Department Publications

In coastal environments, detecting and classifying low radar cross-section (RCS) moving targets such as dinghies and trawlers using pulse radar system is challenging due to strong sea and land clutter. The clutter reduces detection accuracy. Further, the detection and classification heavily depend on radar operator expertise. To overcome these limitations, we propose an operator-independent framework for detection and classification. This paper presents a hybrid approach that combines Robust Principal Component Analysis (RPCA) with deep learning-based object detection models to improve target detection using radar Plan Position Indicator (PPI) images. RPCA is first applied as a low rank nature clutter separation from sparse moving targets. Detection is then performed using You Only Look Once (YOLO) and Detection Transformer (DETR) models on these clutter-suppressed representations. Experimental …

The Gaussian Mixture Model (GMM) based multi-stage Wiener filter (WF), recently introduced as a single-channel noise suppression method, demonstrates promising performance when applied to low signal-to-noise ratio (SNR) speech enhancement. This paper introduces a method for phonocardiogram (PCG) signal denoising using a two-stage subspace adaptive filtering method, with the aim of improving the noise suppression performance using GMM WF. Specifically, we utilize the normalized least mean squares (NLMS) and GMM WF to propose a hybrid adaptive filter with multi-channel spectral principal components analysis (PCA), which suppresses residual noise. We perform experimental evaluations on two heart sound datasets and show that the proposed denoising method exhibits increased performance over baseline approaches under various real-world noise conditions.

Vehicular ad-hoc networks (VANETs) face significant challenges in channel estimation, primarily due to the rapidly changing environment and the impact of Doppler frequency shifts caused by high-speed vehicle movement. This paper aims to evaluate the performance of existing channel estimation algorithms under scenarios with Doppler frequency shifts through computational experiments, with the assessment metric being the bit error rate (BER) performance of the algorithms under various signal-to-noise ratio (SNR) conditions. Based on simulation results, this paper systematically compares the effects of the least squares (LS), linear minimum mean square error (LMMSE), radial basis function (RBF), and genetic algorithm-optimized RBF (GA-RBF) in both relay-free and relayed scenarios, assessing their effectiveness in low-speed and high-speed environments. The results show that the LMMSE and GA-RBF …

In this paper, we consider the problem of recovering desired sound source signals from on-board microphone recordings on a noisy drone. Enhancement of source signal degraded by drone noise is considered to be a difficult task due to the strong noise generated from its motors and propellers causing an extremely low signal-to-drone noise ratio (S D ‾ NR). We propose a solution (i) by combining the widely known multichannel Wiener filter (MWF) to remove drone noise from microphone recordings, and (ii) further reduction of residual noise using a Gaussian mixture model (GMM) based dual-stage parametric Wiener filter (WF). The method exploits known statistics of motor current-specific drone noise. This combination of techniques to the specific context of signal enhancement for drone audition is applicable to irregular microphone arrays embedded on a drone enabling realistic integration to most drones. We …

The ability to accurately localize vehicles is essential for driver assistance applications. Existing modern localization systems, while sophisticated, often come with considerable costs and limitations in accessibility. Recognizing the need for a more accessible solution, there is a growing demand for user-friendly and cost-effective alternatives. One such alternative lies in leveraging mobile devices for localization. However, relying solely on Global Positioning System (GPS), Wi-Fi, or cellular networks, the most commonly used technologies in mobile device localization, often suffer from issues with accuracy. Addressing these drawbacks is crucial to ensuring its effectiveness and widespread adoption. Map matching is a commonly pursued and well-researched approach taken to rectify these erroneous location data. This paper presents the implementation of a hybrid of two map-matching implementations on a mobile …

Even though human experience unfolds continuously in time, it is not strictly linear; instead, it entails cascading processes building hierarchical cognitive structures. For instance, during speech perception, humans transform a continuously varying acoustic signal into phonemes, words, and meaning, and these levels all have distinct but interdependent temporal structures. Time-lagged regression using temporal response functions (TRFs ) has recently emerged as a promising tool for disentangling electrophysiological brain responses related to such complex models of perception. Here, we introduce the Eelbrain Python toolkit, which makes this kind of analysis easy and accessible. We demonstrate its use, using continuous speech as a sample paradigm, with a freely available EEG dataset of audiobook listening. A companion GitHub repository provides the complete source code for the analysis, from raw data to group-level statistics. More generally, we advocate a hypothesis-driven approach in which the experimenter specifies a hierarchy of time-continuous representations that are hypothesized to have contributed to brain responses, and uses those as predictor variables for the electrophysiological signal. This is analogous to a multiple regression problem, but with the addition of a time dimension. TRF analysis decomposes the brain signal into distinct responses associated with the different predictor variables by estimating a multivariate TRF (mTRF), quantifying the influence of each predictor on brain responses as a function of time(-lags). This allows asking two questions about the predictor variables: (1) Is there a significant neural …

