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 feature matches and degraded detection in complex indoor scenes. To address these limitations, we propose REXO (multi-view Radar object dEtection with 3D bounding boX diffusiOn), which lifts the 2D bounding box (BBox) diffusion process of DiffusionDet into the 3D radar space. REXO utilizes these noisy 3D BBoxes to guide an explicit cross-view radar feature association, enhancing the cross-view radar-conditioned denoising process. By accounting for prior knowledge that the person is in contact with the ground, REXO reduces the number of diffusion parameters by determining them from this prior. Evaluated on two open indoor radar datasets, our approach surpasses state-of-the-art methods by a margin of +4.22 AP on the HIBER dataset and +11.02 AP on the MMVR dataset. Our implementation is available at https://github.com/merlresearch/radar-bbox-diffusion.

In this paper, we propose an algorithm for the removal of ghost targets based on angle estimation termed GREST (Ghost targets Removal using ESTAR). In the proposed GREST, the tentative targets reports, including the azimuth angles, is obtained through the conventional 3-D coherent integrations and signal detection at first. Then two-way angles, defined as a combination of direction-of-arrival (DOA) and direction-of-departure (DOD), are estimated using the ESTAR (Estimation of Two-way Angle by MIMO Radar), which was proposed by the authors recently. Subsequently, the ghost targets are removed based on the estimated angles by comparing DOD and DOA to finalize the target reports. The ESTAR method represents a novel approach to angle estimation that yields unambiguous results, even in the case of MIMO radars with transmit sparse arrays, which are commonly utilized in the standard millimeter-wave radars. Based on these angles, it is possible to distinguish between direct and multipath propagation, thereby enabling the removal of ghost targets. In this paper, we will present the detail of the proposed GREST and demonstrate its effectiveness in removing ghost targets using experimental data acquired in a situation where multipath propagation is occurring.

In this paper, we propose a method that enables one-dimensional estimation of angular velocity of a high-speed target by adding preprocessing to a received signal of a digital array radar. The proposed method enables two separate one-dimensional parameter estimations of angular velocity and angle, as opposed to the conventional method using two-dimensional searches. By one-dimensionalizing, both parameters can be calculated using monopulse principles, reducing the computational scale to approximately 1/1000 to 1/10,000 that of the conventional method. Meanwhile, computer simulation evaluations confirmed that the proposed method is better than the conventional method in terms of parameter estimation accuracy at a signal to noise power ratio (SNR) of around 15 dB, and it approaches the Cramer-Rao lower bound at high SNRs.

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), which allows for adjusting the significance of depth positional embedding across multiple views to promote depth-prioritized feature association. However, the TPE ratio is predetermined, rather than being optimized during the training process. In this paper, we propose a differentiable positional encoding (DiPE) scheme for RETR by automatically adjusting the TPE ratio during the training for enhanced performance and avoiding exhaustive grid value search. DiPE can be applied along with either pre-fixed (e.g., sinusoidal) or learnable positional embeddings, achieved by multiplying dual differentiable masks over the depth and angular positional embedding vectors. Comprehensive evaluations on the open MMVR dataset demonstrate that the proposed DiPE not only simplifies the determination of the TPE ratio but also enhances the overall detection performance.

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 address these challenges, the key lies in exploiting temporal feature relation over an extended horizon and enforcing spatial motion consistency for effective association. To this end, this paper proposes SIRA (Scalable Inter-frame Relation and Association) with two designs. First, inspired by Swin Transformer, we introduce extended temporal relation, generalizing the existing temporal relation layer from two consecutive frames to multiple inter-frames with temporally regrouped window attention for scalability. Second, we propose motion consistency track with the concept of a pseudo-tracklet generated from observational data for better trajectory prediction and subsequent object association. Our approach achieves 58.11 [email protected] for oriented object detection and 47.79 MOTA for multiple object tracking on the Radiate dataset, surpassing previous state-of-the-art by a margin of +4.11 [email protected] and +9.94 MOTA, respectively.

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 radar perception by significantly extending the time horizon of temporal relations. To this end, we propose a scalable connective temporal radar (SCTR) method that consists of 1) a standard temporal relation layer (TRL), 2) a connective TRL with shifted window attention, and 3) a window merging operation, to facilitate feature connectivity between radar frames over an extended time interval. Our complexity analysis and comprehensive evaluation of the Radiate dataset demonstrate that the SCTR achieves a great tradeoff between the complexity and downstream detection performance.

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 account for distinctive characteristics of the multi-view radar setting. In this paper, we propose Radar dEtection TRansformer (RETR), an extension of the popular DETR architecture, tailored for multi-view radar perception. RETR inherits the advantages of DETR, eliminating the need for hand-crafted components for object detection and segmentation in the image plane. More importantly, RETR incorporates carefully designed modifications such as 1) depth-prioritized feature similarity via a tunable positional encoding (TPE); 2) a tri-plane loss from both radar and camera coordinates; and 3) a learnable radar-to-camera transformation via reparameterization, to account for the unique multi-view radar setting. Evaluated on two indoor radar perception datasets, our approach outperforms existing state-of-the-art methods by a margin of 15.38+ AP for object detection and 11.91+ IoU for instance segmentation, respectively. Our implementation is available at https://github.com/merlresearch/radar-detection-transformer.

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 between the victim and interfering radars. Compared with our previous spatial-domain detector in [1], the proposed detector further exploits the structural property of both transmit and receive steering vectors of the interference for stronger interference mitigation. Preliminary numerical results confirm the performance of our proposed detector and show advantages over baseline detectors.

In this paper, we investigate wireless-powered communication system (WPCS) with multiple frequency bands. When ambient RF signals can be used as an only energy source of wireless devices (WDs), a traditional signal-generator-based communication (SGC) might be demanding due to its high power consumption mainly because of RF-chain, so that backscatter communication (BC) has gained much attention recently. However, the bandwidth efficiency of BC is significantly limited, and thus BC is not always the best option for WPCSs. Thus, we analyze this trade-off by comparing their achievable capacity and show conditions in which the throughput of SGC is higher than BC when multiple frequency bands are available as an energy source and communication channel. Based on the analyses, we propose a hybrid approach with SGC and BC, which enables WDs to decide the best frequency band and communication method in a distributed manner.

This paper presents new algorithms for moving target detection and velocity estimation for multi-channel SAR (Synthetic Aperture Radar) system. The algorithms are derived as the extensions of conventional DPCA (Displaced Phase Center Antenna) and ATI (Along Track Interferometry) to the multichannel case. The proposed multi-channel DPCA based on orthogonal projection completely suppresses the static clutter even with the presence of the azimuth ambiguity. The multi-channel ATI utilizes the phase differences of all the pairs of the channels to estimate the target radial velocity. The combination of the two, which we call multi-channel DPCA-ATI, is also proposed as the more robust velocity estimation method.

Derivation of monopulse angle accuracy for phased array radar to achieve Cramer-Rao lower bound is presented in this paper. Antenna element positions originating from antenna center are used for difference beam taper in this monopulse angle estimation. For uniform linear array, the accuracy is 1.16 times higher than conventional monopulse method. In other words, SNR can be reduced by 1.3 dB to achieve required angle accuracy. Suboptimal difference beamforming taper for the subarray-based digital beamforming radar is also derived.