Aerial Delivery System with High Accuracy Touchdown

PatentEmbodiments described herein provide a system and method for persistent high-accuracy payload delivery utilizing a twophase procedure during the terminal descent phase of aerial payload delivery. In the first phase a small parafoil provides aerial delivery of a payload to within a close proximity of an intended touchdown point, e.g., a target. In the second phase a target designator acquires the target and a trajectory to the target is determined. A harpoon launcher deploys a harpoon connected to the payload by an attachment line, such as a rope. A reel mechanism reels up the attachment line causing the payload to be moved to the target thus providing high accuracy touchdown payload delivery

Machine Learning of Semi-Autonomous Intelligent Mesh Networks Operation Expertise

Proceedings of the 52nd Hawaii International Conference on System Sciences | 2019The article of record at published may be found at https://hdl.handle.net/10125/59562.Operating networks in very dynamic environments makes network management both complex and difficult. It remains an open question how mesh or hastily formed networks with many nodes could be managed efficiently. Considering the various constraints such as limited communication channels on network management in dynamic environments, the need for semi-autonomous or autonomous networks is evident. Exploitation of machine learning techniques could be a way to solve this network management challenge. However, the need for large training datasets and the infrequency of network management events make it uncertain whether this approach is effective for highly dynamic networks and networks operating in unfriendly conditions, such as tactical military networks. This paper examines the feasibility of this approach by analyzing a recorded dataset of a mesh network experiment in a highly dynamic, austere military environment and derives conclusions for the design of future mesh networks and their network management systems

Machine Learning of Semi-Autonomous Intelligent Mesh Networks Operation Expertise

Operating networks in very dynamic environments makes network management both complex and difficult. It remains an open question how mesh or hastily formed networks with many nodes could be managed efficiently. Considering the various constraints such as limited communication channels on network management in dynamic environments, the need for semi-autonomous or autonomous networks is evident. Exploitation of machine learning techniques could be a way to solve this network management challenge. However, the need for large training datasets and the infrequency of network management events make it uncertain whether this approach is effective for highly dynamic networks and networks operating in unfriendly conditions, such as tactical military networks. This paper examines the feasibility of this approach by analyzing a recorded dataset of a mesh network experiment in a highly dynamic, austere military environment and derives conclusions for the design of future mesh networks and their network management systems

Network Awareness for Wireless Peer-to-Peer Collaborative Environments

Presentation to the 37th Hawaii International Conference on System Science. Hilton Waikoloa Village, Island of Hawaii, 5-8 January 2004.The implications of using mobile wireless communications are significant for emerging peer-to-peer (P2P) collaborative environments. From a networking perspective, the use of wireless technologies to support collaboration may impact bandwidth and spectrum utilization. This paper explores the effects of providing feedback to system users regarding wireless P2P network behavior on the performance of collaboration support applications. We refer to this operational feedback as "network awareness." The underlying premise is that providing feedback on the status of the network will enable users to self-organize their behavior to maintain quality of data sharing. Results achieved during an experiment conducted at the Naval Postgraduate School demonstrate significant effects of roaming on application sharing performance and integration with client-server applications. A solution for improving network aware P2P collaboration, identified in the experiment, is discussed

Manned-Unmanned Self-Organizing Bursty Networks with Biological Nodes

CRUSER TechCon 2018 Research at NPS. Wednesday 1: SensingAn elusive (hard to detect and hard to compromise) fast morphing network composed of cooperative manned-unmanned short living nodes and links, could be a significant force multiplier in providing an asymmetric advantage for emerging urban and coastal maritime combat. During FY16-FY17, sponsored by CRUSER, our research team made initial successful steps in proof of concept experimental studies of short-living projectile-based nodes for multi-domain mesh networking as well as short-living directional links to enable robust elusive littoral mesh networking. Through incremental experimentation, we've explored promising capabilities of integrating short-living nodes with miniature short-living link models to support very temporary connections between nodes of a multi-domain mesh network, enabled by maneuvering UAV formations, small sets of USVs and UGVs, fast patrol boats, and urban area ground units. Integration of biological aerial and ground nodes, such as falcons in the air and canines on the ground, provides a unique opportunity provides a unique opportunity to expand bursty manned-unmanned mesh networks research into the new level of formation autonomous behavior. In this new line of research we explore feasibility and major constraints for falcons and canines to carry on advanced miniature solutions for bursty links and nodes. We explore the falcons capability to serve as fast moving aerial relays for rapidly stretching the UAV-UGV or/and UAV-USV mesh network into the otherwise denied area, negotiate position and distance with closest UAV-UGV-USV-dismounted operator neighbors, and exercise unstructured autonomous behavior to maintain sensing or attack patterns. Similarly, we explore the capability of canines to maintain "canine nose-UAV/Falcon eye" cooperation in stretching the network to mission area, to enable temporary sensor data bursts collection and transfer, and , certainly their ability enhance UAV-UGV-USV mesh network autonomous behavior

Mobile system for precise aero delivery with global reach network capability

This paper discusses the current status of the development of the mobile aerial delivery system to be further employed in a variety of different applications. High accuracy of the developed system enables its use in precision troop resupply, precise sensors placement, urban warfare reconnaissance and other similar operations. This paper overviews the overall system architecture and components of the developed aero delivery system itself and then proceeds with describing the current status of integrating it with an advanced deployment platform, unmanned aerial system, to achieve mobility and autonomy of operations. The paper also discusses some other systems in development pursuing similar goals and reviews some novel applications that become possible with the developed aerial delivery system