In this paper, we address the problem of direction of arrival (DOA) estimation for multiple targets in the presence of sensor failures in a sparse array. Generally, sparse arrays are known with very high-resolution capabilities, where N physical sensors can resolve up to uncorrelated sources. However, among the many configurations introduced in the literature, the arrays that provide the largest hole-free co-array are the most susceptible to sensor failures. We propose here two machine learning (ML) methods to mitigate the effect of sensor failures and maintain the DOA estimation performance and resolution. The first method enhances the conventional spatial smoothing using deep neural network (DNN), while the second one is an end-to-end data-driven method. Numerical results show that both approaches can significantly improve the performance of MRA with two failed sensors. The data-driven method can …

Hearing-impaired listeners have a reduced ability to selectively attend to sounds of interest amid distracting sounds in everyday environments. This ability is not fully regained with modern hearing technology. A better understanding of the brain mechanisms underlying selective attention during speech processing may lead to brain-controlled hearing aids with improved detection and amplification of the attended speech. Prior work has shown that brain responses to speech, measured with magnetoencephalography (MEG) or electroencephalography (EEG), are modulated by selective attention. These responses can be predicted from the speech signal through linear filters called Temporal Response Functions (TRFs). Unfortunately, these sensor-level predictions are often noisy and do not provide much insight into specific brain source locations. Therefore, a novel method called Neuro-Current Response Functions …

In this paper, we consider the problem of recovering speech from audio recordings of on-board microphones in a noisy drone platform. Enhancement of speech degraded by drone noise is considered to be a difficult task due to the strong noise generated from its motors and propellers causing an extremely low signal-to-drone noise ratio (SdNR). We propose a solution by (i) developing a multichannel Wiener filter (MWF) to remove drone noise from microphone recordings, and (ii) further reduction of residual noise using a Gaussian mixture model (GMM) based dual-stage parametric Wiener filter (WF). The method exploits the known statistics of motor current-specific drone noise. The theory developed is applicable to irregular microphone array embedded in a drone enabling realistic integration to most drones. We evaluate the proposed framework using two different drone acoustics datasets under extreme SdNR levels including− 30 dB. The experimental results confirm promising performance in terms of SdNR improvement, speech quality (PESQ), and intelligibility (STOI) and show a strong potential for speech enhancement applications using noisy drones.

In this paper, we consider the problem of recovering desired sound source signals from on-board microphone recordings on a noisy drone.  Enhancement of source signal degraded by drone noise is considered to be a difficult task due to the strong noise generated from its motors and propellers causing an extremely low signal-to-drone noise ratio (SDNR).  We propose a solution (i) by using a multichannel Wiener filter (MWF) to remove drone noise from microphone recordings, and (ii) further reduction of residual noise using a Gaussian mixture model (GMM) based dual-stage parametric Wiener filter (WF).  The method exploits known statistics of motor current-specific drone noise.  The theory developed is applicable to irregular microphone arrays embedded on a drone enabling realistic integration to most drones. We demonstrate the validity of the proposed framework through (i) experimental recordings from two …

Objective The Temporal Response Function (TRF) is a linear model of neural activity time-locked to continuous stimuli, including continuous speech. TRFs based on speech envelopes typically have distinct components that have provided remarkable insights into the cortical processing of speech. However, current methods may lead to less than reliable estimates of single-subject TRF components. Here, we compare two established methods, in TRF component estimation, and also propose novel algorithms that utilize prior knowledge of these components, bypassing the full TRF estimation. Methods We compared two established algorithms, ridge and boosting, and two novel algorithms based on Subspace Pursuit (SP) and Expectation Maximization (EM), which directly estimate TRF components given plausible assumptions regarding component characteristics. Single-channel, multi-channel, and source-localized …

This paper proposes a single-channel speech enhancement method to reduce the noise and enhance speech at low signal-to-noise ratio (SNR) levels and non-stationary noise conditions. Specifically, we focus on modeling the noise using a Gaussian mixture model (GMM) based on a multi-stage process with a parametric Wiener filter. The proposed noise model estimates a more accurate noise power spectral density (PSD), and allows for better generalization under different noise conditions such as harmonic and babble noise environments compared to traditional Wiener filtering methods. Simulations show that the proposed approach can achieve better performance in terms of speech quality (PESQ) and intelligibility (STOI) at low SNR levels.

