Department Publications

In this paper, we are concerned with indoor localization based on multiple-antenna channel measurements. Indoor localization is an active area of research due to its great importance in many applications. We propose a hybrid algorithm which combines the benefits of two techniques, namely signal processing and machine learning. We validate our algorithm based on real measurements acquired from two practical setups. Our approach shows a very promising performance in the IEEE CTW 2019 - Positioning Algorithm Competition where the algorithm achieves an accuracy within RMSE values below 10 cm. We further build a setup in another indoor environment, where the algorithm still proves a very good performance compared to state-of-the art techniques used in indoor localization tasks.

In this letter an intuitive measurement based approximation for packet switched network traffic in backbone links is presented. It consists of a sum of independent non-identical Bernoulli sources. The resulting sum has a Poisson binomial distribution which can be approximated to a skewed Gaussian distribution at the asymptote. It can be further approximated to a binomial distribution for predicting congestion by estimating the number of users of the network. The results are verified using samples of traffic data from 42 academic and research institutions.

We address the problem of identification of spherical objects, which are inside a layered structure by wireless sensing. Here, the backscatter response is used to identify the objects. It is a task, which is improved as the resolution gets higher. Therefore, to have a better resolution we consider the THz band. However, in the THz surface scattering expands the backscatter response of the layered structure in time. Thus, the backscatter response of the objects and the layered structure superimpose each other. This becomes even more severe due to the limited bandwidth at the receiver. In consequence, the exploitation of sophisticated signal separation methods for improved object identification is required. Therefore, we propose morphological component analysis (MCA), semi-blind MCA (MCA-SB) and singular value decomposition (SVD) methods. The MCA and MCA-SB rely on the sparse representation of signals …

In this paper, we introduce a novel algorithm that can dramatically reduce the number of antenna elements needed to accurately predict the direction of arrival (DOA) for multiple input multiple output (MIMO) radar. The new proposed algorithm predicts the received signal of a large antenna setup using reduced number of antenna by using coupled dictionary learning. Hence, this enables the MIMO radar to resolve more paths, which could not be resolved by the fewer antennas. Specifically, we overcome the problem of inaccurate DOA estimation due to a small virtual array setup. For example, we can use dictionary learning to predict 100 virtual array elements using only 25. To evaluate our algorithm, we used multiple signal classification (MUSIC) as a DOA estimation technique to estimate the DOA for non coherent multiple targets. The results show that using the predicted received signal, the proposed algorithm …