Department Publications

Unmanned aerial vehicles are increasingly deployed across diverse domains including security surveillance, precision agriculture, infrastructure inspection, and logistics. However, their rapid proliferation raises critical concerns regarding unauthorized intrusions, smuggling, and adversarial exploitation. Robust drone detection and classification systems are therefore essential. In this paper, we propose a multi-modal framework that fuses frequency-modulated continuous-wave radar and acoustic sensing using a transformer encoder-based architecture. Unlike single-modality approaches, our method exploits cross-modal complementarity to improve robustness against environmental noise, false alarms, and stealthy drone operations. Notably, our approach directly processes raw acoustic waveforms without domain transformations such as spectrograms, reducing preprocessing overhead and improving real-time …

Separation of simultaneously active multiple speakers is a difficult task in environments with strong reverberation and many background noise sources. This paper uses the relative transfer matrix (ReTM), a generalization of the relative transfer function of a room, to propose a simple yet effective approach for separating concurrent speakers using noisy multichannel microphone recordings. The method is built upon the covariance subtraction to enable practical applicability to mixtures of independent sound sources. The proposed method (i) allows multiple speech and background noise sources, (ii) includes reverberation, (iii) does not need the knowledge of the locations of speech and noise sources nor microphone locations and their relative geometry, and (iv) uses a relatively small segment of recordings for training. We illustrate the speaker separation performance through a simulation study, particularly under …

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 …

Sensing and early detection of small unmanned aerial systems (sUAS) are critically important in modern-day defense. In dense urban and indoor environments, detection becomes extremely challenging due to dense multipath, fading, low-altitude flight, and non-line-of-sight (NLOS) radio-frequency propagation. This paper presents a continuous-wave multiple-input multiple-output radar and a deep learning model for sUAS detection using NLOS signals. The radar operates at 2.47 GHz, and spectral correlation densities derived from rotational micro-Doppler signatures from the rotor blades are used as inputs to the deep learning model. Experimental results demonstrate an overall detection accuracy of across a dataset of five drone types, confirming the feasibility of sUAS detection in dense urban environments without direct line-of-sight conditions.

This paper presents the design and implementation of a multi-band multi-mode radar for reliable detection and identification of micro unmanned aerial systems (UASs). A rotational micro-Doppler continuous-wave radar operating at 5.85 GHz and a frequency-modulated continuous-wave (FMCW) radar operating with 77–80 GHz bandwidth are used for multi-modal radio frequency (RF) sensing, localization and detection. Fusion of the two radars is achieved with a neurosymbolic artificial intelligent model. Cyclostationary feature extraction is used for the micro-Doppler signature extraction. Wavelet domain feature extraction is used on the FMCW radar outputs. A custom neural engine is used for multi-modal sensor fusion across the two RF bands and radar types.

Low-complexity digital multi-beamforming via multiplier-less approximations for discrete spatial Fourier transform (DSFT) is explored. The proposed approach employs an approximate-DSFT (ADSFT) which - for a uniform linear array with N antennas producing N simultaneous beams, reduces the hardware multiplier requirement from 3N 2 to 0 with marginal error in the beam profiles. Importantly, the proposed approach extends the previously reported approximate discrete Fourier transform (ADFT) multi-beamforming systems having fixed beam profiles to enable configurable beam profiles, where the user can obtain N simultaneous beams within a given spatial angular region. Simulations demonstrate that the multi-beam profiles are largely accurate, and approximate selection results in deviations for 8- and 16-element ULAs, with worst-case beam angle errors of 0.3° and 0.2°, and worst-case sidelobe deviations of …

The 6-24 GHz upper midband - FR3 range - is the next generation spectrum for mobile wireless applications. Traditionally allocated for defense and scientific use cases, the possible adoption of FR3 frequencies for commercial applications opens the possibility for severe interference between different commercial and scientific/defense systems. We demonstrate lowcomplexity adaptive array processing in the FR3 range using realtime implementation in the 10.7-10.8 GHz band, with 100 MHz of bandwidth using Xilinx Virtex-6 Sx475T FPGA technology. The demonstration supports a uniform linear array with 8 Vivaldi antennas (6-24 GHz) and can provide up to 6 nulls in the aperture pattern in addition to the desired RF beam. The FPGA back-end utilizes sampling at 200 MS/s and exploits digital signal processing to furnish up to 100 MHz of bandwidth at IF.

