Assessment of Nighttime Airborne Visual ASW Capability

NPS NRP Project PosterHaving an unmanned aircraft to investigate possible surface and underwater contacts ahead of a transiting strike group is considered a valuable and urgently needed tool to complement other available sensors to identify a threat (in the case of Anti-Submarine Warfare (AFW)) and/or avoid collision. The hypothesis evaluated in this study is whether modern commercial off-the-shelf (COTS) small unmanned aerial system (sUAS) equipped with a miniaturized COTS low-light night-vision (LLNV) imagers could ultimately provide this capability. Ideally, this study would benefit from using a highly efficient electric vertical takeoff and landing fixed-wing COTS sUAS which can be launched even from a small ship deck, including those operated by Monterey Bay Aquarium Research Institute (MBARI). This sUAS features extremely low noise emission, can stay in the area for 90+ minutes, carry wide range of high precision sensors and cover large areas. It can transmit telemetry at up to 7 km C2 range, and can potentially provide encoded live video stream at 5mb/s (using an encrypted broadband mesh IP network). However, many constrains associated with cyber security, ship operations, airspace may prevent from employing sUAS in this study. The envisioned alternative platform to be used to evaluate feasibility of the overall concept is a general aviation aircraft equipped to collect low-light imagery. The research questions this study may address are as follows: 1. What are the limits of the current airborne LLNV sensors for nighttime detection of underwater objects? 2. Would the use of spectral, spatial and temporal filters would enhance target signature enough to create image database and use it for identification purposes? 3. What are the atmospheric / water condition contributing to emission of light from living organisms? 4. Whether it is feasible to integrate a COTS wavelength-optimized LLNV sensor with a COTS sUAS to provide a viable nighttime detection capability for shallow-swimming biologics and objects? The study will present the overall concept, discuss integration issues and issues associated with operating in the National airspace, specifically within the Monterey Bay area at nighttime; followed by assessing shallow-swimming whale imagery coming out of a typical COTS LLNV sensor. Based on the quality, quantity and variety of the collected samples, this study may include creating an image database and artificial-intelligence based qualifier to distinguish different objects (species) based on their bioluminescent signature. It is expected to collaborate with several external organizations; involve SE, OC and MR students; and summarize all the findings in the final report.Naval Surface and Mine Warfighting Development Center (SMWDC)U.S. Fleet Forces Command (USFF)This research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Assessment of Nighttime Airborne Visual ASW Capability

NPS NRP Executive SummaryHaving an unmanned aircraft to investigate possible surface and underwater contacts ahead of a transiting strike group is considered a valuable and urgently needed tool to complement other available sensors to identify a threat (in the case of Anti-Submarine Warfare (AFW)) and/or avoid collision. The hypothesis evaluated in this study is whether modern commercial off-the-shelf (COTS) small unmanned aerial system (sUAS) equipped with a miniaturized COTS low-light night-vision (LLNV) imagers could ultimately provide this capability. Ideally, this study would benefit from using a highly efficient electric vertical takeoff and landing fixed-wing COTS sUAS which can be launched even from a small ship deck, including those operated by Monterey Bay Aquarium Research Institute (MBARI). This sUAS features extremely low noise emission, can stay in the area for 90+ minutes, carry wide range of high precision sensors and cover large areas. It can transmit telemetry at up to 7 km C2 range, and can potentially provide encoded live video stream at 5mb/s (using an encrypted broadband mesh IP network). However, many constrains associated with cyber security, ship operations, airspace may prevent from employing sUAS in this study. The envisioned alternative platform to be used to evaluate feasibility of the overall concept is a general aviation aircraft equipped to collect low-light imagery. The research questions this study may address are as follows: 1. What are the limits of the current airborne LLNV sensors for nighttime detection of underwater objects? 2. Would the use of spectral, spatial and temporal filters would enhance target signature enough to create image database and use it for identification purposes? 3. What are the atmospheric / water condition contributing to emission of light from living organisms? 4. Whether it is feasible to integrate a COTS wavelength-optimized LLNV sensor with a COTS sUAS to provide a viable nighttime detection capability for shallow-swimming biologics and objects? The study will present the overall concept, discuss integration issues and issues associated with operating in the National airspace, specifically within the Monterey Bay area at nighttime; followed by assessing shallow-swimming whale imagery coming out of a typical COTS LLNV sensor. Based on the quality, quantity and variety of the collected samples, this study may include creating an image database and artificial-intelligence based qualifier to distinguish different objects (species) based on their bioluminescent signature. It is expected to collaborate with several external organizations; involve SE, OC and MR students; and summarize all the findings in the final report.Naval Surface and Mine Warfighting Development Center (SMWDC)U.S. Fleet Forces Command (USFF)This research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Assessment of Multispectral Imaging System for UAS Navigation in a GPS-denied Environment

