Robin Glinton

Geolocation of RF Emitters by Many UAVs

This paper presents an approach to using a large team of UAVs to find radio frequency (RF) emitting targets in a large area. Small, inexpensive UAVs that can collectively and rapidly determine the approximate location of intermittently broadcasting and mobile RF emitters have a range of applications in both military, e.g., for finding SAM batteries, and civilian, e.g., for finding lost hikers, domains. Received Signal Strength Indicator (RSSI) sensors on board the UAVs measure the strength of RF signals across a range of frequencies. The signals, although noisy and ambiguous due to structural noise, e.g., multipath effects, overlapping signals and sensor noise, allow estimates to be made of emitter locations. Generating a probability distribution over emitter locations requires integrating multiple signals from different UAVs into a Bayesian filter, hence requiring cooperation between the UAVs. Once likely target locations are identified, EO-camera equipped UAVs must be tasked to provide a video stream of the area to allow a user to identify the emitter.

An integrated approach to high-level information fusion

In todays fast paced military operational environment, vast amounts of information must be sorted out and fused not only to allow commanders to make situation assessments, but also to support the generation of hypotheses about enemy force disposition and enemy intent. Current information fusion technology has the following two limitations. First, current approaches do not consider the battlefield context as a first class entity. In contrast, we consider situational context in terms of terrain analysis and inference. Second, there are no integrated and implemented models of the high-level fusion process. This paper describes the HiLIFE (High-Level Information Fusion Environment) computational framework for seamless integration of high levels of fusion (levels 2, 3 and 4). The crucial components of HiLIFE that we present in this paper are: (1) multi-sensor fusion algorithms and their performance results that operate in heterogeneous sensor networks to determine not only single targets but also force aggregates, (2) computational approaches for terrain-based analysis and inference that automatically combine low-level terrain features (such as forested areas, rivers, etc.) and additional information, such as weather, and transforms them into high-level militarily relevant abstractions, such as NO-GO, SLOW-GO areas, avenues of approach, and engagement areas, (3) a model for inferring adversary intent by mapping sensor readings of opponent forces to possible opponent goals and actions, and (4) sensor management for positioning intelligence collection assets for further data acquisition. The HiLIFE framework closes the loop on information fusion by specifying how the different components can computationally work together in a coherent system. Furthermore, the framework is inspired by a military process, the Intelligence Preparation of the Battlefield, that grounds the framework in practice. HiLIFE is integrated with a distributed military simulation system, OTBSAF, and the RETSINA multi-agent infrastructure to provide agile and sophisticated reasoning. In addition, the paper presents validation results of the automated terrain analysis that were obtained through experiments using military intelligence Subject Matter Experts (SMEs).

Distributed constraint optimization for teams of mobile sensing agents

Coordinating a mobile sensor team (MST) to cover targets is a challenging problem in many multiagent applications. Such applications are inherently dynamic due to changes in the environment, technology failures, and incomplete knowledge of the agents. Agents must adaptively respond by changing their locations to continually optimize the coverage of targets. We propose distributed constraint optimization problems (DCOP)_MST, a new model for representing MST problems that is based on DCOP. In DCOP_MST, agents maintain variables for their physical positions, while each target is represented by a constraint that reflects the quality of coverage of that target. In contrast to conventional, static DCOPs, DCOP_MST not only permits dynamism but exploits it by restricting variable domains to nearby locations; consequently, variable domains and constraints change as the agents move through the environment. DCOP_MST confers three major advantages. It directly represents the multiple forms of dynamism inherent in MSTs. It also provides a compact representation that can be solved efficiently with local search algorithms, with information and communication locality based on physical locality as typically occurs in MST applications. Finally, DCOP_MST facilitates organization of the team into multiple sub-teams that can specialize in different roles and coordinate their activity through dynamic events. We demonstrate how a search-and-detection team responsible for finding new targets and a surveillance sub-team tasked with coverage of known targets can effectively work together to improve performance while using the DCOP_MST framework to coordinate. We propose different algorithms to meet the specific needs of each sub-team and several methods for cooperation between sub-teams. For the search-and-detection team, we develop an algorithm based on the DSA that forces intensive exploration for new targets. For the surveillance sub-team, we adapt several incomplete DCOP algorithms, including MGM, DSA, DBA, and Max-sum, which requires us to develop an efficient method for agents to find the value assignment in their local environment that is optimal in minimizing the maximum unmet coverage requirement over all targets. The disadvantage of dynamic domains based on physical locality is that adaptations of standard local search algorithms tend to become trapped in local optima where targets beyond the immediate range of the agents go uncovered. To address this shortcoming we develop exploration methods to be used with the local search algorithms. Our algorithms are extensively evaluated in a simulation environment. We use a reputation model to determine the individual credibility of agents and consider both additive and submodular joint credibility functions for determining coverage of targets by multiple agents. The performance is measured on two objectives: minimizing the maximum remaining coverage requirement, and minimizing the sum of remaining coverage requirements. Our results show that DSA and MGM with the exploration heuristics outperform the other incomplete algorithms across a wide range of settings. Furthermore, organizing the team into two sub-teams leads to significant gains in performance, and performance continues to improve with greater cooperation between the sub-teams.

