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1. Universal MIMO Jammer Mitigation via Secret Temporal Subspace Embeddings

   G. Marti; C. Studer (October 2023, Conference record Asilomar Conference on Signals Systems Computers)

   MIMO processing enables jammer mitigation through spatial filtering, provided that the receiver knows the spatial signature of the jammer interference. Estimating this signature is easy for barrage jammers that transmit continuously and with static signature, but difficult for more sophisticated jammers: Smart jammers may deliberately suspend transmission when the receiver tries to estimate their spatial signature, they may use time-varying beamforming to continuously change their spatial signature, or they may stay mostly silent and jam only specific instants (e.g., transmission of control signals). To deal with such smart jammers, we propose MASH, the first method that indiscriminately mitigates all types of jammers: Assume that the transmitter and receiver share a common secret. Based on this secret, the transmitter embeds (with a linear time-domain transform) its signal in a secret subspace of a higher-dimensional space. The receiver applies a reciprocal linear transform to the receive signal, which (i) raises the legitimate transmit signal from its secret subspace and (ii) provably transforms any jammer into a barrage jammer, which makes estimation and mitigation via MIMO processing straightforward. We show the efficacy of MASH for data transmission in the massive multi-user MIMO uplink. 

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2. Weak Target Detection in MIMO Radar via Beamspace Canonical Correlation

   https://doi.org/10.1109/SAM48682.2020.9104284  

   Ibrahim, Mohamed Salah; Sidiropoulos, Nicholas D. (June 2020, 2020 IEEE International Workshop on Sensor Array and Multichannel Signal Processing (SAM 2020))

   Reliable detection and accurate estimation of weak targets and their Doppler frequencies is a challenging problem in MIMO radar systems. Reflections from such targets are often overpowered by those from stronger nearby targets and clutter. Considering a 3-D data model where the coherent processing interval comprises multiple pulses, a novel weak target detection and estimation approach is proposed in this paper. The proposed method is based on creating partially overlapping spatial beams, and performing canonical correlation analysis (CCA) in the resulting beamspace. It is shown that if a target is present in the overlap sector, then its Doppler profile can be reliably estimated via beamspace CCA, even if hidden under much stronger interference from nearby targets and clutter. Numerical results are included to validate this theoretical claim, demonstrating that the proposed Beamspace Canonical Correlation (BCC) method yields considerable performance improvement over existing approaches. 

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3. Clutter Suppression for Target Detection Using Hybrid Reconfigurable Intelligent Surfaces

   https://doi.org/10.1109/RadarConf2351548.2023.10149583  

   Wang, Fangzhou; Li, Hongbin; Swindlehurst, A. Lee (May 2023, 2023 IEEE Radar Conference (RadarConf23))

   Reconfigurable intelligent surface (RIS) technology is a promising approach being considered for future wireless communications due to its ability to control signal propagation with low-cost elements. This paper explores the use of an RIS for clutter mitigation and target detection in radar systems. Unlike conventional reflect-only RIS, which can only adjust the phase of the reflected signal, or active RIS, which can also amplify the reflected signal at the cost of significantly higher complexity, noise, and power consumption, we exploit hybrid RIS that can configure both the phase and modulus of the impinging signal by absorbing part of the signal energy. Such RIS can be considered as a compromise solution between conventional reflect-only and active RIS in terms of complexity, power consumption, and degrees of freedoms (DoFs). We consider two clutter suppression scenarios: with and without knowledge of the target range cell. The RIS design is formulated by minimizing the received clutter echo energy when there is no information regarding the potential target range cell. This turns out to be a convex problem and can be efficiently solved. On the other hand, when target range cell information is available, we maximize the received signal-to-noise-plus-interference ratio (SINR). The resulting non-convex optimization problem is solved through fractional programming algorithms. Numerical results are presented to demonstrate the performance of the proposed hybrid RIS in comparison with conventional RIS in clutter suppression for target detection. 

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4. A Spatial Coherence Beamformer Design for Power Doppler Imaging

   https://doi.org/10.1109/TMI.2019.2953657  

   Ozgun, Kathryn; Tierney, Jaime; Byram, Brett (January 2020, IEEE Transactions on Medical Imaging)

   (Early Access) Acoustic clutter is a primary source of image degradation in ultrasound imaging. In the context of flow imaging, tissue and acoustic clutter signals are often much larger in magnitude than the blood signal, which limits the sensitivity of conventional power Doppler in SNR-limited environments. This has motivated the development of coherence-based beamformers, including Coherent Flow Power Doppler (CFPD), which have demonstrated efficacy in mitigating sources of diffuse clutter. However, CFPD uses a measure of normalized coherence, which incurs a non-linear relationship between image intensity and the magnitude of the blood echo. As a result, CFPD is not a robust approach to study gradation of blood signal energy, which depicts the fractional moving blood volume. We propose the application of mutual intensity, rather than normalized coherence, to retain the clutter suppression capability inherent in coherence beamforming, while preserving the underlying signal energy. Feasibility of this approach was shown via Field II simulations, phantoms, and in vivo human liver data. In addition, we derive an adaptive statistical threshold for the suppression of residual noise signals. Overall, this beamformer design shows promise as an alternative technique to depict flow volume gradation in cluttered imaging environments. 

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5. Joint Jammer Mitigation and Data Detection for Smart, Distributed, and Multi-Antenna Jammers

   https://doi.org/10.1109/ICC45041.2023.10278647  

   Marti, Gian; Studer, Christoph (May 2023, IEEE International Conference on Communications)

   Multi-antenna (MIMO) processing is a promising solution to the problem of jammer mitigation. Existing methods mitigate the jammer based on an estimate of its subspace (or receive statistics) acquired through a dedicated training phase. This strategy has two main drawbacks: (i) it reduces the communication rate since no data can be transmitted during the training phase and (ii) it can be evaded by smart or multi-antenna jammers that are quiet during the training phase or that dynamically change their subspace through time-varying beamforming. To address these drawbacks, we propose joint jammer mitigation and data detection (JMD), a novel paradigm for MIMO jammer mitigation. The core idea is to estimate and remove the jammer interference subspace jointly with detecting the transmit data over multiple time slots. Doing so removes the need for a dedicated rate-reducing training period while enabling the mitigation of smart and dynamic multi-antenna jammers. We instantiate our paradigm with SANDMAN, a simple and practical algorithm for multi-user MIMO uplink JMD. Extensive simulations demonstrate the efficacy of JMD, and of SANDMAN in particular, for jammer mitigation. 

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