A method for fusing synthetic aperture radar (SAR) images with optical aerial images is presented. This is done in a navigation framework, in which the absolute position and orientation of the flying platform, as computed from the inertial navigation system, is corrected based on the aerial image coordinates taken as ground truth. The method is suitable for new low-price SAR systems for small unmanned vehicles. The primary application is surveillance, and to some extent it can be applied to remote sensing, where the SAR image provides complementary information by revealing reflectivity to microwave frequencies. The method is based on first applying an edge detection algorithm to the images and then optimising the most important navigation states by matching the two binary images. To get a measure of the estimation uncertainty, we embed the optimisation in a least squares framework, in which an explicit method to estimate the (relative) size of the errors is presented. The performance is demonstrated on real SAR and aerial images, leading to an error of only a few pixels (around 4 m in our case), which is a quite satisfactory performance for applications like surveillance and navigation.

Abstract-Synthetic Aperture Radar (SAR) equipment is a radar imaging system that can be used to create high resolution images of a scene by utilising the movement of a flying platform. Knowledge of the platform's trajectory is essential to get good and focused images. An emerging application field is real-time SAR imaging using small and cheap platforms, where estimation errors in navigation systems imply unfocused images. This contribution investigates a joint estimation of the trajectory and SAR image. Starting with a nominal trajectory, we successively improve the image, by optimizing a focus measure and updating the trajectory accordingly. The method is illustrated using simulations using typical navigation performance of an unmanned aerial vehicle. One real data set is used to show feasibility, where the result indicates that in particular the azimuth position error is decreased as the image focus is iteratively improved.

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This paper addresses the problem of retrieving consistent estimates in a distributed network where the communication between the nodes is constrained such that only the diagonal elements of the covariance matrix are allowed to be exchanged. Several methods are developed for preserving and/or recovering consistency under the constraints imposed by the communication protocol. The proposed methods are used in conjunction with the covariance intersection method and the estimation performance is evaluated based on information usage and consistency. The results show that among the proposed methods, consistency can be preserved equally well at the transmitting node as at the receiving node.

Abstract-The dependence of radio signal propagation on the environment is well known, and both statistical and deterministic methods have been presented in the literature. Such methods are either based on randomised or actual reflectors of radio signals. In this work, we instead aim at estimating the location of the reflectors based on geo-localised radio channel impulse response measurements using methods from synthetic aperture radar (SAR). Radio channel measurements from 3GPP E-UTRAN have been used to verify the usefulness of the proposed approach. The obtained images show that the estimated reflectors are well-correlated with the aerial map of the environment. Also, trajectory segment contributions to different reflectors have been estimated with promising results.