Swedish Defence Research Agency

About: Swedish Defence Research Agency is a government organization based out in Stockholm, Sweden. It is known for research contribution in the topics: Radar & Laser. The organization has 1413 authors who have published 2731 publications receiving 56083 citations. The organization is also known as: Totalförsvarets forskningsinstitut.

Peer-to-peer-based resource management in-support of HLA-based distributed simulations

Abstract: In recent years, the concept of peer-to-peer computing has gained renewed interest for sharing resources within and between organizations or individuals. This article describes a decentralized reso ...

Particle filter-based information acquisition for robust plan recognition

Abstract: Plan recognition generates high-level information of opponents' plans, typically a probability distribution over a set of plausible plans. Estimations of plans are in our work, made at different decision-levels, both company-level and the subsumed platoon-level. Naturally, successful plan recognition is heavily dependent on the data that is supplied, and, hence, sensor management is a necessity. A key feature of the sensor management discussed here is that it is driven by the information need of the plan recognition process. In our research, we have presented a general framework for connecting information need to sensor management. In our framework implementation, an essential part is the prioritization of sensing tasks, which is necessary to efficiently utilize limited sensing resources. In our first implementation, the priorities were calculated from, for instance, the estimated threats of opponents (as a function of plan estimates), the distance to the opponent, and the uncertainty in its position. In this article, we add a particle filter method to more carefully represent the uncertainty in the opponent state estimate to make prioritization more well founded and, ultimately, to achieve robust plan recognition. By using the particle filter, we can obtain more reliable state estimate (through the particle filter's ability to represent complex probability distributions) and also a statistically based threat variation (through Monte-Carlo simulation). The state transition model of the particle filter can also be used to predict future states to direct sensors with a time delay (a common property of large-scale sensing systems), such as sensors mounted on UAVs that have to travel some distance to make a measurement.

SVED: Scanning, Vulnerabilities, Exploits and Detection

Abstract: This paper presents the Scanning, Vulnerabilities, Exploits and Detection tool (SVED). SVED facilitates reliable and repeatable cyber security experiments by providing a means to design, execute and log malicious actions, such as software exploits, as well the alerts provided by intrusion detection systems. Due to its distributed architecture, it is able to support large experiments with thousands of attackers, sensors and targets. SVED is automatically updated with threat intelligence information from various services.

Lidar for shallow underwater target detection

Abstract: Small underwater objects such as vehicles and divers can pose threats to fixed installations and ships. For ships, these threats are present both at sea and in harbors. Shallow underwater targets, including drifting mines, are difficult to detect with acoustic methods and thus complementary methods are required. If an airborne platform is available, some of those targets could be detected by passive optical means. However, for sensing from a ship or from land, optical detection can be highly improved by use of a pulsed laser system. We present simulated data of importance for the design of a lidar system with low incidence angle with respect to the water surface. We also present our first experimental data from underwater target detection with an incidence angle of 5 degrees.