Collaborative signal and information processing for target detection with heterogeneous sensor networks

In this paper, an approach for target detection and acquisition with heterogeneous sensor networks through strategic resource allocation and coordination is presented. Based on sensor management and collaborative signal and information processing, low-capacity low-cost sensors are strategically deployed to guide and cue scarce high performance sensors in the network to improve the data quality, with which the mission is eventually completed more efficiently with lower cost. We focus on the problem of designing such a network system in which issues of resource selection and allocation, system behaviour and capacity, target behaviour and patterns, the environment, and multiple constraints such as the cost must be addressed simultaneously. Simulation results offer significant insight into sensor selection and network operation, and demonstrate the great benefits introduced by guided search in an application of hunting down and capturing hostile vehicles on the battlefield

Multi-target detection and recognition by UAVs using online POMDPs

This paper tackles high-level decision-making techniques for robotic missions, which involve both active sensing and symbolic goal reaching, under uncertain probabilistic environments and strong time constraints. Our case study is a POMDP model of an online multi-target detection and recognition mission by an autonomous UAV.The POMDP model of the multi-target detection and recognition problem is generated online from a list of areas of interest, which are automatically extracted at the beginning of the flight from a coarse-grained high altitude observation of the scene. The POMDP observation model relies on a statistical abstraction of an image processing algorithm's output used to detect targets. As the POMDP problem cannot be known and thus optimized before the beginning of the flight, our main contribution is an ``optimize-while-execute'' algorithmic framework: it drives a POMDP sub-planner to optimize and execute the POMDP policy in parallel under action duration constraints. We present new results from real outdoor flights and SAIL simulations, which highlight both the benefits of using POMDPs in multi-target detection and recognition missions, and of our`optimize-while-execute'' paradigm

Operator Objective Function Guidance for a Real-time Unmanned Vehicle Scheduling Algorithm

Advances in autonomy have made it possible to invert the typical operator-to-unmanned-vehicle ratio so that asingle operator can now control multiple heterogeneous unmanned vehicles. Algorithms used in unmanned-vehicle path planning and task allocation typically have an objective function that only takes into account variables initially identified by designers with set weightings. This can make the algorithm seemingly opaque to an operator and brittle under changing mission priorities. To address these issues, it is proposed that allowing operators to dynamically modify objective function weightings of an automated planner during a mission can have performance benefits. A multiple-unmanned-vehicle simulation test bed was modified so that operators could either choose one variable or choose any combination of equally weighted variables for the automated planner to use in evaluating mission plans. Results from a human-participant experiment showed that operators rated their performance and confidence highest when using the dynamic objective function with multiple objectives. Allowing operators to adjust multiple objectives resulted in enhanced situational awareness, increased spare mental capacity, fewer interventions to modify the objective function, and no significant differences in mission performance. Adding this form of flexibility and transparency to automation in future unmanned vehicle systems could improve performance, engender operator trust, and reduce errors.Aurora Flight Sciences, U.S. Office of Naval Researc

Vehicle Routing Problem Instances: Application to Multi-UAV Mission Planning

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/83659/1/AIAA-2010-8435-207.pd

An Optimal UAV Deployment Algorithm for Bridging Communication

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record.In recent years, Unmanned Aerial Vehicles (UAVs) have attracted the attention of both the military and civilians because of their deployment in situations where part of the communication infrastructure is destroyed due to bomb blast, earthquake, flood, military operations or landslides. Also UAVs can be used in operations such as search and rescue, surveillance, forest fire monitoring, and border patrolling. Deployment of a UAV in a position where it can provide maximum coverage and high throughput is one of the vital problem that needs to be addressed. In this paper, we have proposed an optimal UAV deployment algorithm (OUDA) in order to bridge communication between two static nodes on the ground. In the proposed algorithm the UAV deploys to a position where it can provide the best communication facilities to both the nodes based on the received signal strength (RSS), and distance between nodes and UAV. Simulation results showed that the algorithm provides maximum throughput and low bit error rate (BER) once the UAV is fixed to an optimal position

Path Planning Algorithm based on Arnold Cat Map for Surveillance UAVs

During their task accomplishment, autonomous unmanned aerial vehicles are facing more and more threats coming from both ground and air. In such adversarial environments, with no a priori information about the threats, a flying robot in charge with surveilling a specified 3D sector must perform its tasks by evolving on misleading and unpredictable trajectories to cope with enemy entities. In our view, the chaotic dynamics can be the cornerstone in designing unpredictable paths for such missions, even though this solution was not exploited until now by researchers in the 3D context. This paper addresses the flight path-planning issue for surveilling a given volume in adversarial conditions by proposing a proficient approach that uses the chaotic behaviour exhibited by the 3D Arnold’s cat map. By knowing the exact location of the volume under surveillance before take-off, the flying robot will generate the successive chaotic waypoints only with onboard resources, in an efficient manner. The method is validated by simulation in a realistic scenario using a detailed Simulink model for the X-4 Flyer quadcopter

