Lightweight, Low-Cost Embedded Radar-Gated Camera Architecture for MCU-Level Motion-Triggered Occupancy Sensing

Sparse4D-Radar: An Efficient and Robust Framework for Surround-View 3D Object Detection via 4D Radar-Camera Fusion

In recent years, 4D imaging radar has gained wide attention in autonomous driving for its robustness against harsh weather and ability to output target velocity. Nevertheless, mainstream 4D radar-camera fusion methods only support front-view perception, lacking mature solutions for surround-view sensing. Directly expanding these pipelines to full 360{\deg} coverage introduces excessive computation cost and limits real-world deployment. To tackle these limitations, this work proposes Sparse4D-Radar, an efficient robust surround-view multi-modal fusion framework. We first design a Deformable Fusion module to embed radar-camera features into sparse queries, constructing the lightweight base version Sparse4D-Radar-Base. Two dedicated modules are further introduced to boost localization accuracy and modality stability: Velocity-Consistency Sampling (VCS) refines features via radar velocity cues for motion awareness, and Adaptive Modality Gating (AMG) dynamically adjusts cross-modal fusion weights according to feature confidence. Combining all components, we build Sparse4D-Radar-Acc for high-precision detection demands. Comprehensive experiments on OmniHD-Scenes verify that our approach achieves state-of-the-art surround-view 3D detection performance. Compared with prior arts, our method obtains over 7% mAP and 10% ODS improvements under complex driving scenes while running at nearly 10 FPS, striking a favorable trade-off among detection accuracy, environmental robustness and inference efficiency. Our open-source code is available at https://github.com/Aiuan/Sparse4D-Radar.

Sequential Radar–Acoustic Drone Detection Using A Lightweight Deep Learning Framework for Edge-Based Sensor Networks

Experimental results demonstrate that the proposed sequential pipeline preserves high specificity while reducing missed detections compared to radar-only processing and indicates that sequential processing can offer a viable alternative to parallel fusion for edge-based drone detection under SNR and computing constraints.

Embedded FMCW Radar Target Detection and Tracking Based on Inter-Frame Differencing and Boundary-Adaptive CA-CFAR

A compact 24 GHz FMCW radar board was evaluated for low-speed bicycle and small-vehicle sensing under strict memory and latency constraints. The hardware uses only 30 MHz modulation bandwidth, giving a nominal range resolution of about 5.0 m and a Doppler-bin spacing of about 2.57 m/s. Its small, incompletely calibrated antenna path also prevents any claim of high-angular-resolution imaging-radar performance. Within this constrained platform, the measured sequences reveal four coupled failure modes: static reflectors remain prominent in the range–Doppler map, useful low-Doppler responses are easily lost near the processed spectral boundary, weak plots do not always initiate a track, and short echo gaps can break otherwise continuous trajectories. To address these limitations, we combine frame-differential range–Doppler enhancement, quadrant-aware boundary-adaptive CA-CFAR, physically gated seed-growing initiation, and finite-frame retained Kalman tracking with SNR-weighted updates. In addition to natural bicycle and small-vehicle measurements, a labeled synthetic 64 by 32 range–Doppler benchmark is used to report Precision, Recall, F1-score, ROC/AUC, detection probability, and false alarms per frame for multiple CFAR variants. Public-radar tracking metrics are also reported on RadarScenes, a public RADIATE foggy sample, and nuScenes mini radar-only sequences with a bounded-approximation JPDA baseline. These public-radar results evaluate tracker-lifecycle and data-association behavior under public target-center observations; they are not presented as full validation of the board-specific RD-to-track pipeline. The evidence supports a bounded embedded-processing claim for this low-resolution board, not general applicability to high-resolution imaging radar systems.

Empirical Evaluation of a Fully Offline, Low-Cost Edge AI Wearable Smart Navigation Assistant for Distance-Aware Obstacle Detection

Real-time visual perception on resource-constrained embedded hardware must reconcile computational economy, low latency, and dependable sensing accuracy within tight power and cost envelopes. This paper reports on the Smart Navigation Device (SND), a wearable assistive perception system that performs object detection, distance ranging, sensor fusion, and speech feedback entirely on a Raspberry Pi 4B without any cloud dependency. At the core of the system is the Cascaded Detection-Ranging Fusion (CDRF) framework, a four-stage pipeline that couples the lightweight YOLO11n detector with ultrasonic time-of-flight ranging through confidence-guided detection acceptance, spatial-zone partitioning, dominant-object association, and adaptive suppression of redundant announcements. The hardware-software co-design keeps every processing stage — image capture, neural inference, ranging, fusion, and text-to-speech synthesis — local to the device, eliminating transmission latency, removing a major privacy exposure, and preserving operability where network connectivity is unreliable or absent. The framework was evaluated across 169 controlled trials spanning three obstacle categories — person, chair, and laptop — at distances from 0.30 m to 4.20 m. Detection rates of 90.4%, 92.9%, and 77.0% were obtained for the three classes respectively, with mean absolute ranging errors of 2.11 cm, 1.60 cm, and 1.79 cm. Agreement between the ultrasonic estimate and ground-truth distance was excellent (Pearson r = 0.9995, p < 10⁻²¹⁹), and Bland–Altman analysis revealed a small systematic bias of −1.10 cm (95% limits of agreement: −7.88 cm to 5.68 cm). A chi-square test indicated a statistically meaningful distance-dependent decline in laptop-class detection reliability. Benchmarked against previously reported wearable travel aids, the SND achieves comparable or better detection reliability than low-cost ultrasonic-only alternatives while additionally providing object identity, and does so at a fraction of the hardware cost and without any of the connectivity dependencies of cloud-assisted alternatives — positioning it as a reproducible, statistically grounded, and economically accessible baseline for future assistive-perception research.

Lightweight Dual Radar Point Cloud Learning for Cross Scene Human Behavior Recognition

Millimeter-wave radar enables privacy-preserving human behavior recognition, but point-cloud observations remain sparse, viewpoint-dependent and sensitive to scene changes. This paper presents a lightweight dual-radar point-cloud learning method for cross-scene behavior recognition. The two radar streams are first registered into a unified coordinate system and temporal ly fused to reduce instantaneous sparsity. A compact frame-level point encoder extracts spatial descriptors from four-dimensional radar points, and a shallow temporal Transformer models motion evolution across frames. Experiments on 4,848 paired dual-radar samples covering eight behaviors show that the proposed method achieves stable cross-scene recognition, with an Accuracy of 0. 8586 at K=5 and a Macro-F1 of 0.8538 at K=11. Robustness analysis further indicates that random perturbations cause limited degradation, whereas structured spatial masking is the dominant failure case. The lightweight baseline contains 1.44M parameters and reaches 440.0 FPS, showing a favorable trade-off between accuracy and deployment efficiency.

mmWave Radar-Based Human Motion Detection for Airports