Department Publications

A spatially-interpolated (SI) beamformer is proposed in this paper to enhance narrowband plane waves received by a sparse linear antenna array. Similar to a conventional SI filter, the proposed beamformer employs two filters: an SI prototype filter and a masking filter. However, the proposed SI beamformer employs a parallel structure compared to a cascade structure of conventional SI filters leading to a sparse antenna array. Both the prototype filter and the masking filter are designed as optimal filters in the minimax sense. Furthermore, we employ a bell-shaped passband for the ideal magnitude response for the design compared to a conventional ideal frequency response that employs 1 and 0 as the passband and the stopband gains with a finite transition band. Simulation results confirm that the proposed SI beamformer provides approximately 37% reduction in computational complexity and a similar reduction …

This thesis explores the acoustic signal processing on drones which can enhance the drone audition functions for search and rescue missions, wildlife monitoring, and video recording for media and filming industries. With the recent emergence of drone-based applications and services, microphone arrays are beginning to be mounted to drones. However, audio capturing using drone-mounted microphone arrays leads to very low signal-to-drone noise ratio (SDNR) recordings (below negative 10 dB) due to the drone-generated motor and propeller noise signals. Hence, this causes a highly adverse noisy environment and degrades the quality and intelligibility of the recorded audio signals. Therefore, there is a growing interest in deriving new signal processing algorithms for the future deployment of the drone audition. To this end, we develop novel methods for spatial soundfield modelling, and spatial audio processing using a microphone array embedded into the body of drones for sound source localisation and audio signal enhancement. To a lesser extent, drone noise characterisation is also studied. We propose modelling of the drone noise harmonics as current to noise transfer function by taking the significant harmonics of the drone noise signature. The model is derived from the experimental measurements of a single motor propeller combination, and validated using extensive error analysis. A cross-correlation-based DOA estimation method with drone noise angular spectrum subtraction is derived for acoustic source localisation. Our approach is to exploit the underlying spatial characteristics of drone noise to mitigate the noise emission by …

With the recent emergence of virtual reality, augmented reality, and mixed-reality technologies, considerable attention has been received to integrate spatial audio recording capabilities into these devices. Extracting higher order spherical harmonics from the recordings is not always possible due to complex scattering effects from the arbitrary-shaped devices, which motivates the need for modeling the acoustic properties beyond free-field convention. In this paper, we propose modeling the wearable device-related transfer function (WDRTF) that incorporates scattering, and diffraction from the device and wearer's body to an incident soundfield. We adopt the spherical wave model to translate the measurements into microphone alignment, by exploiting both magnitude and phase corrections. We use a discrete set of measurements by multiple circular arrays for efficiently estimating the WDRTF coefficients. We illustrate the WDRTF modeling procedure from a head-worn microphone array and obtain accurate reconstruction performance over frequency and 3D directions.

The Relative Transfer Matrix (ReTM), recently introduced as a generalization of the relative transfer function for multiple sources and multiple microphones, shows promising performance when applied to speech denoising and speaker separation in a noisy reverberant room. This work utilizes the ReTM to propose a novel framework for drone noise suppression. Difficulties in noise cancellation for drone audition applications arise due to its self-generated noise that causes an adverse noisy environment of low Signal-to-Drone Noise Ratio (SDNR) levels. In this paper, we divide the drone on-board microphones into two multichannel groups to approximately estimate the drone noise from one group to the other with known drone noise ReTM for denoising. We demonstrate the ReTM spatial mapping ability in both indoor and outdoor experiments using hovering drone on-board microphone recordings with low average magnitude spectrum error. Finally, we validate the method in a real-life environment for source signal enhancement over different SDNR conditions and offer both improved speech intelligibility and signal-to-distortion ratio.

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 …

A low-complexity multibeamforming scheme for uniform linear arrays is proposed based on warped and approximated discrete Fourier transform (DFT), albeit in the spatial domain. Conventional DFT- and approximated-DFT-based multibeamformers provide a low-complexity alternatives to produce multiple simultaneous beams with fixed beam profiles. The proposed approach aims to produce configurable (i.e., steerable) multibeam profiles using the warped DFT. Design examples are presented for an 8-element uniform linear array verifying, 81% and 18 % reduction in multiplier and adder complexities, respectively, when compared to a conventional narrowband multibeamformer having dedicated steering weights for each beam. Three system configurations are proposed using approximations for warped transform and the DFT. Hardware designs implemented on Xilinx Ultrascale KCU105 FPGA device verify 5.2 …