Using Agent-Based Modeling to Search for Elusive Hiding Targets

The SCUD hunt problem that emerged during Operation Desert Storm has become a source of great interest to major commands like Air Combat Command. One of the metrics used to measure the effectiveness of our operations in a SCUD hunt is time to detect and target. We use the agent-based System Effectiveness and Analysis Simulation (SEAS) to provide a simulation environment in which all the elements of a SCUD hunt mission can adequately be modeled. Our Blue Force agents are modeled as multirole fighters, satellites and unmanned aerial vehicles (UAV) with various sensor capabilities. The Red Force agents are modeled as the SCUD transporter/erector/launcher (TEL). Particular interest is paid to the effectiveness of various sensors modeled in a set of scenarios following an experimental design. Four measures of performance (MOP) were fashioned to provide insight into the contribution of sensors at work in a SCUD hunt. These MOPs were evaluated to show any statistically significant differences between various mixes of sensors

An Information Value Approach to Route Planning for UAV Search and Track Missions

This dissertation has three contributions in the area of path planning for Unmanned Aerial Vehicle (UAV) Search And Track (SAT) missions. These contributions are: (a) the study of a novel metric, G, used to quantify the value of the target information gained during a search and track mission, (b) an optimal planning horizon that minimizes time-error of a planning horizon when interrupted by Poisson random events, and (c) a modified Particle Swarm Optimization (PSO) algorithm for search missions that uses the prior target distribution in the generation of paths rather than just in the evaluation of them. UAV route planning is an important topic with many applications. Of these, military applications are the best known. This dissertation focuses on route planning for SAT missions that jointly optimize the conflicting objectives of detecting new targets and monitoring previously detected targets. The information theoretic approach proposed here is different from and is superior to existing approaches. One of the main differences is that G quantifies the value of the target information rather than the information itself. Several examples are provided to highlight G’s desirable properties. Another important component of path planning is the selection of a planning horizon, which specifies the amount of time to include in a plan. Unfortunately, little research is available to aid in the selection of a planning horizon. The proposed planning horizon is derived in the context of plan updates triggered by Poisson random events. To our knowledge, it is the only theoretically derived horizon available making it an important contribution. While the proposed horizon is optimal in minimizing planning time errors, simulation results show that it is also near optimal in minimizing the average time needed to capture an evasive target. The final contribution is the modified PSO. Our modification is based on the idea that PSO should be provided with the target distribution for path generation. This allows the algorithm to create candidate path plans in target rich regions. The modified PSO is studied using a search mission and is used in the study of G

Cognitively Sensitive User Interface for Command and Control Applications

While there are broad guidelines for display or user interface design, creating effective human-computer interfaces for complex, dynamic systems control is challenging. Ad hoc approaches which consider the human as an afterthought are limiting. This research proposed a systematic approach to human / computer interface design that focuses on both the semantic and syntactic aspects of display design in the context of human-in-the-loop supervisory control of intelligent, autonomous multi-agent simulated unmanned aerial vehicles (UAVs). A systematic way to understand what needs to be displayed, how it should be displayed, and how the integrated system needs to be assessed is outlined through a combination of concepts from naturalistic decision making, semiotic analysis, and situational awareness literature. A new sprocket-based design was designed and evaluated in this research. For the practical designer, this research developed a systematic, iterative design process: design using cognitive sensitive principles, test the new interface in a laboratory situation; bring in subject matter experts to examine the interface in isolation; and finally, incorporate the resulting feedback into a full-size simulation. At each one of these steps, the operator, the engineer and the designer reexamined the results

WATER-BASED MITIGATION TECHNIQUES AND NETWORK INTEGRATION TO COUNTER DRONE SWARMS

Potential and current U.S. adversaries are purchasing and deploying commercial small Unmanned Aircraft Systems (sUAS) in networked swarms. These swarms can be used for intelligence collection and reconnaissance, and have the potential to be weaponized as well. Additionally, the unlawful, but probably not malicious, activity of civilian UAS (drone) operators is of increasing concern. More specifically, there is increased risk to naval assets while in constrained environments, such as harbor transit, where both navigation and weaponized responses are serious concerns. This thesis uses the scenario of protecting a U.S. Navy destroyer entering and exiting a harbor to develop a sUAS mitigation procedure based on existing firefighting and counter-piracy technologies. The proposed procedure includes a communications plan and can be implemented almost immediately using existing civilian and military assets. Additional recommendations to improve the performance of such procedures are provided.CRUSARRRTOLieutenant, United States NavyApproved for public release. Distribution is unlimited