Gregory A. McIntyre

Information theoretic approach to sensor scheduling

This paper demonstrates an approach to sensor scheduling and sensor management which effectively deals with the search/track decision problem. Every opportunity a sensor has to sense the environment equates to a certain amount of information which can be obtained about the state of the environment. A fundamental question is how to use this potential information to manage a suite of sensors while maximizing ones net knowledge about the state of the environment. The fundamental problem is whether to use ones resources to track targets already in track or to search for new ones. Inherent in this search/track problem is the further decision as to which sensor to use. A computer model has been developed that simulates a modest multiple sensor, multiple threat scenario. Target maneuvers are modeled using the Singer Model for manned maneuvering vehicles. Each sensors capabilities and characteristics are captured in the model by converting their energy constraints to a probability of detecting a target as a function of range and field of view (beamwidth). The environment is represented by a probability distribution of a target being at a given location. As the environment is sensed and targets are detected, the environments probability distribution is continually updated to reflect the new probability state of the environment. This probability state represents the systems best estimate about the location of all targets in track and the probable location of, as yet undetected, targets.

Goal lattices for sensor management

A new methodology for quantifying the relative contribution of specific sensor actions to a set of mission goals is presented. The mission goals are treated as a set, and an ordering relationship is applied to it leading to a partially ordered set which can be represented as a lattice. At each layer in the lattice, each goals value is computed as the sum of the (higher) goals in which it is included and its value is apportioned among the (lower) goals which it includes. A system designer is forced to make a zero-sum apportionment of each goals value among those goals which it includes. The net result of this methodology is a quantifiable measure of the contributing value of each real type of sensor action to the system of goals, leading to more effective allocation of resources. While applied here to sensor scheduling, the method has applications to other decision making processes as well.

Sensor measurement scheduling: an enhanced dynamic, preemptive algorithm

This paper presents an enhanced architecture for a sensor measurement scheduler as well as a dynamic sensor scheduling algorithm called the On-line, Greedy, Urgency-driven, Preemptive Scheduling Algorithm (OGUPSA). The premise is that the function of sensor management can be partitioned into the two tasks of information management, essentially an information to measurement mapping, and a sensor scheduler which takes the measurement requests along with their priorities and optimally maps them to a set of sensors. OGUPSA was developed using the three main scheduling policies of Most-Urgent-First to pick a task, EarliestCompleted-First to select a sensor, and Least-Versatile-First to resolve ties. By successive application of these policies. OGUPSA dynamically allocates, schedules, and distributes a set of measurement tasks from an information manager among a set of sensors. OGUPSA can detect the failure of a measurement task to meet a deadline and improves the dynamic load balance among all sensors while being a polynomial time algorithm. One of the key components of OGUPSA is the information in the applicable sensor table. This table is the mechanism that is used to assign requested tasks to specific sensors. Subject terms: sensor scheduling, sensor management, resource scheduling, sensors

Applications of discrete event simulation modeling to military problems

The military is a big user of discrete event simulation models. The use of these models range from training and wargaming their constructive use in important military analyses. In this paper we discuss the uses of military simulation, the issues associated with military simulation to include categorizations of various types of military simulation. We then discuss three particular simulation studies undertaken with the Air Force Institute of Technologys Department of Operational Science focused on important Air Force and Army issues.

Comparison of several maneuvering target tracking models

The tracking of maneuvering targets is complicated by the fact that acceleration is not directly observable or measurable. Additionally, acceleration can be induced by a variety of sources including human input, autonomous guidance, or atmospheric disturbances. The approaches to tracking maneuvering targets can be divided into two categories both of which assume that the maneuver input command is unknown. One approach is to model the maneuver as a random process. The other approach assumes that the maneuver is not random and that it is either detected or estimated in real time. The random process models generally assume one of two statistical properties, either white noise or an autocorrelated noise. The multiple-model approach is generally used with the white noise model while a zero-mean, exponentially correlated acceleration approach is used with the autocorrelated noise model. The nonrandom approach uses maneuver detection to correct the state estimate or a variable dimension filter to augment the state estimate with an extra state component during a detected maneuver. Another issue with the tracking of maneuvering target is whether to perform the Kalman filter in Polar or Cartesian coordinates. This paper will examine and compare several exponentially correlated acceleration approaches in both Polar and Cartesian coordinates for accuracy and computational complexity. They include the Singer model in both Polar and Cartesian coordinates, the Singer model in Polar coordinates converted to Cartesian coordinates, Helfertys third order rational approximation of the Singer model and the Bar-Shalom and Fortmann model. This paper shows that these models all provide very accurate position estimates with only minor differences in velocity estimates and compares the computational complexity of the models.

