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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 Kalman filter to estimate all position derivatives, and the particle filter becomes low-dimensional.(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)
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)
— We present an experimental deployment of an IP-based 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(More)
The terrain-aided navigation problem is a highly nonlinear estimation problem with application to aircraft navigation and missile guidance. In this work the Bayesian approach is used to estimate the aircraft position. With a quantization of the state space an implementable algorithm is found. Problems with low excitation, rough terrain and parallel position(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)
This paper describes general modelling of radar target tracking scenarios. Manoeuvres are modelled using a stochastic continuous-time model. The goal of the paper is to compare two diierent manoeuvre models, called the Markov model and jump model respectively. Both approaches lead to Kalman lter banks, with the number of models kept constant by merging and(More)