Distributed Constraint Optimization for Large Teams of Mobile Sensing Agents

A team of mobile sensors can be used for coverage of targets in different environments. The dynamic nature of such an application requires the team of agents to adjust their locations with respect to changes which occur. The dynamic nature is caused by environment changes, changes in the agents’ tasks and by technology failures. A new model for representing problems of mobile sensor teams based on Distributed Constraint Optimization Problems (DCOP), is proposed. The proposed model, needs to handle a dynamic problem in which the alternative assignments for agents and set of neighbors, derive from their physical location which is dynamic. DCOP MST enables representation of variant dynamic elements which a team of mobile sensing agents face. A reputation model is used to determine the credibility of agents. By representing the dynamic sensing coverage requirements in the same scale as the agents’ credibility, the deployment of sensors in the area can be evaluated and adjusted with correspondence to dynamic changes. In order to solve a DCOP MST, a local (incomplete) search algorithm (MGM MST) based on the MGM algorithm is proposed and combined with various exploration methods. While existing exploration methods are evidently not effective in DCOP MSTs, new exploration methods which are designed for these special applications are found to be successful in our experimental study.

Extending the ONESAF Testbed into a C4ISR Testbed

This article describes how the modeling and simulation environment of the OneSAF Testbed Baseline (OTB) v1.0 has been extended to enable the testing of heterogeneous algorithms that are being designed for real-world C4ISR applications. This has been accomplished by building an architecture that extends functional and logical components of the OTB system in the following ways: the use of the OTB Compact Terrain Database for terrain analysis and preliminary threat assessment, the addition of the RETSINA-OTB Bridge for the real-time query and control of OTB entities, and the addition of new DIS-based sensor entities for interoperation with Command and Control algorithms, to name a few. This article illustrates how to make a few small but general extensions to a modeling and simulation system to create a larger testbed system with minimum impact on the native system and with great potential for the range of applications that can exploit it.

A Markov Random Field Model of Context for High-Level Information Fusion

This paper presents a method for inferring threat in a military campaign through matching of battle field entities to a doctrinal template. In this work the set of random variables denoting the possible template matches for the scenario entities is a realization of a Markov random field. This approach does not separate low level fusion from high level fusion but optimizes both simultaneously. The result of the added high level context is a method that is robust to false positive and false negative, or missed, sensor readings. Furthermore, the high level context helps to direct the search for the best template match. Empirical results illustrate the efficacy of the method both at identifying threats in the face of false negatives, and at negating false positives, as well as illustrating the reduced computational effort resulting from the incorporation of additional high-level context

Intent inference using a potential field model of environmental influences

Intent inferencing is the ability to predict an opposing forces (OPFOR) high level goals. This is accomplished by the interpretation of the OPFORs disposition, movements, and actions within the context of known OPFOR doctrine and knowledge of the environment. For example, given likely OPFOR force size, composition, disposition, observations of recent activity, obstacles in the terrain, cultural features such as bridges, roads, and key terrain, intent inferencing will be able to predict the opposing forces high level goal and likely behavior for achieving it. This paper describes an algorithm for intent inferencing on an enemy force with track data, recent movements by OPFOR forces across terrain, terrain from a GIS database, and OPFOR doctrine as input. This algorithm uses artificial potential fields to discover field parameters of paths that best relate sensed track data from the movements of individual enemy aggregates to hypothesized goals. Hypothesized goals for individual aggregates are then combined with enemy doctrine to discover the intent of several aggregates acting in concert.

Self-Organized Criticality of Belief Propagation in Large Heterogeneous Teams

Large, heterogeneous teams will often be faced with situations where there is a large volume of incoming, conflicting data about some important fact. Not every team member will have access to the same data and team members will be influenced most by the teammates with whom they communicate directly. In this paper, we use an abstract model to investigate the dynamics and emergent behaviors of a large team trying to decide whether some fact is true. Simulation results show that the belief dynamics of a large team have the properties of a Self-Organizing Critical system. A key property of such systems is that they regularly enter critical states, where one additional input can cause dramatic, system wide changes. In the belief sharing case, this criticality corresponds to a situation where one additional sensor input causes many agents to change their beliefs. This can include the entire team coming to a “wrong” conclusion despite the majority of the evidence suggesting the right conclusion. Self-organizing criticality is not dependent on carefully tuned parameters, hence the observed phenomena are likely to occur in the real world.

Automating Terrain Analysis: Algorithms for Intelligence Preparation of the Battlefield

Terrain information supplies an important context for ground operations. The layout of terrain is a determining factor in arraying of forces, both friendly and enemy, and the structuring of Courses of Action (COAs). For example, key terrain, such as a bridge over an unfordable river, or terrain that allows observation of the opposing forces line of advance, is likely to give a big military advantage to the force that holds it. Combining information about terrain features with hypotheses about enemy assets can lead to inferences about possible avenues of approach, areas that provide cover and concealment, areas that are vulnerable to enemy observation, or choke points. Currently, intelligence officers manually combine terrain-based information, information about the tactical significance of certain terrain features as well as information regarding enemy assets and doctrine to form hypotheses about the disposition of enemy forces and enemy intent. In this paper, we present a set of algorithms and tools for automating terrain analysis and compare their results with those of experienced intelligence analysts.

An analysis and design methodology for belief sharing in large groups

Many applications require that a group of agents share a coherent distributed picture of the world given communication constraints. This paper describes an analysis and design methodology for coordination algorithms for extremely large groups of agents maintaining a distributed belief. This design methodology creates a probability distribution which relates global properties of the system to agent interaction dynamics using the tools of statistical mechanics. Using this probability distribution we show that this system undergoes a rapid phase transition between low divergence and high divergence in the distributed belief at a critical value of system temperature. We also show empirically that at the critical system temperature the number of messages passed and belief divergence between agents is optimal. Finally, we use this fact to develop an algorithm using system temperature as a local decision parameter for an agent.