Department Publications

The radio spectrum is a scarce and extremely valuable resource that demands careful real-time monitoring and dynamic resource allocation. Dynamic spectrum access (DSA) is a new paradigm for managing the radio spectrum, which requires AI/ML-driven algorithms for optimum performance under rapidly changing channel conditions and possible cyber-attacks in the electromagnetic domain. Fast sensing across multiple directions using array processors, with subsequent AI/ML-based algorithms for the sensing and perception of waveforms that are measured from the environment is critical for providing decision support in DSA. As part of directional and wideband spectrum perception, the ability to finely channelize wideband inputs using efficient Fourier analysis is much needed. However, a fine-grain fast Fourier transform (FFT) across a large number of directions is computationally intensive and leads to a high chip area and power consumption. We address this issue by exploiting the recently proposed approximate discrete Fourier transform (ADFT), which has its own sparse factorization for real-time implementation at a low complexity and power consumption. The ADFT is used to create a wideband multibeam RF digital beamformer and temporal spectrum-based attention unit that monitors 32 discrete directions across 32 sub-bands in real-time using a multiplierless algorithm with low computational complexity. The output of this spectral attention unit is applied as a decision variable to an intelligent receiver that adapts its center frequency and frequency resolution via FFT channelizers that are custom-built for real-time monitoring at high …

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 …

Estimating the Relative Transfer Functions (ReTFs) of multiple speakers in a noisy reverberant environment are beneficial to signal processing applications of acoustic scene analysis, denoising, dereverberation, signal enhancement, and separation. Whilst most audio applications exploit the covariance matrix structure of multichannel recordings, estimating ReTF in the presence of simultaneously active multiple speakers is still considered to be a challenging problem. In this paper, we propose a novel method for estimating the ReTF using the relative transfer matrix (ReTM) of multiple microphones for multi-talker scenarios, which is suitable for noisy reverberant rooms. The method is based on the noise-only ReTM, and calculates the covariance matrices of (i) the first speaker and noise, and (ii) both speakers and noise at two microphone groups to reconstruct the ReTF of the second speaker. We demonstrate the …

The radio spectrum is a scarce and extremely valuable resource that demands careful real-time monitoring and dynamic resource allocation. Dynamic spectrum access (DSA) is a new paradigm for managing the radio spectrum, which requires AI/ML driven algorithms for optimum performance under rapidly changing channel conditions and possible cyber attacks in the electromagnetic domain. Fast sensing across multiple directions using array processors, with subsequent AI/ML based algorithms for sensing and perception of the waveforms that are measured from the environment is critical for providing decision support in DSA. As part of directional and wideband spectrum perception, the ability to finely channelize wideband inputs using efficient Fourier analysis is much needed. However, a fine-grain fast Fourier transform (FFT) across a large number of directions is computationally intensive and leads to high chip area and power consumption. We address this issue by exploiting recently proposed approximate discrete Fourier transforms (ADFT) which has its own sparse factorization for real-time implementation at low complexity and power consumption. The ADFT is used to create a wideband multi-beam RF digital beamformer and temporal spectrum-based attention unit which monitors 32 discrete directions across 32 sub-bands in real-time using a multiplierless algorithm having low computational complexity. The output of this spectral attention unit is applied as a decision variable to an intelligent receiver that adapts its center frequency and frequency resolution via FFT channelizers that are custom-built for real-time monitoring at high …

A low-complexity multibeam wideband transmit beamformer using a 2D sparse FIR filter design capable of multiple beams is proposed as a digital building block for fully digital beamformers. The 2D sparse FIR filter has multiple trapezoid-shaped passbands pertaining to wideband beams aimed at particular directions. The proposed multibeam digital beamformer drives a uniform linear array of wideband antenna elements to achieve the wideband multibeam transmit-mode signals desired by the communication system. The 2D sparse FIR filter is designed to be optimal in the minimax sense using convex optimization techniques. Full-wave electromagnetic simulations using real antenna models confirm that the proposed wideband transmit beamformer can achieve multibeam transmission in the 1.3–2.8 GHz frequency range, with more than 70% fractional bandwidth. Furthermore, the adoption of the wideband transmit multibeam beamformer leads to a significant reduction in digital arithmetic (computational) complexity compared with previously reported wideband transmit beamformers of similar transfer function type, without deteriorating beam directionality and causing increases in the side-lobe level. The proposed sparse 2D FIR multibeam beamformer architecture is well-suited for both sub-6 GHz (legacy) band transmit beamforming, frequency range three (FR3) beamforming up to 28 GHz, and mmWave operation for emerging 5G/6G applications.

Frequency-modulated continuous-wave (FMCW) multiple-input multiple-output (MIMO) long-range radars currently employed for autonomous robotics have limited maximum range. By employing transmit beamforming and beam scanning, the range can be increased. However, the beam scanning time reduces the achievable velocity resolution. In this paper, we propose an FMCW MIMO radar, operating at 77 GHz, with transmit (TX) beamforming with subarrays to increase the range. We employ four TX subarrays, each having three antennas, with analog beamforming. Compared to TX beamforming as one TX array, our approach provides sufficiently wide beams alleviating beam scanning, further, without substantially reducing the virtual elements of the MIMO radar. We implement the radar signal processing pipeline on a ZYNQ Ultrascale+ ZCU106 FPGA to achieve real-time processing. Furthermore, we employ a …

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 real-time adaptive nulling of a digital beamformer is crucial for receiving weak signals in a high RFI or RF denied environment. The Howells-Applebaum and MVDR adaptive beamformers use the Weiner optimum solution to ensure that RFI sources are blanked out in an array processor. In this paper, we create a custom 32-channel fully-digital array operating at 5.7-5.8 GHz, with real-time FPGA processing for both adaptation and subsequent beamforming with RFI suppression. Real-time measurements with up to three RFI sources show correct real-time digital beamforming in the FPGA processor. The developed microwave and digital systems along with hardware accelerated Howells-Applebaum array-null adaptation architecture will be used as a building block to create RF identification, location, spectrum sensing, and wireless communications that are resilient to jammers and RFI sources in the environment.

Multichannel Wiener filter (MWF), commonly used as a spatial filter for noise reduction, exhibits promising performance due to its robustness in multi-channel setups. Additionally, the preservation of spatial cues at the MWF is usually a favorable property that contributes to sound source localization. Previous works have shown that the MWF can be used in drone noise suppression for acoustic sensing tasks in drone audition. In this paper, we analyze the preservation of spatial cues at the MWF from on-board microphone recordings on a drone at very low signal-to-drone noise ratio (SDNR) levels. The analysis builds upon the relative transfer function (ReTF), and explores the spatial information of the desired sound sources such as human speakers and birds through experimental recordings from simulated and real-life environments. We also demonstrate the effectiveness of the preservation of spatial cues at the MWF in an acoustic localization application

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.