Broadband beamformers designed as two-dimensional (2-D) spatially-interpolated finite-extent impulse responses (FIR) filters, using a cascade structure, cannot employ sparse linear arrays despite significantly reducing the computational complexity. This happens due to the fact that the 2-D masking filter requires spatial samples equal to the spatial order at the input. Therefore, the output of the 2-D spatially-interpolated prototype filter should be computed for more than one spatial index. In order to address this limitation, we propose a 2-D spatially-interpolated FIR filter using a parallel structure. With the proposed structure, both 2-D spatially-interpolated prototype filter and the 2-D masking filter need to compute the output only for one spatial index, therefore allowing to employ sparse linear arrays. In order to support broadband beamforming, we design the 2-D FIR filter to have a parallelogram passband …

The use of approximate transforms in conjunction with approximate adder hardware is explored towards implementing low-complexity multi-beam antenna arrays. The use of approximate adders is motivated by the efficient look up table (LUT) usage on field programmable gate arrays (FPGA) resulting in better area and time complexities on hardware. 8-beam and 16-beam hardware architectures for multi-beamforming are designed using previously reported 8-point and 16-point spatial approximate-DFT (ADFT) algorithms, respectively, albeit with approximate adders. Designs have been implemented on Xilinx Kintex UltraScale KCU105 FPGA device verifying 31.5% reduction in (FPGA) LUT count and 58.08% improvement in critical path delay with a maximum beam error of 0.6 degrees and maximum peak side lobe level deviation 0.21 dB for the 8-beam case, when compared to ADFT with accurate adders with the …

This paper presents a sound source localization method using an irregular microphone array embedded in a drone. Sound source localization is an integral function of drone audition systems which enables various applications of drones such as search and rescue missions. However, the audio recordings using the on-board microphones obscure the sound emitted by a source on the ground due to drone generated motor and propeller noise, thus leading to an extremely low signal-to-drone noise ratio (SdNR). In this paper, we propose a cross-correlation based direction of arrival (DOA) estimation technique using the time difference of arrival (TDOA) at different microphone pairs, with noise angular spectrum subtraction. Through the measured current-specific drone noise spectrum, noise suppression has been achieved from the multi-channel recordings. Experimental results show that the proposed method is capable …

Transformers combined with convolutional encoders have been recently used for hand gesture recognition (HGR) using micro-Doppler signatures. In this letter, we propose a vision-transformer-based architecture for HGR with multiantenna continuous-wave Doppler radar receivers. The proposed architecture consists of three modules: 1) a convolutional encoder–decoder, 2) an attention module with three transformer layers, and 3) a multilayer perceptron. The novel convolutional decoder helps to feed patches with larger sizes to the attention module for improved feature extraction. Experimental results obtained with a dataset corresponding to a two-antenna continuous-wave Doppler radar receiver operating at 24 GHz (published by Skaria et al.) confirm that the proposed architecture achieves an accuracy of 98.3% which substantially surpasses the state-of-the-art on the used dataset.

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.

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 …

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.

This paper describes a technique for de-embedding electromagnetic mutual coupling effects between nearest neighbors of a ULA receiver. Measured S-parameters across a range of frequencies of interest are used in a closed-form mathematical model that relates measured S-parameters, measured LNA reflection coefficients, and transmission line parameters to digital beamformer design equations, such that the beam shape distortions arising from mutual coupling can be compensated in the DSP algorithm. A 5.8 GHz 32-element receiver array with custom receivers and 32-channel Xilinx Virtex-6 Sx35 FPGA based DSP system implementing a real-time complex-valued Frost beamformer operating at IF is proposed to show the benefits of de-embedding mutual coupling for far-field side-lobe suppression. The worst-case side-lobe level is reduced by 11.2 dB, and the average side-lobe level is reduced by 5.2 dB, for …

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 …

A multi-port spatio-temporal noise-shaping ADC is proposed to process plane waves received by spatially-oversampled antenna arrays. In the proposed multi-port ADC, the desired plane waves are processed with a spatial low-pass frequency response whereas the noise and distortion are shaped with a spatial high-pass frequency response. By employing a first-order Butterworth filter, approximately circular passbands and stopbands are achieved for the signal and the noise transfer functions, respectively. The proposed noise-shaping system is designed in the TSMC 180 nm CMOS process, with ADCs and DACs modeled as noise sources. Circuit simulation results show that the proposed system can achieve a bandwidth of 50 MHz.

