Anders Dahlbom

Trajectory clustering for coastal surveillance

Achieving superior situation awareness is a key task for military, as well as civilian, decision makers. Today, automatic systems provide us with an excellent opportunity for assisting the human decision maker in achieving this awareness. Due to the potential of information overload one important aspect is to understand where to focus attention. Anomaly detection is concerned with finding deviations from normalcy and it is an increasingly important topic when providing decision support, since it can give hints towards where more analysis is needed. In this paper we explore trajectory clustering as a means for representing normal behavior in a coastal surveillance scenario. Trajectory clustering however suffers from some drawbacks in this type of setting and we therefore propose a new approach, spline-based clustering, with a potential for solving the task of representing the normal course of events.

Towards template-based situation recognition

The process of tracking and identifying developing situations is an ability of importance within the surveillance domain. We refer to this as situation recognition and believe that it can enhance situation awareness for decision makers. Situation recognition requires that many subproblems are solved. For instance, we need to establish which situations are interesting, how to represent these situations, and which inferable events and states that can be used for representing them. We also need to know how to track and identify situations and how to determine the correlation between present information about situations with knowledge. For some of these subproblems, data-driven approaches are suitable, whilst knowledge-driven approaches are more suitable for others. In this paper we discuss our current research efforts and goals concerning template-based situation recognition. We provide a categorization of approaches for situation recognition together with a formalization of the template-based situation recognition problem. We also discuss this formalization in the light of a pick-pocket scenario. Finally, we discuss future directions for our research on situation recognition. We conclude that situation recognition is an important problem to look into for enhancing the overall situation awareness of decision makers.

Transparency of military threat evaluation through visualizing uncertainty and system rationale

Threat evaluation (TE) is concerned with determining the intent, capability and opportunity of detected targets. To their aid, military operators use support systems that analyse incoming data and make inferences based on the active evaluation framework. Several interface and interaction guidelines have been proposed for the implementation of TE systems; however there is a lack of research regarding how to make these systems transparent to their operators. This paper presents the results from interviews conducted with TE operators focusing on the need for and possibilities of improving the transparency of TE systems through the visualization of uncertainty and the presentation of the system rationale.

Situation Recognition and Hypothesis Management Using Petri Nets

Situation recognition --- the task of tracking states and identifying situations --- is a problem that is important to look into for aiding decision makers in achieving enhanced situation awareness. The purpose of situation recognition is, in contrast to producing more data and information, to aid decision makers in focusing on information that is important for them, i.e. to detect potentially interesting situations. In this paper we explore the applicability of a Petri net based approach for modeling and recognizing situations, as well as for managing the hypothesis space coupled to matching situation templates with the present stream of data.

Extending the scope of situation analysis

The use of technology to assist human decision making has been around for quite some time now. In the literature, models of both technological and human aspects of this support can be identified. However, we argue that there is a need for a unified model which synthesizes and extends existing models. In this paper, we give two perspectives on situation analysis: a technological perspective and a human perspective. These two perspectives are merged into a unified situation analysis model for semi-automatic, automatic and manual decision support (SAM)2. The unified model can be applied to decision support systems with any degree of automation. Moreover, an extension of the proposed model is developed which can be used for discussing important concepts such as common operational picture and common situation awareness.

A component-based simulator for supporting research on situation recognition

Research on information fusion and situation management within the military domain, is often focused on data-driven approaches for aiding decision makers in achieving situation awareness. We have in a companion paper identified situation recognition as an important topic for further studies on knowledge-driven approaches. When developing new algorithms it is of utmost importance to have data for studying the problem at hand (as well as for evaluation purposes). This often become a problem within the military domain as there is a high level of secrecy, resulting in a lack of data, and instead one often needs to resort to artificial data. Many tools and simulation environments can be used for constructing scenarios in virtual worlds. Most of these are however data-centered, that is, their purpose is to simulate the real-world as accurately as possible, in contrast to simulating complex scenarios. In high-level information fusion we can however often assume that lower-level problems have already been solved - thus the separation of abstraction - and we should instead focus on solving problems concerning complex relationships, i.e. situations and threats. In this paper we discuss requirements that research on situation recognition puts on simulation tools. Based on these requirements we present a component-based simulator for quickly adapting the simulation environment to the needs of the research problem at hand. This is achieved by defining new components that define behaviors of entities in the simulated world.

Situation Modeling and Visual Analytics for Decision Support in Sports

High performance is the goal in most sporting activities, for elite athletes as well as for recreational practitioners, and the process of measuring, evaluating and improving performance is one fundamental aspect to why people engage in sports. This is a complex process which possibly involves analyzing large amounts of heterogeneous data in order to apply actions that change important properties for improved outcome. The number of computer based decision support systems in the context of data analysis for performance improvement is scarce. In this position paper we briefly review the literature, and we propose the use of information fusion, situation modeling and visual analytics as suitable tools for supporting decision makers, ranging from recreational to elite, in the process of performance evaluation.

Fuzzy, I-fuzzy, and H-fuzzy partitions to describe clusters

In this paper we discuss how three types of fuzzy partitions can be used to describe the results of three types of cluster structures. Standard fuzzy partitions are suitable for centroid based clusters, and I-fuzzy partitions for clusters represented by segments or lines (e.g., c-varieties). In this paper, we introduce hesitant fuzzy partitions. They are suitable for clusters defined by sets of centroids. Because of that, we show that they are useful for hierarchical clustering. We also establish the relationship between hesitant fuzzy partitions and I-fuzzy partitions.

Mining Trackman Golf Data

Recently, innovative technology like Trackman has made it possible to generate data describing golf swings. In this application paper, we analyze Trackman data from 275 golfers using descriptive statistics and machine learning techniques. The overall goal is to find non-trivial and general patterns in the data that can be used to identify and explain what separates skilled golfers from poor. Experimental results show that random forest models, generated from Trackman data, were able to predict the handicap of a golfer, with a performance comparable to human experts. Based on interpretable predictive models, descriptive statistics and correlation analysis, the most distinguishing property of better golfers is their consistency. In addition, the analysis shows that better players have superior control of the club head at impact and generally hit the ball straighter. A very interesting finding is that better players also tend to swing flatter. Finally, an outright comparison between data describing the club head movement and ball flight data, indicates that a majority of golfers do not hit the ball solid enough for the basic golf theory to apply.

On This Book: Clustering, Multisets, Rough Sets and Fuzzy Sets

This chapter gives an overview of the content of this book, and links them with the work of Prof. Sadaaki Miyamoto, to whom this book is dedicated.