The imperfections of image acquisition systems produce noise. The majority of edge detectors, including gradient-based edge detectors, are sensitive to noise. To reduce this sensitivity, the first step of some edge detectors’ algorithms, such as the Canny’s edge detector, is the filtering of acquired images with a Gaussian filter. We show experimentally that this filtering is not sufficient in case of strong Additive White Gaussian or multiplicative speckle noise, because the remaining grains of noise produce false edges. The aim of this paper is to improve edge detection robustness against Gaussian and speckle noise by preceding the Canny’s edge detector with a new type of denoising system. We propose a two-stage denoising system acting in the Hyperanalytic Wavelet Transform Domain. The results obtained in applying the proposed edge detection method outperform state-of-the-art edge detection results from the literature.

A way to reduce the uncertainty at the output of a Kalman filter embedded into a tracker connected to an automotive RADAR sensor consists of the adaptive selection of parameters during the tracking process. Different informed strategies for automatically tuning the tracker’s parameters and to jointly learn the parameters and state/output sequence using: expectation maximization; optimization approaches, including the simplex algorithm; coordinate descent; genetic algorithms; nonlinear programming using finite differencing to estimate the gradient; Bayesian optimization and reinforcement learning; automatically tuning hyper-parameters in the least squares, were already proposed. We develop here a different semi-blind post-processing approach, which is faster and more robust. Starting from the conjecture that the trajectory is polynomial in Cartesian coordinates, our method supposes to fit the data obtained at the output of the tracker to a polynomial. We highlight, by simulations, the improvement of the estimated trajectory’s accuracy using the polynomial fitting for single and multiple targets. We propose a new polynomial fitting method based on wavelets in two steps: denoising and polynomial part extraction, which compares favorably with the classical polynomial fitting method. The effect of the proposed post-processing methods is visible, the accuracy of targets’ trajectories estimations being hardly increased.

A Kalman based trajectory estimate always has an associated uncertainty characterized by its covariance. A way to reduce this uncertainty consists in the adaptive selection of parameters during the filtering process. Different informed strategies for automatically tuning the parameters in Kalman smoothers were already proposed. We propose here a different semi-blind post processing approach, which is faster and more robust. Starting from the conjecture that the trajectory is polynomial in cartesian coordinates, our method supposes to fit the data obtained at the output of Kalman filter to a polynomial. We propose a new polynomial fitting method based on wavelets in two steps: denoising and polynomial part extraction and we compare favorably its performance with the performance of classical polynomial fitting method in an experiment using real data.

This paper describes the simulations of EGNOS performances improvements when using Ranging & Integrity Monitoring Stations (RIMS) sites in Romania, namely in Timisoara, Cluj-Napoca, Iasi and Bucharest. A reference scenario is created for the last EGNOS release, using the Service Volume Simulator SBASSimulator V1. Coverage extension is studied for different scenarios, starting from single RIMS and continuing with multiple RIMS sites scenarios. The best locations and scenarios for improving the theoretical coverage at the border area of the EGNOS service are discussed.

This paper demonstrates the images dimensionality reduction by using a new algorithm of Principal Component Analysis based on Hyperanalytic Wavelet Packets. A new interpretation of the concept of Principal Component in case of images is proposed, taking advantage of the concrete character of directionality in this case.