NPS NRP Technical ReportMultispectral (MS) imaging systems have been used for the detection, identification, and quantification in numerous environmental and military applications already. It is proposed to analyze feasibility of utilizing this emerging technology on small unmanned aerial vehicles (sUAS) for the purpose of enhancing accuracy and precision of object detection (identification), classification and tracking (DCT) that may contribute to a variety of downstream applications including threat detection, forensics, battle damage-assessment, additional/alternative aid to navigation (ATON) in the GPS-degraded or GPS-denied environments. This study assesses applicability and benefits of using a MS sensor as opposed to standard infrared (IR) and/or electro-optical (EO) sensors for DCT applications. It also includes an assessment of the computer-vision (CV) and artificial intelligence (AI) algorithms to quickly and reliably process the sensor output data. It is envisioned that a MicaSense RedEdge-MX or Altum like high-resolution global-shutter 5-band MS sensor integrated with a commercial-of-the-shelf (COTS) Group 1 or Group2 sUAS will be used to collect data to train a deep-learning (DL) convolutional neural network (DCNN) capable to handle one or two specific DCT problems to address the following research questions: Whether using multiple spectral bands has any benefits compared to a standard EO sensor or EO sensor combined with IR sensor? That includes benefits of having a spectral profile of surrounding background area and objects from the standpoint of more reliable/precise DCT. What are the limitations of using MS sensors and CV/AI algorithms to process data from the standpoint of operating environment, terrain, altitudes, object size and material, time of the day, weather, number of spectral bands, resolution, narrow field of view, addition of a downwelling light sensor)? What computational resources would be required to enable DTS capability aboard COTS sUAS The study will look at the requirements to such a system and its CONOPS, followed by conducting numerical experiments and field testing to gather and analyze data coming out of a MS imaging sensor. It is expected to involve SE, OC and CS students, and summarize all the findings in the final report.Naval Special Warfare Command (NAVSPECWARCOM)N9 - Warfare SystemsThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Development of a Steerable Single-Actuator Cruciform Parachute

The article of record as published may be found at https://doi.org/10.2514/1.C034416Despite success in developing and fielding a family of different-weight precision self-guiding parafoil-based aerial payload delivery systems, there is a need for a system that would trade relatively large standoff deployment distances for a substantially lower cost of production and operation. One such system, based on a standard round canopy with a slightly modified rigging, was developed and demonstrated in the early 2000s. This paper capitalizes on controllable round canopy developments and considers using inexpensive cruciform-type canopies with a single-actuator control paradigm. Although the aerodynamics of cruciform-type canopies were extensively studied in the past, lesser efforts were devoted to converting them into a steerable platform. This paper presents the conceptual design and prototyping of a cruciform parachute-based aerial payload delivery system and discusses the results of the initial subscale developmental tests devoted to studying the control authorities and dynamics of such a system

Assessment of Multispectral Imaging System for UAS Navigation in a GPS-denied Environment

NPS NRP Project PosterMultispectral (MS) imaging systems have been used for the detection, identification, and quantification in numerous environmental and military applications already. It is proposed to analyze feasibility of utilizing this emerging technology on small unmanned aerial vehicles (sUAS) for the purpose of enhancing accuracy and precision of object detection (identification), classification and tracking (DCT) that may contribute to a variety of downstream applications including threat detection, forensics, battle damage-assessment, additional/alternative aid to navigation (ATON) in the GPS-degraded or GPS-denied environments. This study assesses applicability and benefits of using a MS sensor as opposed to standard infrared (IR) and/or electro-optical (EO) sensors for DCT applications. It also includes an assessment of the computer-vision (CV) and artificial intelligence (AI) algorithms to quickly and reliably process the sensor output data. It is envisioned that a MicaSense RedEdge-MX or Altum like high-resolution global-shutter 5-band MS sensor integrated with a commercial-of-the-shelf (COTS) Group 1 or Group2 sUAS will be used to collect data to train a deep-learning (DL) convolutional neural network (DCNN) capable to handle one or two specific DCT problems to address the following research questions: Whether using multiple spectral bands has any benefits compared to a standard EO sensor or EO sensor combined with IR sensor? That includes benefits of having a spectral profile of surrounding background area and objects from the standpoint of more reliable/precise DCT. What are the limitations of using MS sensors and CV/AI algorithms to process data from the standpoint of operating environment, terrain, altitudes, object size and material, time of the day, weather, number of spectral bands, resolution, narrow field of view, addition of a downwelling light sensor)? What computational resources would be required to enable DTS capability aboard COTS sUAS The study will look at the requirements to such a system and its CONOPS, followed by conducting numerical experiments and field testing to gather and analyze data coming out of a MS imaging sensor. It is expected to involve SE, OC and CS students, and summarize all the findings in the final report.Naval Special Warfare Command (NAVSPECWARCOM)N9 - Warfare SystemsThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Assessment of Multispectral Imaging System for UAS Navigation in a GPS-denied Environment

