18 accepted papers
Multi-view indoor radar perception has drawn attention due to its cost-effectiveness and low privacy risks. Existing methods often rely on implicit cross-view radar feature association, such as proposal pairing in RFMask or query-to-feature cross-attention in RETR, which can lead to ambiguous featur
This paper addresses the integration of millimeter-wave (mmWave) Wi-Fi communication and sensing during data transmission intervals (DTIs). We leverage prior knowledge from codebook beam training conducted during preceding beacon transmission intervals (BTIs) and association beamforming training (A-…
The Radar dEtection TRansformer (RETR) has recently been introduced to fuse multi-view millimeter-wave radar heatmaps by leveraging the detection transformer architecture and a geometric learning framework for indoor radar perception. A notable feature of RETR is its tunable positional encoding (TPE…
Radar-based indoor 3D human pose estimation typically relied on fine-grained 3D keypoint labels, which are costly to obtain especially in complex indoor settings involving clutter, occlusions, or multiple people. In this paper, we propose \textbf{RAPTR} (RAdar Pose esTimation using tRansformer) unde…
"∗ : Equal contribution. † : The work of M. Rahman (Univ. of Alabama, USA), S. Kato (Osaka Univ., Japan), P. Li (Brandeis Univ., USA), and A. Cardace (Univ. of Bologna, Italy) was done during their internship at MERL. ♯ : The work was done as a visiting scientist from Mitsubishi Electric Corporation…
This paper considers object detection with millimeter-wave (mmWave) Wi-Fi beam training frames, e.g., beacon frames, in a monostatic passive directional multi-gigabit (DMG) sensing configuration. We derive an explicit signal model that accounts for the preamble, frame-to-frame antenna gains, and clu…
In contrast to existing multi-band Wi-Fi fusion in a frame-to-frame basis for simple classification, this paper considers asynchronous sequence-to-sequence fusion between sub-7GHz channel state information (CSI) and 60GHz beam SNR for more challenging downstream tasks such as continuous regression.…
Indoor radar perception has seen rising interest due to affordable costs driven by emerging automotive imaging radar developments and the benefits of reduced privacy concerns and reliability under hazardous conditions (e.g., fire and smoke). However, existing radar perception pipelines fail to accou…
Due to the noise and low spatial resolution in automotive radar data, exploring temporal relations of learnable features over consecutive 2 radar frames has shown performance gain on downstream tasks (e.g., object detection and tracking) in our previous study [1]. In this paper, we further enhance r…
Conventional radar feature extraction faces limitations due to low spatial resolution noise multipath reflection the presence of ghost targets and motion blur. Such limitations can be exacerbated by nonlinear object motion particularly from an ego-centric viewpoint. It becomes evident that to addres…
Recent developments in wave-based sensor technologies, such as ground penetrating radar (GPR), provide new opportunities for accurate imaging of underground scenes. Given measurements of the scattered electromagnetic wavefield, the goal is to estimate the spatial distribution of the permittivity of…
We consider the problem of simultaneously learning a convex penalty function and its proximity operator for image reconstruction from incomplete measurements. Our goal is to apply Accelerated Proximal Gradient Method (APGM) using a learned proximity operator in place of the true proximity operator o…
In frequency-modulated continuous-wave (FMCW) lidar, the distance to an illuminated target is proportional to the beat frequency of the interference signal. Laser phase noise often limits the range accuracy of FMCW lidar, and existing frequency estimation methods make overly simplistic assumptions a…
This paper considers spatial-domain detector design for mutual interference mitigation among automotive MIMO-FMCW radars. This detector design is based on our previously derived interference signal model that fully accounts for the time-frequency incoherence and the slow-time code incoherence betwee…
We leverage standards-compliant beam training measurements from commercial-of-the-shelf (COTS) 802.11ad/ay devices for localization of a moving object. Two technical challenges need to be addressed: (1) the beam training measurements are intermittent due to beam scanning overhead control and content…
This paper introduces a B-spline chained ellipses model representation for extended object tracking (EOT) using high-resolution automotive radar measurements. With offline automotive radar training datasets, the proposed model parameters are learned using the expectation-maximization (EM) algorithm.…
Motivated by real-world automotive radar measurements that are distributed around object (e.g., vehicles) edges with a certain volume, a novel hierarchical truncated Gaussian measurement model is proposed to resemble the underlying spatial distribution of radar measurements. With the proposed measur…
This paper considers object detection in the case of imperfect waveform separation, in the context of automotive radars with a slow-time MIMO-FMCW signaling scheme. We develop an explicit signal model that accounts for waveform separation residuals and propose a Kronecker subspace-based object detec…