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We develop an Expectation-Maximization (EM) algorithm for the simultaneous tracking and shape estimation of a star-convex object based on multiple spatially distributed measurements. In order to formulate the problem within the EM framework, the unknown measurement sources on the object are modeled as hidden variables. As the measurement sources are continuous quantities, we develop a suitable discretization...
This work considers the problem of tracking a mobile object with an unknown shape based on noisy point cloud measurements from the object contour. For this purpose, an Expectation Maximization (EM) method is developed that is capable of simultaneously estimating the location and shape parameters of an object based on a temporal sequence (i.e., batch) of point clouds. The benefits of the EM approach...
Driven by real-world issues in maritime surveillance, we consider the problem of estimating the target state from a sequence of observations that can be imprecisely time-stamped. That is, the time between two consecutive observations can be affected by an unknown error or delay. We propose an adaptive filtering strategy able to sequentially detect the time delays and correctly estimate the target...
The Ensemble Kalman Filter (EnKF) is a Kalman based particle filter which was introduced to solve large scale data assimilation problems where the state space is of very large dimensionality. It also achieves good results when applied to a target tracking problem, however, due to its Gaussian assumption for the prior density, the performance can be improved by introducing Gaussian mixtures. In this...
In active sonar and radar applications measurements consist of range, bearing and often range rate — all nonlinear functions of the target state (usually modeled in Cartesian coordinates). The converted measurement Kalman filter (CMFK) first converts the range and bearing measurements into Cartesian coordinates to allow for the use of a linear Kalman filter. The extension of the CMKF to use range...
In target tracking, it is natural to describe target motion in Cartesian coordinates. In many cases the measurements require some form of nonlinear conversion prior to use in a Cartesian coordinate tracker. There are two sources of bias that can arise as a result of this conversion. The first occurs when the conversion process introduces a bias in the expected value of the converted measurement. The...
In many target tracking problems it is advantageous to perform tracking in a different coordinate system than the measurements. In these cases, the measurements require some form of conversion prior to use in tracking. There are two potential issues that arise when performing converted measurement tracking. The first occurs when the measurement conversion results in a biased (converted) measurement...
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