Graph-based learning and estimation are fundamental problems in various applications involving power, social, and brain networks, to name a few. While learning pair-wise interactions in network data is a well-studied problem, discovering higher-order interactions among subsets of nodes is still not yet fully explored. To this end, encompassing and leveraging (non)linear structural equation models as well as vector autoregressions, this paper proposes autoregressive graph Volterra models (AGVMs) that can capture not only the connectivity between nodes but also higher-order interactions presented in the networked data. The proposed overarching model inherits the identifiability and expressibility of the Volterra series. Furthermore, two tailored algorithms based on the proposed AGVM are put forth for topology identification and link prediction in distribution grids and social networks, respectively. Real-data experiments on different real-world collaboration networks highlight the impact of higher-order interactions in our approach, yielding discernible differences relative to existing methods.@en

Sensor selection is a useful method to help reduce data throughput, as well as computational, power, and hardware requirements, while still maintaining acceptable performance. Although minimizing the Cramér-Rao bound has been adopted previously for sparse sensing, it did not consider multiple targets and unknown source models. In this work, we propose to tackle the sensor selection problem for angle of arrival estimation using the worst-case Cramér-Rao bound of two uncorrelated sources. To do so, we cast the problem as a convex semi-definite program and retrieve the binary selection by randomized rounding. Through numerical examples related to a linear array, we illustrate the proposed method and show that it leads to the natural selection of elements at the edges plus the center of the linear array. This contrasts with the typical solutions obtained from minimizing the single-target Cramér-Rao bound.@en

Integrated sidelobe level is a useful measure to quantify robustness of a waveform-filter pair to unknown range clutter and multiple closely located targets. Sidelobe suppression on receive will incur a loss in the signal to noise ratio after pulse compression. We derive a pulse compression filter that has the greatest integrated sidelobe suppression possible for a given acceptable signal to noise ratio loss. The solution is given in a closed form, which can be adjusted using a single parameter to chose between greater sidelobe or interference and noise suppression. We verify the derived filter using simulations, comparing it to other proposed mismatched filter designs. To expand the robustness of the filter, we additionally investigate noise uncertainty robustness. We derive two robustness measures for noise uncertainty and analyze the performance through simulation.@en

In recent years, convolutional neural networks (CNNs) have been increasingly used for classifying radar micro-Doppler signatures of various targets. However, obtaining large amounts of data for efficient CNN training in defence and surveillance scenarios can be challenging. Therefore, designing techniques that maximize the use of available samples is critical. In this paper, we propose an approach built on the hypothesis that certain classes of radar spectrograms, such as those used for discerning armed from unarmed walking personnel, do not have information about the class encoded in the trajectory. Therefore, our method entails segmenting each input spectrogram into individual frames that correspond to a distinct step of human locomotion. Subsequently, we classify each segment independently and combine the resulting classification scores to obtain the final score for the entire spectrogram. As a result of this segmentation, the size of the training set is increased, whereas the dimensions of each sample - and therefore the number of parameters in the classifier - is decreased, reducing the risk of overfitting. Our experimental results demonstrate the effectiveness of our approach and its potential to enhance CNN-based classification of micro-Doppler signatures.

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In this work, we focus on partitioning dynamic graphs with two types of nodes (bi-colored), though not necessarily bipartite graphs. They commonly appear in communication network applications, e.g., one color being base stations, the other users, and the dynamic process being the varying connection status between base stations and moving users. We introduce a partition cost function that incorporates the coloring of the graph and propose solutions based on the generalized eigenvalue problem (GEVP) for the static two-way partition problem. The static multi-way partition problem is then handled by a heuristic based on the two-way partition problem. Regarding the adaptive partition, an eigenvector update-based method is proposed. Numerical experiments demonstrate the performance of the devised approaches.

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