Hui Zhang

Millimeter-wave radar is a vital component of advanced driver assistance systems and intelligent transportation systems, thanks to its affordability, continuous operational capability, and resilience in adverse weather conditions. Point cloud imaging is one of the most crucial techniques that enables a thorough perception of the surroundings and enhances target detection and recognition in practical scenarios. However, conventional radar point cloud imaging methods encounter performance limitations due to inadequate target reflectivity under complex electromagnetic environments. This study investigates coherent and non-coherent accumulation methods for point cloud imaging and introduces a novel neural network method, namely, 3DCFAR-Net, to overcome these limitations. The robustness and effectiveness of 3DCFAR-Net are rigorously evaluated through both numerical simulations and real-world experiments under complex electromagnetic scenarios.

With the development of frequency-modulated continuous-wave (FMCW) radar, its forward-looking imaging capability has gained prominence. However, traditional super-resolution methods based on direction-of-arrival (DOA) estimation often fail to deliver satisfactory results in complex scenarios with many targets occupying multiple bins. This is because many DOA methods process individual range bins independently, ignoring target correlations and resulting in significant information loss. Additionally, some DOA estimation methods generate outputs that do not reflect the spatial spectrum of target intensity, thereby making them unsuitable for imaging tasks. Furthermore, these methods typically involve high computational complexity, limiting their applicability to scenarios with simple processing devices. In this study, we introduce a novel low-rank and sparse recovery forward-looking imaging (LSFI) approach for FMCW radar, establishing a new forward-looking imaging model. This method formulates an optimization problem that integrates both target and background features within an overall image perspective and resolves it using the alternating direction method of multipliers (ADMM), replacing the iterative operations along the range dimension required by most methods with matrix operations and thereby significantly reducing computational complexity. Moreover, the proposed method has been successfully implemented on field programmable gate array (FPGA) platform and has demonstrated excellent super-resolution imaging performance in extensive experiments, including complex intelligent driving scenarios.

Millimeter-wave radar plays a vital role in 3D object detection for autonomous driving due to its all-weather and all-lighting-condition capabilities for perception. However, radar point clouds suffer from pronounced sparsity and unavoidable angle estimation errors. To address these limitations, incorporating a camera may partially help mitigate the shortcomings. Nevertheless, the direct fusion of radar and camera data can lead to negative or even opposite effects due to the lack of depth information in images and low-quality image features under adverse lighting conditions. Hence, in this paper, we present the radar-camera fusion network with Hybrid Generation and Synchronization (HGSFusion), designed to better fuse radar potentials and image features for 3D object detection. Specifically, we propose the Radar Hybrid Generation Module (RHGM), which fully considers the Direction-Of-Arrival (DOA) estimation errors in radar signal processing. This module generates denser radar points through different Probability Density Functions (PDFs) with the assistance of semantic information. Meanwhile, we introduce the Dual Sync Module (DSM), comprising spatial sync and modality sync, to enhance image features with radar positional information and facilitate the fusion of distinct characteristics in different modalities. Extensive experiments demonstrate the effectiveness of our approach, outperforming the state-of-the-art methods in the VoD and TJ4DRadSet datasets by 6.53% and 2.03% in RoI AP and BEV AP, respectively.

With the advancement of self-driving vehicles, autonomous driving systems depend on multimodal data to achieve a dynamic perception of the surrounding environment. Synthetic aperture radar (SAR) techniques can enhance azimuth resolution by utilizing the relative motion between the vehicle and targets, requiring a precise trajectory of the vehicle, normally without the assistance of automotive-grade navigation systems. In this case, data-driven autofocus-based algorithms are typically used to implement compensation for non-systematic motion errors. Despite demonstrating robust autofocus capabilities in numerous scenarios, their potential for application in automotive scenarios still needs to be exploited. This paper aims to provide a comprehensive automotive SAR imaging with autofocus workflow and to analyze the performance of autofocus algorithms based on phase gradient autofocus (PGA) in typical automotive scenarios. We rigorously derive the Omega-$\boldsymbol K$ algorithm based on the system-grade waveform of frequency modulated continuous wave (FMCW) signals. Based on the analysis of motion error and phase error characteristics, a sub-aperture contrast-based weighted PGA (SAC-WPGA) method, a contrast-based selection strategy (CBSS), and a contrast-based WPGA kernel are proposed to improve the robustness of autofocus for automotive scenarios. In addition, we theoretically discuss the impact of the selection strategy, the PGA kernel, and the selection threshold in detail, highlighting the validity of the proposed method. Finally, we showcase the superiority of the proposed technique by employing experimental data in two typical automotive scenarios, i.e., a simple scenario with isolated dominant points and a complex scenario with strong clutter.