We address the detection of material defects, which are inside a layered material structure using compressive sensing-based multiple-input and multiple-output (MIMO) wireless radar. Here, strong clutter due to the reflection of the layered structure’s surface often makes the detection of the defects challenging. Thus, sophisticated signal separation methods are required for improved defect detection. In many scenarios, the number of defects that we are interested in is limited, and the signaling response of the layered structure can be modeled as a low-rank structure. Therefore, we propose joint rank and sparsity minimization for defect detection. In particular, we propose a non-convex approach based on the iteratively reweighted nuclear and ℓ1-norm (a double-reweighted approach) to obtain a higher accuracy compared to the conventional nuclear norm and ℓ1-norm minimization. To this end, an iterative algorithm is designed to estimate the low-rank and sparse contributions. Further, we propose deep learning-based parameter tuning of the algorithm (i.e., algorithm unfolding) to improve the accuracy and the speed of convergence of the algorithm. Our numerical results show that the proposed approach outperforms the conventional approaches in terms of mean squared errors of the recovered low-rank and sparse components and the speed of convergence.

We address the compensation of distortions in terahertz time-domain spectroscopy (THz-TDS) systems. In this context, the main sources of possible distortions are misalignment of the hardware and the sample during the scan, vibration of the measurement system and time delay variations due to the unevenness of the material surface. This heavily affects the achievable accuracy of material characterization by the THz-TDS system. Therefore, it is important to develop approaches to mitigate these effects. In this work, we profit from the low-rankness of the distortion profile. To this end, we formulate an iterative algorithm based on rank minimization to estimate the material parameters in the presence of the aforementioned impairments. In contrast to the standard convex relaxation of the rank, we propose an iteratively reweighted nuclear norm for improved estimation performance. Further, we compare our approach with …

Wireless signals-based non-destructive material characterization and object identification is a vital research area that has many potential day-to-day and industrial applications. Here, one of the main challenges is suppression of the unwanted reflections coming from the surroundings that are not in our interest, ie, clutter. The clutter echoes are much stronger than the reflection that comes from the desired object. Also, in many scenarios, the clutter source and the desired objects are in close proximity. Therefore, clutter may mask the reflections coming from the desired objects. This dissertation intensively discusses clutter suppression for the identification of objects/defects. In this regard, we utilize advances in supervised learning and nonconvex optimization for clutter suppression. In this context, we profit from the low-rankness and the sparseness of the responses of the clutter and the objects. Instead of the convex relaxation of rank and sparsity commonly applied in state-ofthe-art, we propose a non-convex iteratively reweighted nuclear and ℓ1− norm-based iterative algorithm to obtain higher accuracy. Moreover, in contrast to the standard parameter tuning of the iterative algorithm, we propose supervised deep learning to tune the parameters, which is referred to as algorithm unrolling. Our numerical results show that deep learning-based parameter tuning significantly improves the accuracies of the recovered low-rank and sparse components and the speed of the convergence of the iterative algorithm. Additionally, we investigated the robustness of our approach by introducing artificial errors into the measurement matrices. Our findings show that …

The Kalman filter is often used with plants without zeros. However, when the plant has zeros, the Kalman filter tends to neglect the effects of zeros, leading to errors in output estimation. Without the effects of zeros, the Kalman filter estimation would lag behind the true output, or it would lead to true output if the zeros are on the RHP. The parallel Kalman filter bank, instead of a single filter is proposed to remedy this problem. Each Kalman filter in the bank operates on a plant submodel that is obtained by decomposing the plant model. The output estimations of all of the Kalman filters in the bank are added together to synthesize the plant output estimation. The method was simulated in a PID-controlled system where better results have been obtained.

The quadrotor is an under-actuated nonlinear system that poses challenges in modeling and controller design. This work uses the translational (Newtonian) and rotational (Eulerian) dynamics of the quadrotor to build a decoupled control system. A linear quadratic Gaussian (LQG) regulator is used as the controller, and two extended Kalman filters are used for state estimation in the two dynamic models. The entire design process from modeling to flight simulation is presented. Model linearization while preserving controllability, state-space modeling under gravity disturbance, feedforward compensation, LQR gain adjustment, actuator dynamics, sensor system for observability, adaptation of extended Kalman filter, and its limitations are presented.

We present a MUSIC-based Direction of Arrival (DOA) estimation strategy using small antenna arrays, via employing deep learning for reconstructing the signals of a virtual large antenna array. Not only does the proposed strategy deliver significantly better performance than simply plugging the incoming signals into MUSIC, but surprisingly, the performance is also better than directly using an actual large antenna array with MUSIC for high angle ranges and low test SNR values. We further analyze the best choice for the training SNR as a function of the test SNR, and observe dramatic changes in the behavior of this function for different angle ranges.