This paper characterizes the communication–sensing rate tradeoff of an active reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication system operating under residual environmental clutter. The BS beamformer and active-RIS coefficients are jointly designed while accounting for amplifier noise on the forward and return sensing paths, a global RIS power budget, and perelement amplification limits. Residual clutter introduces a beamformer-dependent term in the sensing denominator, leading to a generally indefinite, nonconvex sensing-QoS constraint in the communication-centric (C-C) design and a fractional quadratic beamformer objective in the sensing-centric (S-C) design. We develop alternating optimization procedures for both formulations based on established MM, Kronecker-vectorization, and Dinkelbach techniques. Their QoS-threshold sweeps are refined and filtered to …

Integrated sensing and communication (ISAC) is expected to be a key enabler for future wireless networks, improving spectral and hardware efficiency by jointly performing radar sensing and wireless communication within a unified framework. This paper proposes BeamNet , an unsupervised deep learning framework for transmit beamforming in dual-function radar-communication systems operating over general fading with imperfect channel state information (CSI). BeamNet maps noisy estimates of the communication and sensing channels to a transmit beamforming vector and is trained end-to-end by maximizing a weighted sum of the communication rate (CR) and sensing rate (SR), thereby learning the CR–SR Pareto frontier without beamforming labels or embedded optimization solvers. Using Rayleigh fading with perfect CSI, we first show that BeamNet reproduces the analytical Pareto-optimal beamforming solutions. We then use BeamNet to characterize, for Nakagami- m and Rician fading, the CR–SR trade-off across a range of fading parameters, and to assess robustness under distribution mismatch between training and test channels. Finally, under imperfect CSI, we demonstrate that BeamNet yields CR–SR trade-offs that are consistently sandwiched between the perfect-CSI and mismatched analytical baselines, outperforming the closed-form beamformer applied to imperfect CSI and recovering part of the performance loss caused by channel estimation errors. These results indicate that unsupervised learning offers a flexible and robust approach to ISAC beamforming in fading environments with imperfect channel knowledge.

The Relative Transfer Matrix (ReTM), recently introduced as a generalization of the relative transfer function for multiple receivers and sources, shows promising performance when applied to speech enhancement in noisy environments. Estimating the ReTM of sound sources by exploiting the covariance matrices of multichannel recordings is highly beneficial for practical applications and, to date, remains the only proposed approach. This paper investigates deep learning-based ReTM estimation. We propose three novel supervised learning frameworks using time and short-time frequency transform domain convolutional networks, and a Long Short-Term Memory-based recurrent neural network. Experimental results demonstrate that the proposed models achieve more accurate estimation of the ReTM using five objective metrics compared to the covariance-based method. We also show the effectiveness of the proposed frameworks for speech enhancement, achieving performance on par with the baseline method.

Isolating a desired speech signal in noisy multi-talker conversational scenarios is a key requirement for augmented reality (AR) wearable microphone array systems. In this work, a binaural target speaker extraction (TSE) framework, termed BiTSE, is proposed. It leverages both spatial and temporal cues, specifically the direction-of-arrival (DoA) of the target speaker and corresponding voice activity information, to guide the extraction process. Built upon a binaural signal denoising architecture, our model integrates three key enhancements: (i) a DoA-aware attention mechanism using cyclic positional embeddings, (ii) a timestamp-based masking strategy that utilizes speaker activity to suppress non-target segments, and (iii) a novel two-stage loss optimization strategy that first trains the model for robust denoising and then fine-tunes it to improve perceptual quality. Evaluations on the SPeech Enhancement for Augmented Reality (SPEAR) challenge dataset demonstrate that the proposed BiTSE consistently improves upon conventional approaches, leading to enhanced signal fidelity and perceptual quality.

