Interference from unknown devices makes time-of-arrival (ToA) estimation using conventional signal processing methods unreliable. In this paper, we propose new ToA estimation techniques based on Bayesian compressive sensing (BCS) to improve the accuracy of the ToA estimation under the interference scenario. Our proposed BCS based ToA estimation schemes maximize the posterior probability of the channel impulse response (CIR) with given frequency domain received signals. Simulation results show that proposed BCS based ToA estimations exhibit significantly improved ToA detection accuracy and mean-squared error performance in interference scenarios. We also demonstrate a practical example of the ToA estimation using real measured indoor channels.

We derive the posterior Cramér–Rao bound (PCRB) for tracking in anchorless dynamic networks consisting of static nodes at unknown locations and mobile nodes. We show that this is a type of PCRB under parametric constraints where the parameter space cannot be modeled by equation constraints. We propose a novel framework for modeling the constrained parameter manifold, as a factor manifold under the actions of a group. The approach we propose is generic, and can be used to derive lower error bounds for other parameter estimation problems where the parametric constraints cannot be optimally described by equation constraints.

We present a new method for anchorless localization of mobile nodes in wireless networks using only measured distances between pairs of nodes. The novelty of the algorithm lies in the method to complete the Euclidean distance matrix by inferring the unknown distances such as to align all nodes on a k-hyperplane, where typically k is 2 or 3. The relative locations of the nodes are then computed using multidimensional scaling. We also present experimental results with data collected using our WASP platform to verify the improvement in localization using our algorithm over previously published techniques.

We give a complete characterization for the range of the algebraic degree of 1-perfect binary codes. We show that the algebraic degree range is a compact interval, and that the Vasil'ev construction produces perfect with algebraic degrees covering the entire range. We also hint at a link between the algebraic degree of a perfect binary code and the coefficients of the code character.

In this communication, we give a complete characterization for the range of the algebraic degree of perfect binary codes. For 1 -perfect binary codes, we show that for anydin the algebraic degree range, there are Vasil'ev codes with algebraic degreed. It is also shown that the algebraic degree of 1-perfect binary codes is independent of the rank and the dimension of the kernel.

After a very promising start, progress in fractal image coding has been relatively slow recently. Most improvements have been concentrating on better adaptive coding algorithms and on search strategies to reduce the encoding time. Very little has been-done to challenge the linear model of the fractal transformations used so far in practical applications. In this paper, we explain why effective nonlinear transformations are not easy to find and propose a model based on conformal mappings in the geometric domain that are a natural extension of the affine model. Our compression results show improvements over the linear model and support the hope that a deeper understanding of the notion of self-similarity would further advance fractal image coding.