advanced Projects Lab Beavercreek OH Tactical Aerospace Department Southwest Research Institute San Antonio TX

The Neuromorphic based system contained in this research learns from the human pilot. The typical system for this type of research would have been designed for GPUs utilizing a Deep Neural Network. However, previous research indicates that GPUs require relatively large amounts of energy to operate and produce comparatively high amounts of heat. Moreover, due to Low Size Weight and Power (SWaP) constraints for Aircraft (A/C) and for Uncrewed Aerial Systems (UAS), GPUs may have to be off-board and or cloud computing utilized. Which may not be practical in situations. Therefore as an alternative the neuromorphic processor Loihi 2 was chosen. In this research, it was found that Loihi 2 solves these problems while showing to provide exceptional and robust performance. The Lava Sigma-Delta Neural Network (SDNN) inspired by PilotNet can be used in such a way to fly an aircraft or drone by learning from the human pilot using the neuromorphic Loihi 2. This study utilized actual live flight test data the researchers collected from live drone test flights to train an SDNN. During this research, a model was developed using the human hippocampus and visual cortex as they relate to the SDNN. The results of this study should enable an expanded autonomy envelope for A/C and UAS. This research will allow the creation of more scientific and engineering discovery and autonomy applications, ultimately empowering more mission efficacy by way of fast, on the edge, and stand alone computing. Demonstrated by the SDNN was a robust algorithm that emulated biological systems and generalized very well. To the best of our knowledge, this is the first in the world’s work that enables a UAS to be autonomously flown with Loihi 2, utilizing the SDNN.

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Spiking Neural Networks for LPI Radar Waveform Recognition with Neuromorphic Computing

Spiking Neural Networks for LPI Radar Waveform Recognition w...

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IEEE National Conference on Radar

作者： Alex Henderson Steven Harbour Chris Yakopcic Tarek Taha David Brown Justin Tieman Garrett Hall Dayton Engineering Advanced Projects Lab Southwest Research Institute Beavercreek OH Department of Electrical and Computer Engineering University of Dayton Dayton OH Advanced Electronic Warfare Department Southwest Research Institute Warner Robins GA RF Sensors and Systems Department Southwest Research Institute San Antonio TX

The ultra-wideband, frequency agile and high duty cycle features of Low Probability of Intercept (LPI) radar pose serious concerns for passive radar detection systems that must efficiently identify LPI radar modulation schemes with very low latency and high accuracy. For these tasks, conventional deep learning techniques have often been employed, though this comes at the cost of increased model complexity and high energy consumption when implemented in tactically relevant scenarios with significant channel deterioration and size, weight, and power (SWaP) constrained platforms. In this paper, we propose a neuromorphic approach based on energy-efficient and robust Spiking Neural Networks (SNNs). To demonstrate the effectiveness of our proposed approach, we perform a challenging LPI radar waveform recognition task with a Spiking Convolutional Neural Network (SCNN). Simulation results show that the SCNN achieves a 13-waveform recognition accuracy of 99.1% at OdB SNR with 1.51µJ of total energy per inference. It is found that our system outperforms non-spiking models with a reduction in model size and three orders of magnitude lower energy consumption. To the best of our knowledge, this work presents the first spike-based implementation of an LPI radar waveform recognition system. Moreover, this paper highlights the potential for extremely low SWaP neuromorphic hardware deployment to enable intelligent electronic support systems that demand high throughput efficiency and ultra-low power.

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Circuit Optimization Techniques for Efficient Ex-Situ Training of Robust Memristor Based Liquid State Machine 22

Circuit Optimization Techniques for Efficient Ex-Situ Traini...

引用

Proceedings of the 17th ACM International Symposium on Nanoscale Architectures

作者： Alex Henderson Chris Yakopcic Steven Harbour Tarek Taha Cory Merkel Hananel Hazan Dayton Engineering Advanced Projects Lab Southwest Research Institute Beavercreek OH USA Department of Electrical and Computer Engineering University of Dayton Dayton OH USA Department of Computer Engineering Rochester Institute of Technology Rochester NY USA Allen Discovery Center Tufts University Medford MA USA

ISBN: (纸本)9781450399388

Spiking neural network hardware offers a high performance, power-efficient and robust platform for the processing of complex data. Many of these systems require supervised learning, which poses a challenge when using gradient-based algorithms due to the discontinuous properties of SNNs. Memristor based hardware can offer gains in portability, power reduction, and throughput efficiency when compared to pure CMOS. This paper proposes a memristor-based spiking liquid state machine (LSM). The inherent dynamics of the LSM permit the use of supervised learning without backpropagation for weight updates. To carry out the design space evaluation of the LSM for optimal hardware performance, several temporal signal classification tasks are performed. It is found that the binary neuron activations in the output layer improve testing accuracy by 3.7% and 5% for classification, while reducing training time. A power and energy analysis of the proposed hardware is presented, resulting in an approximately 50% reduction in power consumption and cycle energy.

关键词： spiking neural network liquid state machine memristor

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