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1. UAV Radar Sensing of Respiratory Variations for COVID-Type Disorders

   https://doi.org/10.1109/APMC47863.2020.9331613  

   Islam, Shekh M.; Grado, Christian; Lubecke, Victor; Lubecke, Lana C. (December 2020, 020 IEEE Asia-Pacific Microwave Conference (APMC))

   null (Ed.)

   Unmanned Aerial Vehicles (UAVs) with onboard Doppler radar sensors can be used for health reconnaissance including the remote detection of respiratory patterns associated with COVID-19. While respiratory diagnostics have been demonstrated with radar, the motion of the airborne introduces motion interference. An adaptive filter method is applied here which uses a second radar facing a non-moving surface (ceiling) for a nose cancellation reference signal. Variations in respiratory rate and displacement have been demonstrated which is consistent with the need for detecting tachypnea associated with COVID-19. 

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2. Joint Transmit Waveform and Receive Filter Design for Dual-Functional Radar-Communication Systems

   https://doi.org/10.1109/ICC45855.2022.9838990  

   Liu, Rang; Li, Ming; Liu, Qian; Lee Swindlehurst, A. (May 2022, Proc. International Conference on Communications)

   Space-time adaptive processing (STAP) is an effective method for multi-input multi-output (MIMO) radar systems to identify moving targets in the presence of multiple interferers. The idea of joint optimization in both spatial and temporal domains for radar detection is consistent with the symbol-level precoding (SLP) technique for MIMO communication systems, that optimizes the transmit waveform according to instantaneous transmitted symbols. Therefore, in this paper we combine STAP and constructive interference (CI)-based SLP techniques to realize dual-functional radar-communication (DFRC). The radar output signal-to-interference-plus-noise ratio (SINR) is maximized by jointly optimizing the transmit waveform and receive filter, while satisfying the communication quality-of-service (QoS) constraints and the constant modulus power constraint. An efficient algorithm based on majorization-minimization (MM) and nonlinear equality constrained alternative direction method of multipliers (neADMM) methods is proposed to solve the non-convex optimization problem. Simulation results verify the effectiveness of the proposed DFRC scheme and the associate algorithm. 

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3. Measurement Study of FMCW Radar Configurations for Non-contact Vital Signs Monitoring

   https://doi.org/10.1109/RadarConf2248738.2022.9764200  

   Xie, Zongxing; Hua, Yindong; Ye, Fan (January 2022, IEEE Radar Conference)

   Non-contact vital signs monitoring (NCVSM) with radio frequency (RF) is attracting increasing attention due to its non-invasive nature. Recent advances in COTS radar technologies accelerate the development of RF-based solutions. While researchers have implemented and demonstrated the feasibility of NCVSM with diverse radar hardware, most efforts have been focused on devising algorithms to extract vital signs, with limited understanding about the effects of radar configurations. The deficiency in such understanding hinders the design of software defined radar (SDR) optimally customized for NCVSM. In this work, we first hypothesize the effects of FMCW radar configurations using signal-to-interference-plus-noise ratio (SINR) based signal modeling, then we conduct extensive experiments with a COTS FMCW radar, TinyRad, to understand how various parameters impact NCVSM performance compared to a medical device. We find that a larger bandwidth or higher transmitting power in general improves vital sign estimation accuracy; however, coherent processing of consecutive chirps (time diversity) or multiple receiving antennas (space diversity) does not improve the performance. Observations on the baseband (BB) signal show that coherent processing contributes to a higher amplitude but similar phase patterns, whose periodic changes are the key in extracting vital signs. 

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4. Periodic Signal Denoising: An Analysis-Synthesis Framework Based on Ramanujan Filter Banks and Dictionaries

   https://doi.org/10.1109/ICASSP39728.2021.9413689  

   Kulkarni, Pranav; Vaidyanathan, P. P. (June 2021, Proc. IEEE Int. Conf. Acoust. Speech, and Signal Proc)

   null (Ed.)

   Ramanujan filter banks (RFB) have in the past been used to identify periodicities in data. These are analysis filter banks with no synthesis counterpart for perfect reconstruction of the original signal, so they have not been useful for denoising periodic signals. This paper proposes to use a hybrid analysissynthesis framework for denoising discrete-time periodic signals. The synthesis occurs via a pruned dictionary designed based on the output energies of the RFB analysis filters. A unique property of the framework is that the denoised output signal is guaranteed to be periodic unlike any of the other methods. For a large range of input noise levels, the proposed approach achieves a stable and high SNR gain outperforming many traditional denoising techniques. 

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5. Exploitation of Symmetrical Non-Convexity for Symbol-Level DFRC Signal Design

   https://doi.org/10.1109/ICC51166.2024.10622712  

   Nguyen, Ly V; Liu, Rang; Swindlehurst, A Lee (June 2024, IEEE)

   Constructive interference exploited by symbol-level (SL) signal processing is a promising solution for addressing the inherent interference problem in dual-functional radar-communication (DFRC) signal designs. This paper considers an SL-DFRC signal design problem which maximizes the radar performance under communication performance constraints. We exploit the symmetrical non-convexity property of the communication-independent radar sensing metric to develop low- complexity yet efficient algorithms. We first propose a radar-to- DFRC (R2DFRC) algorithm that relies on the non-convexity of the radar sensing metric to find a set of radar-only solutions. Based on these solutions, we further exploit the symmetrical property of the radar sensing metric to efficiently design the DFRC signal. Since the radar sensing metric is independent of the communication channel and data symbols, the set of radar-only solutions can be constructed offline, therefore reducing the computational complexity. We then develop an accelerated R2DFRC algorithm that further reduces the complexity. Finally, we demonstrate the superiority of the proposed algorithms compared to existing methods in terms of both radar sensing and communication performance as well as computational complexity. 

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