The acquisition of channel state information is an essential step in enabling intelligent reflecting surfaces (IRSs) for wireless communications. In this letter, we introduce a novel procedure to estimate the cascaded channel between the base station (BS), IRS, and users in a multi-user multiple-input single-output system. The common BS-IRS channel, over which all users transmit, is leveraged to decompose the cascaded channel into a series of rank-1 matrices. Low-rank matrix recovery methods are utilized to improve upon the linear minimum mean-squared error estimate of the cascaded channel. A theoretical upper bound for the mean-square error (MSE) of the proposed estimator is derived. Numerical results reveal that the proposed techniques outperform the existing counterparts in terms of the MSE and scale with the number of BS antennas.

Self-localization based on passive RFID-based has many potential applications. One of the main challenges it faces is the suppression of the reflected signals from unwanted objects (i.e., clutter). Typically, the clutter echoes are much stronger than the backscattered signals of the passive tag landmarks used in such scenarios. Therefore, successful tag detection can be very challenging. We consider two types of tags, namely low-Q and high-Q tags. The high-Q tag features a sparse frequency response, whereas the low-Q tag presents a broad frequency response. Further, the clutter usually showcases a short-lived response. In this work, we propose an iterative algorithm based on a low-rank plus sparse recovery approach (RPCA) to mitigate clutter and retrieve the landmark response. In addition to that, we compare the proposed approach with the well-known time-gating technique. It turns out that RPCA outperforms significantly time-gating for low-Q tags, achieving clutter suppression and tag identification when clutter encroaches on the time-gating window span, whereas it also increases the backscattered power at resonance by approximately 12 dB at 80 cm for high-Q tags. Altogether, RPCA seems a promising approach to improve the identification of passive indoor self-localization tag landmarks.

Defect detection plays an important role in product quality assurance in many industrial applications. Air-coupled ultrasound defect detection setups have the advantage that they are non-destructive and do not contaminate the investigated materials. However, this approach comes with some challenges due to the weak signaling response of the defects. Thus, sophisticated signal separation methods are required. To address this challenge, low-rank-plus-sparse recovery (LRPSR) is proposed and compared with state-of-the-art machine learning (ML) methods. The results obtained show that the LRPSR method achieves comparable results in terms of detection rate to those achieved by ML. Yet, for a small training data set, the LRPSR approach outperforms the ML algorithms. The small training data set is important for detecting defects of different production lines without having a time-consuming training process. In …

We consider the problem of determining Granger causal influences among sources that are indirectly observed through low-dimensional and noisy linear projections. Commonly used methods proceed in a two-stage fashion, by first solving an inverse problem to localize the sources, and then inferring the Granger causal influences from the estimated sources. The inferred Granger causal links thus inherit the various biases that are used in source localization techniques, in the form of spatiotemporal priors designed in favor of spatial localization. In addition, this approach does not account for the structural properties of the underlying functional networks such as sparsity of the links. We address these issues by modeling the source dynamics as sparse vector autoregressive processes and estimate the model parameters directly from the observations, with no recourse to intermediate source localization. We evaluate …

Estimating parameters and properties of various materials without causing damage to the material under test (MUT) is important in many applications. Thus, in this letter, we address MUT's parameter estimation by wireless sensing. Here, the precision of the estimation depends on the accurate estimation of the properties of the reflected signal from the MUT (e.g., number of reflections, their amplitudes, and time delays). For a layered MUT, there are multiple reflections, and due to the limited bandwidth at the receiver, these reflections superimpose with each other. Since the number of reflections coming from the MUT is limited, we utilize sparse signal processing (SSP) to decompose the reflected signal. In SSP, a so called dictionary is required to obtain a sparse representation of the signal. Here, instead of a fixed dictionary, an iterative dictionary-update technique is proposed to improve the estimation of the reflected …

In this paper, we propose a novel algorithm based on supervised learning for antenna array extrapolation for the purpose of super resolution DoA estimation. We use multiple signal classification (MUSIC) as a DoA estimation technique to estimate the DoA. In order to reduce the computational burden, existing approaches focus on interpolating the missing elements in a virtual array using a sparse array (or non-uniform linear arrays) or employ antenna selection within the same aperture. In contrast, here we utilize an uniform linear array (ULA) using a low number of antennas (within a small array aperture) to extrapolate another ULA with a higher number of antennas (within a bigger aperture). This approach is not restricted to any specific antenna array configuration, in fact it can be generalized to any array configuration. We propose an algorithm which utilizes the advances of supervised learning (dictionary learning …