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Free, publicly-accessible full text available October 27, 2025
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Multiple-input, multiple-output (MIMO) radars can estimate radial velocities of moving objects, but not their tangential velocities. In this paper, we propose to exploit multi-bounce scattering in the environment to form an effective multi-“look” synthetic aperture and enable estimation of a moving object's entire velocity vector - both tangential and radial velocities. The proposed approach enables instantaneous velocity vector estimation with a single MIMO radar, without additional sensors or assumptions about the object size. The only requirement of our approach is the existence of at least one resolvable multi-bounce path to the object from a static landmark in the environment. The approach is validated both in theory and simulation.more » « lessFree, publicly-accessible full text available July 3, 2025
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Multiple-input, multiple-output (MIMO) radars can estimate radial velocities of moving objects, but not their tangential velocities. In this paper, we propose to exploit multi-bounce scattering in the environment to form an effective multi-“look” synthetic aperture and enable estimation of a moving object's entire velocity vector - both tangential and radial velocities. The proposed approach enables instantaneous velocity vector estimation with a single MIMO radar, without additional sensors or assumptions about the object size. The only requirement of our approach is the existence of at least one resolvable multi-bounce path to the object from a static landmark in the environment. The approach is validated both in theory and simulation.more » « less
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In this paper, we are interested in startle reflex detection with WiFi signals. We propose that two parameters related to the received signal bandwidth, maximum normalized bandwidth and bandwidth-intense duration, can successfully detect reflexes and robustly differentiate them from non-reflex events, even from those that involve intense body motions (e.g., certain exercises). In order to confirm this, we need a massive RF reflex dataset which would be prohibitively laborious to collect. On the other hand, there are many available reflex/non-reflex videos online. We then propose an efficient way of translating the content of a video to the bandwidth of the corresponding received RF signal that would have been measured if there was a link near the event in the video, by drawing analogies between our problem and the classic bandwidth modeling work of J. Carson in the context of analog FM radios (Carson's Rule). This then allows us to translate online reflex/non-reflex videos to an instant large RF bandwidth dataset, and characterize optimum 2D reflex/non-reflex decision regions accordingly, to be used during real operation with WiFi. We extensively test our approach with 203 reflex events, 322 non-reflex events (including 142 intense body motion events), over four areas (including several through-wall ones), and with 15 participants, achieving a correct reflex detection rate of 90.15% and a false alarm rate of 2.49% (all events are natural). While the paper is extensively tested with startle reflexes, it is also applicable to sport-type reflexes, and is thus tested with sport-related reflexes as well. We further show reflex detection with multiple people simultaneously engaged in a series of activities. Optimality of the proposed design is also demonstrated experimentally. Finally, we conduct experiments to show the potential of our approach for providing cost-effective and quantifiable metrics in sports, by quantifying a goalkeeper's reaction. Overall, our results confirm a fast, robust, and cost-effective reflex detection system, without collecting any RF training data, or training a neural network.more » « less
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In this review, we present a comprehensive perspective on communication-aware robotics, an area that considers realistic communication environments and aims to jointly optimize communication and navigation. The main focus of the article is theoretical characterization and understanding of performance guarantees. We begin by summarizing the best prediction an unmanned vehicle can have of the channel quality at unvisited locations. We then consider the case of a single robot, showing how it can mathematically characterize the statistics of its traveled distance until connectivity and further plan its path to reach a connected location with optimality guarantees, in real channel environments and with minimum energy consumption. We then move to the case of multiple robots, showing how they can utilize their motions to enable robust information flow. We consider two specific robotic network configurations—robotic beamformers and robotic routers—and mathematically characterize properties of the co-optimum motion–communication decisions.more » « less