With growing applications of drones, there is a demand for drone audition functionality that allows microphone arrays to be embedded into or attached to the body of drones for sound source localization and signal enhancement. In practical acoustical environments, the performance of drone audition is degraded due to the free-field sound propagation assumption. In this paper, we propose (i) a method for modeling the scattering and diffraction of a drone body beyond free-field propagation as a Drone-Related Transfer Function (DRTF), and (ii) a practical method to estimate the DRTF coefficients using circular loudspeaker arrays. The method incorporates the acoustic reciprocity principle and a finite order spherical harmonic expansion by utilizing a discrete set of measurements by multiple loudspeaker placements on circular arrays. We validate our DRTF model and parameterization method on a known rigid sphere …

The Relative Transfer Matrix (ReTM), recently introduced as a generalization of the relative transfer function for multiple receivers and sources, shows promising performance when applied to speech enhancement and speaker separation in noisy environments. Blindly estimating the ReTM of sound sources by exploiting the covariance matrices of multichannel recordings is highly beneficial for practical applications. In this paper, we use covariance subtraction to present a flexible and practically viable method for estimating the ReTM for a select set of independent sound sources. To show the versatility of the method, we validated it through a speaker separation application under reverberant conditions. Separation performance is evaluated at low signal-to-noise ratio levels in comparison with existing ReTM-based and relative transfer function-based estimators, in both simulated and real-life environments.

Real-time sensing and perception of the radio spectrum based on artificial intelligence (AI) is crucial for emerging intelligent wireless and electronic warfare systems. However, sensing can be greatly impacted by harmful radio frequency interference (RFI). Emerging drone warfare allows many RFI sources/jammers to be distributed across a wide field-of-view which necessitates real-time measurement, adaptation and aperture nulling to remove the RFI before AI-based sensing and perception of sources of interest can occur. This work explores algorithmic innovations that improve the computational complexity of classical Howells-Applebaum adaptive nulling algorithm to enable fast, real-time adaptive operation at significantly lower arithmetic complexity. Design examples for AI-enabled sensing and perception across a 32-element antenna receiver with 32 independent channels and a Xilinx Virtex-6 Sx475 FPGA …

Drone swarms pose a threat to national security. There is growing interest in an “Iron Dome for America” to protect against weaponized drones, missiles and other threats. The sensing, classification and characterization of a swarm of drones-typically with hundreds of devices flying in close proximity to each other-is a challenging radar and AI/ML problem. In this paper, we report on early work on using state-of-the-art AI/ML algorithms with advanced digital signal processing towards demonstrated capabilities for UAS swarm detection and classification at the laboratory scale. An automotive grade 77 GHz FMCW radar evaluation board, and commercial (toy) micro drones are used for experimentation. The FMCW pulses use 3 GHz of bandwidth. All experiments are conducted within a short range whereas long range experiments reserved for future work.

Discrete Fourier Transform (DFT) based beam-formers provide N beams with a uniform linear array having N antennas. Despite low-complexity, DFT beamformers cannot generate deep nulls at arbitrary directions of arrivals (DOAs) in the beam pattern to attenuate interferences. On the other hand, data-dependent adaptive beamformers can achieve deep nulls at arbitrary DOAs, however, at higher complexity due to an inversion of a matrix in deriving optimum weights. In this paper, we propose a combined approximate DFT (ADFT) and adaptive beamformer for a uniform linear array consisting of P subarrays, each consisting of M antennas. Here, we first employ P ADFT beamformers to process signals received by subarrays, and the P outputs of these subarrays are then processed by an adaptive beamformer. Simulation results confirm that the proposed beamformer provides the benefits of both DFT and adaptive …

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