Niclas Bergman

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A framework for positioning, navigation and tracking problems using particle filters (sequential Monte Carlo methods) is developed. It consists of a class of motion models and a general non-linear measurement equation in position. A general algorithm is presented, which is parsimonious with the particle dimension. It is based on marginalization, enabling a(More)
We present an experimental deployment of an IPbased wireless sensor network that is intended to operate as an intrusion monitoring system. This network is the first actual deployment of a fully IP-based wireless sensor network with small and computationally constrained sensor nodes. The intrusion monitoring system detects motion in a building which should(More)
In this paper the tracking of ground targets using acoustic sensors, distributed in a wireless sensor network, is studied. Since only acoustic sensors are utilized in the study the tracking problem can be regarded as a bearings-only application. The solution to the problem is given within the Bayesian recursive framework, where a sequential Monte Carlo(More)
i Abstract Terrain navigation is a concept for autonomous aircraft navigation. If measurements of the terrain height over mean sea-level are collected along the aircraft ight path, an estimate of the aircraft position can be formed by matching these measurements with a digital reference terrain map. This matching is a recursive nonlinear estimation problem.(More)
Terrain aided aircraft navigation can be used to detect and correct errors in inertial navigation systems The idea of terrain aided naviga tion is to measure the vertical distance to the ground and compare it with a map on board the aircraft The comparison between the mea surements and the map can be made in a number of ways Both batch and recursive(More)
The nonlinear estimation problem in navigation using terrain height variations is studied. The optimal Bayesian solution to the problem is derived. The implementation is grid based, calculating the probability of a set of points on an adaptively dense mesh. The Cramer-Rao bound is derived. Monte Carlo simulations over a commercial map shows that the(More)