NPS NRP Executive SummaryMultispectral (MS) imaging systems have been used for the detection, identification, and quantification in numerous environmental and military applications already. It is proposed to analyze feasibility of utilizing this emerging technology on small unmanned aerial vehicles (sUAS) for the purpose of enhancing accuracy and precision of object detection (identification), classification and tracking (DCT) that may contribute to a variety of downstream applications including threat detection, forensics, battle damage-assessment, additional/alternative aid to navigation (ATON) in the GPS-degraded or GPS-denied environments. This study assesses applicability and benefits of using a MS sensor as opposed to standard infrared (IR) and/or electro-optical (EO) sensors for DCT applications. It also includes an assessment of the computer-vision (CV) and artificial intelligence (AI) algorithms to quickly and reliably process the sensor output data. It is envisioned that a MicaSense RedEdge-MX or Altum like high-resolution global-shutter 5-band MS sensor integrated with a commercial-of-the-shelf (COTS) Group 1 or Group2 sUAS will be used to collect data to train a deep-learning (DL) convolutional neural network (DCNN) capable to handle one or two specific DCT problems to address the following research questions: Whether using multiple spectral bands has any benefits compared to a standard EO sensor or EO sensor combined with IR sensor? That includes benefits of having a spectral profile of surrounding background area and objects from the standpoint of more reliable/precise DCT. What are the limitations of using MS sensors and CV/AI algorithms to process data from the standpoint of operating environment, terrain, altitudes, object size and material, time of the day, weather, number of spectral bands, resolution, narrow field of view, addition of a downwelling light sensor)? What computational resources would be required to enable DTS capability aboard COTS sUAS The study will look at the requirements to such a system and its CONOPS, followed by conducting numerical experiments and field testing to gather and analyze data coming out of a MS imaging sensor. It is expected to involve SE, OC and CS students, and summarize all the findings in the final report.Naval Special Warfare Command (NAVSPECWARCOM)N9 - Warfare SystemsThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

System and Method for Unmanned Aerial Vehicle (UAV)-Based Foreign Object Debris (FOD) Detection

A computer-based system and method allows a user to automatically aerially inspect a selected runway/taxiway tarmac surface for foreign object debris (FOD) using a plurality of UAVs equipped with electro - optical (EO) sensors. Live images of the runway surface captured by the EO sensors are transmitted from the UAVs during an inspection sweep of the runway surface and are compared with FOD free reference images of the runway surface to determine whether candidate FOD are detected on the runway surface. Information about and images of candidate FOD are displayed to the user allowing the user to determine whether to send a team to remove the FOD from the runway.US 11,281,905 B

Shipboard Landing Challenges for Autonomous Parafoils

21st AIAA Aerodynamic Decelerator Systems Technology Conference, Dublin, Ireland, May 23-26, 2011.This paper examines some of the challenges that must be overcome if future aerial delivery systems are to have the capability to land on the _ight deck of a ship underway. The unique aspects of trajectory planning for landing on a shipﾒs _ight deck are _rst examined, followed by formulation of the position estimation problem for a moving target. Some preliminary investigations into characterizing the wind over a moving landing platform at sea are then described. Finally, experimental results are presented for testing of a small prototype autonomous parafoil with a simple moving target on land

A Framework for Automatic Behavior Generation in Multi-Function Swarms

17 USC 105 interim-entered record; under review.Multi-function swarms are swarms that solve multiple tasks at once. For example, a quadcopter swarm could be tasked with exploring an area of interest while simultaneously functioning as ad-hoc relays. With this type of multi-function comes the challenge of handling potentially conflicting requirements simultaneously. Using the Quality-Diversity algorithm MAP-elites in combination with a suitable controller structure, a framework for automatic behavior generation in multi-function swarms is proposed. The framework is tested on a scenario with three simultaneous tasks: exploration, communication network creation and geolocation of Radio Frequency (RF) emitters. A repertoire is evolved, consisting of a wide range of controllers, or behavior primitives, with different characteristics and trade-offs in the different tasks. This repertoire enables the swarm to online transition between behaviors featuring different trade-offs of applications depending on the situational requirements. Furthermore, the effect of noise on the behavior characteristics in MAP-elites is investigated. A moderate number of re-evaluations is found to increase the robustness while keeping the computational requirements relatively low. A few selected controllers are examined, and the dynamics of transitioning between these controllers are explored. Finally, the study investigates the importance of individual sensor or controller inputs. This is done through ablation, where individual inputs are disabled and their impact on the performance of the swarm controllers is assessed and analyzed