Using agent-based modeling to capture airpower strategic effects

Airpowers strength lies in quickly striking the enemy directly where they are vulnerable while being unhampered by geography and surface forces. Airpower theory suggests the effects of these strikes propagate throughout an opponents military system, yielding catastrophic output or strategic effects. Despite this theory being a cornerstone of US Air Force doctrine, current Air Force models do not seem to capture airpowers inherent strength. Since these models are used to support budgetary decision making, the US may not be funding the airpower capability it needs. The article focuses on developing an approach to capture strategic effects in models. The approach establishes a basis for the effects in military theory as well as the field of complex adaptive systems. Using these concepts as a foundation, a simulation model called the Hierarchical Interactive Theater Model (HITM) is constructed and exercised. HITM output depicts a cascading deterioration in force effectiveness and eventual total collapse resulting from destruction of vital targets. This outcome is consistent with the expected results of strikes against centers of gravity defined in Air Force doctrine, suggesting agent based modeling is an effective way to simulate strategic effects at the operational level of war.

Information instantiation in sensor management

One of the tasks associated with a heterogeneous, multi-sensor system is the determination of which function to perform (search, track, or identification). Previous work by the authors has focused on the use of the information gain attributable to a reduction in the kinematic, identification, or search uncertainty as a useful cost function for making the trade-off among the possible uses of a sensor. This view has been subsumed as a subcomponent of a new approach (not covered here) which quantitatively apportions goal-values ordered in a lattice among the several tasks. That is, rather than use an information measure to determine whether to search, track, or identification, an information instantiator is used to determine when to schedule the next observations of a target in order to insure that tracks are maintained, areas of uncertainty are searched, and important targets identified. The scheduling of these observations among the various sensors is optimized separately using the previously developed OGUPSA algorithm. Information instantiation is a collection of methods used to convert information needs at the mission management level to the actual type of measurement(s) to make. This paper describes these methods which are used to schedule measurements of search areas with associated probabilities of detection to meet search information needs, obtain measurements of a target in track to reduce its kinematic uncertainty to a specified level, and to reduce the uncertainty about a targets identity as both a specific information gain in identification and capitalize on that ID to increase target track accuracy. A brief description and block diagram of our complete sensor management model is also presented to show the interrelationship of the information instantiator to the other components.

Sensor management simulation and comparative study

Within the framework of a command and control system, vast amounts of data are being collected and processed from a variety of dissimilar sensors. Through sensor management, sensor usage is integrated to accomplish specific and often dynamic mission objectives. Every opportunity a sensor has to measure the environment can be equated to a reduction in uncertainty in its state, and hence a quantifiable amount of information. A difficulty arises when the data from sensors is not directly comparable as in the case of kinematic and nonkinematic sensors. This paper expands on our previous work, in which a modest multiple sensor, multiple threat simulation model was built to demonstrate the use of Information Theory in sensor management. The simulation model was used to demonstrate the use of Information Theory to effectively deal with the target tracking and target search decision problem. This paper builds upon that work by implementing the OGUPSA sensor scheduling algorithm in the simulation model with more fidelity by replacing the unit interval tasks by appropriate non-unit interval tasks and compares several sensor management methods including minimum position error and maximum information.

View from the top: military challenges for the simulation community

The Department of Defense (DoD) has become increasingly reliant on models and in particular on simulation models. The military-defense establishment and its combat-preparation orientation is one of the most complex systems in existence, particularly in the extremely dynamic modern world. Simulation models form the basis for analyses spanning issues ranging from force structuring to acquisition prioritization. These analyses, and the subsequent decisions they support, mold and shape the DoD thereby influencing the posture of the US defense establishment. This panel brings together a set of the militarys influential decision makers directly involved in the development and use of simulation models. The panel discusses their current and anticipated needs for the future of simulation and pose the challenges the simulation community must meet to ensure those future needs are met.

A Validation Assessment of THUNDER 6.5's Intelligence, Surveillance, and Reconnaissance Module

Abstract : A validation assessment of THUNDER 6.5s Intelligence, Surveillance, and Reconnaissance (ISR) module is accomplished using formulational and experimental validation techniques. A comparison of ISR purposes and processes according to military doctrine is made with the purposes and processes of ISR implemented within THUNDER 6.5. This comparison provides an overview of the process, an understanding of the level of aggregation within THUNDER, insight into possible problem areas in THUNDER, and a basis for improving THUNDER ISR processes. Sensitivity analysis of the ISR parameters as they relate to the Quality, Quantity, and Timeliness of ISR is also presented to provide insight into the responsiveness of THUNDER to changes in ISR capability for selected battle outcomes. Linear Regression and a Face-Centered Central Composite Design were used to generate a response surface. Ninety-percent confidence intervals were used to determine differences in mean response among the full factorial design points.