Highly squinted synthetic aperture radar (SAR) data focusing is a challenging problem with difficulty to correct the severe range-azimuth coupling and motion errors. Squint minimization processing with the range-walk correction is widely adapted to simplify the decoupling processing, while it destructs the azimuth-shift invariance of conventional SAR transfer function. In this paper, a two-stage focusing algorithm (TSFA) is proposed to generate a focused imagery for the highly squinted airborne SAR. In the proposed algorithm, conventional range cell migration correction and azimuth matched filtering are performed and a fine focusing stage is established to correct the azimuth variance. In the fine focusing procedure, the coarse-focused image is divided into azimuth blocks to accommodate the correction of azimuth-variant residual range migration and phase terms. Moreover, precise motion compensation is embedded into the TSFA procedure to form an accurate airborne SAR imagery, which may be called the extended TSFA. In order to balance the processing precision and computational load, optimal selection of block size is investigated in detail. Both simulated and real measured airborne SAR data sets are used to validate the proposed approaches.

Global Navigation Satellite Systems (GNSS) deliver absolute position and velocity, as well as time information (P, V, T). However, in urban areas, the GNSS navigation performance is restricted due to signal obstructions and multipath. This is especially true for applications dealing with highly automatic or even autonomous driving. Subsequently, multi-sensor platforms including laser scanners and cameras, as well as map data are used to enhance the navigation performance, namely in accuracy, integrity, continuity and availability. Although well-established procedures for integrity monitoring exist for aircraft navigation, for sensors and fusion algorithms used in automotive navigation, these concepts are still lacking. The research training group i.c.sens, integrity and collaboration in dynamic sensor networks, aims to fill this gap and to contribute to relevant topics. This includes the definition of alternative integrity concepts for space and time based on set theory and interval mathematics, establishing new types of maps that report on the trustworthiness of the represented information, as well as taking advantage of collaboration by improved filters incorporating person and object tracking. In this paper, we describe our approach and summarize the preliminary results.

In this paper, we propose a new technique—called Ellipsoidal and Gaussian Kalman filter—for state estimation of discrete-time nonlinear systems in situations when for some parts of uncertainty, we know the probability distributions, while for other parts of uncertainty, we only know the bounds (but we do not know the corresponding probabilities). Similarly to the usual Kalman filter, our algorithm is iterative: on each iteration, we first predict the state at the next moment of time, and then we use measurement results to correct the corresponding estimates. On each correction step, we solve a convex optimization problem to find the optimal estimate for the system’s state (and the optimal ellipsoid for describing the systems’s uncertainty). Testing our algorithm on several highly nonlinear problems has shown that the new algorithm performs the extended Kalman filter technique better—the state estimation technique usually applied to such nonlinear problems.

In relation to the computation of electromagnetic scattering in layered media by the Gabor-frame-based spatial spectral Maxwell solver, we present two methods to compute the Gabor coefficients of the transverse cross section of three-dimensional scattering objects with high accuracy and efficiency. The first method employs the analytically obtained two-dimensional Fourier transform of the cross section of a scattering object, which we describe by two-dimensional characteristic functions, in combination with the traditional discrete Gabor transform method for computing the Gabor coefficients. The second method concerns the expansion of the so-called dual window function to compute the Gabor coefficients by employing the divergence theorem. Both methods utilize (semi)-analytical approaches to overcome the heavy oversampling requirement of the traditional discrete Gabor transform method in the case of discontinuous functions. Numerical results show significant improvement in terms of accuracy and computation time for these two methods against the traditional discrete Gabor transform method.