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1. Instantaneous Velocity Vector Estimation Using a Single MIMO Radar Via Multi-Bounce Scattering

   Mehrotra, Nishant; Pandey, Divyanshu; Madhow, Upamanyu; Mostofi, Yasamin; Sabharwal, Ashutosh (July 2024, IEEE)

   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. 

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2. Single-Frame MIMO Radar Velocity Vector Estimation via Multi-Bounce Scattering

   https://doi.org/10.1109/TCI.2025.3594013  

   Mehrotra, Nishant; Pandey, Divyanshu; Madhow, Upamanyu; Mostofi, Yasamin; Sabharwal, Ashutosh (July 2025, IEEE Transactions on Computational Imaging)

   Radars are widely adopted for autonomous navigation and vehicular networking due to their robustness to weather conditions as compared to visible light cameras and lidars. However, radars currently struggle with differentiating static vs tangentially moving objects within a single radar frame since both yield the same Doppler along line-of-sight paths to the radar. Prior solutions deploy multiple radar or visible light camera modules to form a multi-“look” synthetic aperture for estimating the single-frame velocity vectors, to estimate tangential and radial velocity components of moving objects leading to higher system costs. In this paper, we propose to exploit multi-bounce scattering from secondary static objects in the environment, e.g., building pillars, walls, etc., to form an effective multi-“look” synthetic aperture for single-frame velocity vector estimation with a single multiple-input, multiple-output (MIMO) radar, thus reducing the overall system cost and removing the need for multi-module synchronization. We present a comprehensive theoretical and experiment evaluation of our scheme, demonstrating a 4.5× reduction in the error for estimating moving objects’ velocity vectors over comparable single-radar baselines. 

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3. A Novel Joint Angle-Range-Velocity Estimation Method for MIMO-OFDM ISAC Systems

   https://doi.org/10.1109/TSP.2024.3442886  

   Xiao, Zichao; Liu, Rang; Li, Ming; Liu, Qian; Swindlehurst, A Lee (January 2024, IEEE Transactions on Signal Processing)

   Integrated sensing and communication (ISAC) is emerging as a key technique for next-generation wireless systems. In order to expedite the practical implementation of ISAC in pervasive mobile networks, it is crucial to have widely deployed base stations with radar sensing capabilities. Thus, the utilization of standardized multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) hardware architectures and waveforms is pivotal for realizing seamless integration of effective communication and sensing functionalities. In this paper, we introduce a novel joint anglerange- velocity estimation algorithm for MIMO-OFDM ISAC systems. This approach exclusively depends on the format of conventional MIMO-OFDM waveforms that are widely adopted in wireless communications. Specifically, the angle-range-velocity information of potential targets is jointly extracted by utilizing all the received echo signals within a coherent processing interval (CPI). The proposed joint estimation algorithm can achieve larger signal-to-noise-ratio (SNR) processing gains and higher resolution by fully exploiting the echo signals and jointly estimating the angle-range-velocity information. A theoretical analysis for maximum unambiguous range, resolution, and SNR processing gains is provided to verify the advantages of the proposed joint estimation algorithm. Finally, the results of extensive numerical experiments are presented to demonstrate that the proposed joint estimation approach can achieve significantly lower rootmean- square-error (RMSE) performance for angle/range/velocity estimation for both single- and multi-target scenarios. 

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4. Discovery of a split stellar stream in the periphery of the Small Magellanic Cloud

   https://doi.org/10.1093/mnras/stae1783  

   Nidever, David L (August 2024, Monthly Notices of the Royal Astronomical Society)

   ABSTRACT I report the discovery of a stellar stream (Sutlej) using Gaia DR3 (third data release) proper motions and XP metallicities located $$\sim$$15° north of the Small Magellanic Cloud (SMC). The stream is composed of two parallel linear components (‘branches’) approximately $$\sim$$8° × 0.6° in size and separated by 2.5°. The stars have a mean proper motion of ($$\mu _{\rm RA},\mu _{\rm Dec.}$$) = (+0.08 mas yr−1, −1.41 mas yr−1), which is quite similar to the proper motion of stars on the western side of the SMC. The colour–magnitude diagram of the stream stars has a clear red giant branch, horizontal branch, and main-sequence turn-off that are well matched by a parsec isochrone of 10 Gyr, [Fe/H] = −1.8 at 32 kpc, and a total stellar mass of $$\sim$$33 000 M$$_{\odot }$$. The stream is spread out over an area of 9.6 deg2 and has a surface brightness of 32.5 mag arcsec−2. The metallicity of the stream stars from Gaia XP spectra extends over $-2.5$$\le$$ [M/H] $$\le$$-1.0$ with a median of [M/H] = −1.8. The tangential velocity of the stream stars is 214 km s−1 compared to the values of 448 km s−1 for the Large Magellanic Cloud and 428 km s−1 for the SMC. While the radial velocity of the stream is not yet known, a comparison of the space velocities using a range of assumed radial velocities shows that the stream is unlikely to be associated with the Magellanic Clouds. The tangential velocity vector is misaligned with the stream by nearly 90°, which might indicate an important gravitational influence from the nearby Magellanic Clouds. 

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5. The 3D Kinematics of the Orion Nebula Cluster: NIRSPEC-AO Radial Velocities of the Core Population

   https://doi.org/10.3847/1538-4357/ac3252  

   Theissen, Christopher A.; Konopacky, Quinn M.; Lu, Jessica R.; Kim, Dongwon; Zhang, Stella Y.; Hsu, Chih-Chun; Chu, Laurie; Wei, Lingfeng (February 2022, The Astrophysical Journal)

   Abstract The kinematics and dynamics of stellar and substellar populations within young, still-forming clusters provide valuable information for constraining theories of formation mechanisms. Using Keck II NIRSPEC+AO data, we have measured radial velocities for 56 low-mass sources within 4′ of the core of the Orion Nebula Cluster (ONC). We also remeasure radial velocities for 172 sources observed with SDSS/APOGEE. These data are combined with proper motions measured using HST ACS/WFPC2/WFC3IR and Keck II NIRC2, creating a sample of 135 sources with all three velocity components. The velocities measured are consistent with a normal distribution in all three components. We measure intrinsic velocity dispersions of ( σ v α , σ v δ , σ v r ) = (1.64 ± 0.12, 2.03 ± 0.13, 2.56 − 0.17 + 0.16 ) km s −1 . Our computed intrinsic velocity dispersion profiles are consistent with the dynamical equilibrium models from Da Rio et al. (2014) in the tangential direction but not in the line-of-sight direction, possibly indicating that the core of the ONC is not yet virialized, and may require a nonspherical potential to explain the observed velocity dispersion profiles. We also observe a slight elongation along the north–south direction following the filament, which has been well studied in previous literature, and an elongation in the line-of-sight to tangential velocity direction. These 3D kinematics will help in the development of realistic models of the formation and early evolution of massive clusters. 

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