Khin Hua Ng

Sensor management – what, why and how

Abstract This paper presents a comprehensive discussion and systematic classification of the sensor management (SM) with respect to its roles, concepts, architectures and techniques. The SM is put in perspective with the discussion of the sensing system framework. The role of SM was presented based on the different level of its functionality. The architectures discussed in this paper include centralized, decentralized and hierarchical methods. SM techniques such as scheduling and decision-making techniques are discussed. A generic SM framework is also presented.

Multiobjective optimization of sensor network deployment by a genetic algorithm

Decision support tools for assisting the human mission planner in the deployment of sensor networks is an important component of sensor management. The optimal selection of the number and types of sensors available from a suite of sensors, and their optimal placement in the terrain, is typically a multiobjective optimization problem with objectives defined based on the mission and scenario. One of the key advantages in applying multiobjective genetic algorithms for solving such problems is their ability to find multiple Pareto optimal solutions in a single run which then allows for the mission planner to select a final optimal solution based on higher-level considerations. The aim in this work is an effective genetic algorithm implementation of such a decision support tool for the deployment of sensor networks.

A cognitive architecture for knowledge exploitation

A cognitive architecture specifies a computational infrastructure that defines the various regions and functions working as a whole to produce human-like intelligence [Newell, 1990]. It also defines the main connectivity and information flow between the various regions and functions. These functions and the connectivity between them in turn facilitate and provide implementation specifications for a variety of algorithms. Drawing inspirations from computational science, neuroscience, psychology and biology, a top-level cognitive architecture which models the information processing in human brain is developed. Four key design principles [Ng, 2009] inspired by the brain, namely the hierarchical structure, distributed memory, parallelism in information flow and pathways, are incorporated into the architecture. A prototype cognitive architecture is developed and it is able to bring to bear different types of knowledge to solve a problem. It has been applied to object recognition in images. The cognitive architecture is able to exploit bottom-up perceptual information, top-down contextual knowledge and visual feedback in a way similar to how human utilizes different knowledge to recognize objects in images.

Application of intent inference for surveillance and conformance monitoring to aid human cognition

Intent inference involves analyzing the actions and activities of a target of interest to deduce its purpose. In an environment cluttered with many targets, loaded with information, and under stress, the human may not be able to perform well Hence a cognitive aid that could derive possible intent inference and monitor the target may help augment human cognition and assist critical human decision making. This paper reports research on two applications: determining the likelihood of weapon delivery by an attack aircraft under military surveillance and conformance monitoring in air traffic control systems. The proposed solution is based on flight profile analysis. Simulation process comprises Interacting Multiple Model-based state estimation and Mamdani-type fuzzy inference to deduce possibilities of weapon delivery and of nonconforming aircraft behavior respectively. Results verify that the method is feasible and provides timely inference that will aid human cognition and hence assist decision making.

Sensor management - control and cue

Presents the role of sensor management with respect to the sensor system and the fusion process. The important roles and functions of sensor management are discussed. A multi-level classification of the sensor management is presented. The control and cueing aspects of sensor management are explored. An experiment is set up to demonstrate a sensor manager controlling an electro-optical (EO) sensors direction of motion and the sensor cueing process to track a target. A fuzzy controller is used in this experiment. The sensor fusion system used an interactive multiple model (IMM) algorithm to give the estimated target direction.

Intent inference for attack aircraft through fusion

Intent inference is about analyzing the actions and activities of an adversarial force or target of interest to reach a conclusion (prediction) on its purpose. In this paper, we report one of our research works on intent inference to determine the likelihood of an attack aircraft being tracked by a military surveillance system delivering its weapon. Effective intent inference will greatly enhance the defense capability of a military force in taking preemptive action against potential adversaries. It serves as early warning and assists the commander in his decision making. For an air defense system, the ability to accurately infer the likelihood of a weapon delivery by an attack aircraft is critical. It is also important for an intent inference system to be able to provide timely inference. We propose a solution based on the analysis of flight profiles for offset pop-up delivery. Simulation tests are carried out on flight profiles generated using different combinations of delivery parameters. In each simulation test, the state vectors of the tracked aircraft are updated via the application of the Interacting Multiple Model filter. Relevant variables of the filtered track (flight trajectory) are used as inputs to a Mamdani-type fuzzy inference system. The output produced by the fuzzy inference system is the inferred possibility of the tracked aircraft carrying out a pop-up delivery. We present experimental results to support our claim that the proposed solution is indeed feasible and also provides timely inference that will assist in the decision making cycle.

DSO Cognitive Architecture: Unified Reasoning with Integrative Memory Using Global Workspace Theory

In this work, we present a design enhancement to the DSO Cognitive Architecture to augment its existing cognitive functions in an attempt to produce more general level of artificial intelligence in computational intelligent systems. Our design is centered on the concept of unified reasoning that indirectly addresses the diversity dilemma in designing cognitive architectures. This is done by implementing an integrative memory with the incorporation of the Global Workspace Theory. We discuss how other cognitive architectures using the Global Workspace Theory have influenced our design and also demonstrate how the new design can be used to solve an image captioning problem.

Sensor management: coverage versus survivability - a multiple-objective optimization problem

The Sensor Management Team from DSO National Laboratories has developed a Decision Support System (DSS) to assist human operators in determining the most effective employment and/or deployment of a suite of sensors given a particular mission or operational scenario. The key issue addressed by the system is the resource allocation problem accompanied by two contradictory objectives, namely to maximize combined coverage of the sensor suite and to maximize survivability of the sensor within the suite. Furthermore, the system is to handle operational constraints on the usage of the suite of sensors. In this paper, we will describe how we handle the problem by formulating it as a Multiple Objective Optimization (MOO) problem. This system may be used as a pre-mission planning tool or a real time decision aid for the sensor suite commander. With the increase in size of the sensor suite and the number of possible deployment sites, the feasibility space of the employment/deployment configurations will grow tremendously. In order to allow for near real time decision support, the team has incorporated genetic algorithm to solve the MOO problem.

DSO Cognitive Architecture: Implementation and Validation of the Global Workspace Enhancement

An enhanced DSO Cognitive Architecture design was recently introduced to augment its cognitive functions by incorporating the Global Workspace Theory. A computational implementation of this new design is described in detail in this paper. The implementation is built as a distributed system with parallel pipelines of specialised processes, executing asynchronously. Competition initiated by these processes, and facilitated by the attention mechanism and global broadcast mechanism, leads to pipelines being dynamically created and allows disconnected pipelines to influence the processing of others. To validate the implementation, it was applied to a traffic control problem and experimental results showed increase in performance gain using the enhanced cognitive architecture.