This letter presents a novel substrate integrated asymmetric aperture radiating element with circularly polarized (CP) radiation. The proposed CP radiating element is constructed using only a single layer laminate via standard printed circuit board (PCB) technology, thus, it is simple in structure yet can achieve high gain and wide bandwidth. Circular polarization is realized by SIW septum constructed by metalized vias which introduce 90° phase difference and orthogonal electric fields simultaneously. A prototype of 1×4 array using the proposed CP radiating element is constructed, fabricated and measured, and the measurement results indicate that a -10 dB impedance bandwidth of 20.1% (29.6-36.2 GHz) and a 3 dB axial ratio (AR) bandwidth of 17.3% (29.6-35.2 GHz) have been achieved. Meanwhile, the left-handed CP (LHCP) realized gain is stable throughout the operating bandwidth with a peak value of 12.2 dBic. The proposed antenna can be a promising candidate for various consumer level millimeter-wave (mm-Wave) band applications due to its merits of wideband, high gain, low cost, compact size, and easy integration with front-end circuits.

High-angle resolution is commonly achieved by utilizing a wide aperture for electromagnetic systems, such as radar, navigator, sonar, and communication systems. Especially for radars, a multiple-input multiple-output (MIMO) scheme has been widely used to extend the aperture via a virtual array. Meanwhile, machine learning or metasurfaces have been widely researched to improve the resolution during the last few years. These approaches either require multiple antennas with strict arrangement or involve intricate optimizations. In this article, we propose a novel framework for an elegant dual-antenna radar system with dielectric radome, which gets inspiration from dielectric penetrating radar imaging, to realize angular super-resolution for target positioning. The dual-antenna radar model is rigorously derived, and the position or angle estimation is formulated in theory. It proves to obtain excellent performance in multitarget positioning and angular discrimination through only two independent radars that are placed with flexible spacing. We test the position estimation performance on automotive millimeter-wave radars, and the proposed method shows great advantages compared with many other super-resolution methods, revealing great potential in fields like automatic driving.

In this letter, an ultra-thin large frequency ratio dual-band antenna singly fed by a dual-mode feeding network is proposed. The dual-mode feeding network can excite even mode in the microwave band and odd mode in the millimeter-wave (mm-wave) band, respectively, through a microstrip-to-CPW transition with an asymmetrical T-junction. The integration of dual-band radiation is realized by reusing a grooved metal strip as radiators for both the microwave monopole antenna and the mm-wave spoof surface plasmon polaritons (SSPPs) antenna. Moreover, six paralleled strip directors are added ahead of the front end of the metal strip to enhance the end-fire radiation performance of the SSPPs antenna which has been originally restricted by the design of the monopole antenna. The fabricated antenna achieves wideband operation in both microwave and mm-wave bands, with impedance bandwidths (|S11|<-10 dB) of 69.2% and 42.0%, respectively. In addition, a high realized peak gain of 12.75 dBi and a large front-to-back ratio (FBR) of 25 dB can be obtained in the mm-wave band.

In this letter, an integrated microwave and millimeter-wave (mm-Wave) dual-band circularly polarized (CP) antenna using the structural reuse method is proposed. The mm-Wave CP antenna is a 2×2 array employing the sequential rotated array technique, in which the CP radiating element is constructed using two centrally symmetrical L-shaped patches that are excited via a substrate integrated coaxial line (SICL) feeding network. In terms of the microwave CP antenna, a slot-coupled square patch fed through a microstrip directional coupler incorporating stubs is developed. One microstrip stub is added to introduce a new coupling path with an opposite current, thus, forming an additional null in the upper sideband. The measured results demonstrate that the proposed CP antenna exhibits impressive impedance bandwidths of 27.91% (2.24∼2.91 GHz) in the microwave band and 23.53% (24.45∼31.04 GHz) in the mm-Wave band, respectively. The remarkable isolation between the two operating bands (over 45 dB) ensures independent operation. Moreover, the antenna demonstrates a notable out-of-band suppression level of 33 dB in the industrial scientific medical (ISM) band, indicating favorable gain filtering characteristics.