We present a compressive sensing based defect detection by multiple input multiple output (MIMO) wireless radar. Here, defects are inside a layered material structure, therefore, due to reflections from the surface of the layered material structure the defect detection is challenging. By utilizing a low-rank nature of the reflections of the layered material structure and sparse nature of the defects, we propose a method based on rank minimization and sparse recovery. To improve the accuracy in the recovery of low-rank and sparse components, we propose a non-convex approach based on the iteratively reweighted nuclear norm and iteratively reweighted norm algorithm. Our numerical results show that the proposed method is able to demix and recover the signalling responses of the defects and layered structure successfully from substantially reduced number of observations. Further, the proposed approach outperforms the state-of-the-art clutter reduction approaches

This paper presents the design, implementation, and the experimental verifications of a 2.4-GHz multibeam array receiver based on two-dimensional (2-D) spatially bandpass (SBP) digital plane wave (PW) filters. The digital array receiver consists of 2.4-GHz 16-element uniform linear array, and quadrature sampling is employed to process the broadband PWs at the baseband. Both 2-D finite impulse response (FIR) and infinite impulse response (IIR) digital filters having multiple trapezoidal-shaped passbands are employed as the 2-D SBP digital PW filters. Both 2-D FIR and IIR trapezoidal filters are implemented on a Xilinx Virtex 6 sx475t field programmable gate array chip that comes with the reconfigurable open architecture computing hardware version-2 (ROACH-2) platform. The 16 in-phase and quadrature-phase downconverted signals are sampled using the ROACH-2 platform and are processed using the …

A real-time directional sensing system is proposed for 2.4 GHz ISM band by exploiting the concept of spatiotemporal spectral white spaces. The proposed system consists of a 16-element patch antenna array, an FFT-based multi-beam beamformer and an energy detector. Our system operates at the baseband with quadrature sampling. Furthermore, digital architectures for two energy detectors that employ integrate-and-dump circuits are presented. With the multi-beam beamformer, the first energy detector can be employed to directional sensing and the second can be employed for both directional and spectral sensing of radio frequency sources. The multi-beam beamformer having 16 beams and the energy detectors are implemented on a ROACH-2 based FPGA system with a 160 MHz clock. With an 8-point temporal FFT, the second energy detector provides approximately 20 MHz bandwidth per temporal FFTbin …

A low-complexity wideband transmit beamformer is proposed using a digitally-fed uniform linear array of broadband Vivaldi antennas operating in the S-band. The proposed transmit beamformer employs a novel DSP feeding network based on a transmit-mode 2-D sparse finite-extent impulse response (FIR) filter having a planar passband in the 2-D frequency-wavenumber space. Electronic beam steering is achieved by changing the filter coefficients defined in closed-form and hard-thresholding (HT) is employed to obtain a sparse 2-D impulse responses of the filter. Full-wave electromagnetic simulations are used to obtain the far-field beam patterns produced by the 2-D sparse FIR filter in the frequency range 2-2.8 GHz for wideband signals with 33% fractional bandwidth. Computational complexity, beam directionality and side-lobe performance are investigated with varying HT along with quantitative comparisons …

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 …

A 3-D finite-extent impulse response (FIR) frustum filter having sparse coefficients is proposed for enhancing broadband plane waves (PWs) received by uniform aperture arrays. Motivated by the design technique of 1-D interpolated FIR filters, the proposed filter is realized by cascading a spatially-upsampled 3-D FIR prototype filter having a frustum-shaped passband and a 3-D FIR masking filter having a parallelepiped passband. Sparsity of the filter coefficients is achieved through the spatial upsampling of the coefficients of the prototype filter and by hard-thresholding the coefficients of both prototype and masking filters. The proposed 3-D FIR frustum filter saves 39.5% multiplications and 66.6% additions compared to the state-of-the-art 3-D FIR frustum filters without degrading the performance in enhancing broadband PWs.

This paper presents the implementation of a two-dimensional (2-D) finite-impulse-response (FIR) trapezoidal filter based beamforming array receiver. A 2.4 GHz 16-element receive-mode IQ array is designed and used for physically measuring the beam patterns corresponding to 2-D FIR trapezoidal filters. The 16-element beamformer is implemented using the reconfigurable open architecture computing hardware version-2 (ROACH-2) field programmable gate array (FPGA) platform to perform analog to digital conversion and digital signal processing. The 2-D FIR trapezoidal filter is designed as a 16-spatial input 32-tap filter and is implemented targeting the ROACH-2’s Xilinx Virtex 6 (sx475t) FPGA chip as a filter-and-sum architecture. The receiver array is precisely rotated (keeping the transmitter fixed) to digitally measure the received energy by integrating the instantaneous power of the output of the 2-D FIR …