Situational Awareness (SA) multi agent system overview

SA Multi Agent System is a program that was developed for the purpose of increasing battlespace awareness of "war fighters" and combatant commanders during STAN (Surveillance and Target Acquisition Network) and TNT (Tactical Network Topology) experiments that are run each quarter at NPS. The common operating picture is based on maps or charts of the area of operations populated with agents that are represented by various icons like a person, truck, UAV, sensor, etc. (Figure 1). The entities are located on the chart via latitude and longitude positions that can be entered manually, by clicking and dragging your symbol to your current position, or by a Global Positioning System (GPS) device. Since SA Multi Agent System did not have any user guide information available yet, this chapter is written to provide an overview of the program and to act as a user guide for those using this software program in the future

Voice-on-Target: A New Approach to Tactical Networking and Unmanned Systems Control via the Voice Interface to the SA Environment

Center for Network Innovation and Experimentation (CENETIX) Publication14th International Command and Control Research and Technology Symposium (ICCRTS), June 15-17, 2009, Washington DC.Paper ID Number 179. C2 and Agility, Track 2: Network and Networkin

Adaptive on-demand networking with self-aligning wireless nodes

Includes a .doc version to provide an adaptive on-demand networking with self-aligning wireless nodes overview with interactive demo of ground (swf) and maritime (swf) operation. by Alex Bordetsky and Eugene Bourakov.Center for Network Innovation and Experimentation (CENETIX) PublicationThe emerging tactical networks represent complex network-centric systems, in which multiple sensors, unmanned vehicles, and geographically distributed units of highly mobile decision makers, transfer and analyze data while on the move. The network could easily scale up to hundreds of cooperating nodes, providing tactical extension to the system-of-systems environment of Global Information Grid [1]. The node mobility as well as ad hoc network topology reconfiguration becomes a powerful control option, which network operators or intelligent management agents could apply to provide for self-forming, self-healing behavior [2]. This in turn requires new techniques for adaptive remote management of mobile wireless nodes; their rapid remote or autonomous reconfiguration at both physical and application layers, subject to changing operational requirements. We name this new adaptive tactical networking management paradigm as Network-on-Target (NoT). It is assumed that the NoT process starts at the level of Situational Awareness Interface used by the local or higher echelon commander, to point onto the Target, which in this case is the site to be reached by the self-configuring network. In response the mobile networking node, i.e. small boat, light reconnaissance vehicle, or operator are moved to the area to extend the tactical mesh. However, if site is too far, or the preceding links are about to break down, the UAV is deployed to stretch the network further to the remote most node , or to heal the overstretched intermediate link. This in turn would require rapid and frequent re-alignment, of the antenna assets including panel switching and tune-up decisions made right at the level of local commander situational awareness view. More so, the commander’s remote advisers, located thousands miles away of surveillance and targeting area would be able to see the effects of the healing assets deployment in the Situational Awareness view and assist the commander in re-aligning and stretching the mobile network to the target area. In this paper we describe an innovative solution, developed at the Naval Postgraduate School Center for Network Innovation and Experimentation (CENETIX), enabling the Network-on-Target process by multiplatform control (separation of control and data links) and remote re-alignment of the self-forming OFDM-based tactical networking assets

Network on Target: Remotely Configured Adaptive Tactical Networks

Center for Network Innovation and Experimentation (CENETIX) Publication11th International Command and Control Research and Technology Symposium (ICCRTS), June 20-22, 2006, San Diego, CA.The emerging tactical networks represent complex network-centric systems, in which multiple sensors, unmanned vehicles, and geographically distributed units of highly mobile decision makers, transfer and analyze data while on the move. The network could easily scale up to hundreds of cooperating nodes, providing tactical extension to the system-of-systems environment of Global Information Grid [1]. The node mobility as well as ad hoc network topology reconfiguration becomes a powerful control option, which network operators or intelligent management agents could apply to provide for self-forming, self-healing behavior [2]. This in turn requires new techniques for adaptive remote management of mobile wireless nodes; their rapid remote or autonomous reconfiguration at both physical and application layers, subject to changing operational requirements. We name this new adaptive tactical networking management paradigm as Network-on-Target (NoT). It is assumed that the NoT process starts at the level of Situational Awareness Interface used by the local or higher echelon commander, to point onto the Target, which in this case is the site to be reached by the self-configuring network. In response the mobile networking node, i.e. small boat, light reconnaissance vehicle, or operator are moved to the area to extend the tactical mesh. However, if site is too far, or the preceding links are about to break down, the UAV is deployed to stretch the network further to the remote most node , or to heal the overstretched intermediate link. This in turn would require rapid and frequent re-alignment, of the antenna assets including panel switching and tune-up decisions made right at the level of local commander situational awareness view. More so, the commander’s remote advisers, located thousands miles away of surveillance and targeting area would be able to see the effects of the healing assets deployment in the Situational Awareness view and assist the commander in re- aligning and stretching the mobile network to the target area. In this paper we describe an innovative solution, developed at the Naval Postgraduate School Center for Network Innovation and Experimentation (CENETIX), enabling the Network-on- Target process by multiplatform control (separation of control and data links) and remote re- alignment of the self-forming OFDM-based tactical networking assets. The described self- aligning solution allows for mobile nodes critical capability of tracking each other on the move