Accurate positioning, navigation and timing (PNT) is important for military and civilian applications alike. In recent years navigation using signals of opportunity (SOPs) has received increased interest as a complement or alternative to Global Navigation Satellite Systems (GNSS). We previously proposed a localization framework using opportunistic digital television signals. In it, a navigator localizes itself aided by a stationary base station at a known location, using two time difference of arrival (TDOA) measurements and one twoway ranging (TWR) measurement. In this work we implement and evaluate the proposed framework using real measurements, and based on this propose an extended model which includes the difference in clock bias between the navigator and the base station. The experimental results show that the framework can achieve a root mean square error (RMSE) in absolute position of less than 50 m provided that at least three TDOA measurements are used (with or without TWR), and sometimes if only two TDOA measurements are used in combination with a TWR measurement. We also show experimentally that the transmitter clocks are stable enough for the navigator to extract the TDOA measurements relying only on its own current measurements and previously collected data by the base station. Thus, by replacing the TWR measurement with a third TDOA measurement, the base station need not be active or communicate with the navigator during the localization. In scenarios where communication is undesirable or impossible, this can be advantageous.

This paper presents a camera-based reference system for positioning. The system provides reliable ground truth data in relatively large indoor environments using a minimum of preinstalled infrastructure. The reference system will be used together with a foot-mounted inertial navigation system (INS) with the purpose to evaluate how errors in the foot-mounted INS grow over time. The reference system itself is evaluated and shown to provide sufficient accuracy.

Correlation is often used to measure the similarity between signals and is an important tool in signal and image processing. In some applications it is common that signals are corrupted by local bursts of noise. This adversely affects the performance of signal recognition algorithms. This paper presents a novel correlation estimator, which is robust to locally corrupted signals. The estimator is generalized to multivariate correlation analysis (general linear model, GLM, and canonical correlation analysis, CCA). Synthetic functional MRI data is used to demonstrate the estimator, and its robustness is shown to increase the performance of signal detection.

This paper introduces a new method for automatic quantification of subcutaneous, visceral and non-visceral internal fat from MR-images acquired using the two point Dixon technique in the abdominal region. The method includes (1) a three dimensional phase unwrapping to provide water and fat images, (2) an image intensity inhomogeneity correction, and (3) a morphon based registration and segmentation of the tissue. This is followed by an integration of the corrected fat images within the different fat compartments that avoids the partial volume effects associated with traditional fat segmentation methods. The method was tested on 18 subjects before and after a period of fast-food hyper-alimentation showing high stability and performance in all analysis steps.

The inverse gradient problem, finding a scalar field f with a gradient near a given vector fieldgon some bounded and connected domain Ω ϵ R𝓃, can be solved by means of a Poisson equation with inhomogeneous Neumann boundary conditions. We present an elementary derivation of this partial differential equation and an efficient multigrid-based method to numerically compute the inverse gradient on non-rectangular domains. The utility of the method is demonstrated by a range of important medical applications such as phase unwrapping, pressure computation, inverse deformation fields, and fiber bundle tracking.

In analysis of fMRI data, it is common to average neighboring voxels in order to obtain robust estimates of the correlations between voxel time-series and the model of the signal expected to be present in activated regions. We have previously proposed a method where only voxels with similar correlation coefficients are averaged. In this paper we extend this idea, and present a novel method for analysis of fMRI data. In the proposed method, only voxels with similar correlation coefficients and similar time-series are averaged. The proposed method is compared to our previous method and to two well-known filtering strategies, and is shown to have superior ability to discriminate between active and inactive voxels

It has previously been shown that canonical correlation analysis (CCA) works well for detecting neural activity in fMRI data. This is due to the ability of CCA to perform simultaneous temporal modeling and adaptive spatial filtering of the data. In this paper, we demonstrate that our previously proposed method for CCA-based fMRI data analysis does not provide rotationally invariant detection of activated regions. We propose a modification of the previous method and show that it resolves the rotational invariance issue, thereby further improving the analysis method.