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.

Feature-level fusion shows promise in collaborative perception (CP) through balanced performance and communication bandwidth trade-off. However, its effectiveness critically relies on input feature quality. The acquisition of high-quality features faces domain gaps from hardware diversity and deployment conditions, alongside temporal misalignment from transmission delays. These challenges degrade feature quality with cumulative effects throughout the collaborative network. In this paper, we present the Domain-And-Time Alignment (DATA) network, designed to systematically align features while maximizing their semantic representations for fusion. Specifically, we propose a Consistency-preserving Domain Alignment Module (CDAM) that reduces domain gaps through proximal-region hierarchical downsampling and observability-constrained discriminator. We further propose a Progressive Temporal Alignment Module (PTAM) to handle transmission delays via multi-scale motion modeling and two-stage compensation. Building upon the aligned features, an Instance-focused Feature Aggregation Module (IFAM) is developed to enhance semantic representations. Extensive experiments demonstrate that DATA achieves state-of-the-art performance on three typical datasets, maintaining robustness with severe communication delays and pose errors. The code will be released at https://github.com/ChengchangTian/DATA.

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.

Synthetic aperture radar (SAR) is a kind of active microwave remote sensing imaging radar, which can obtain high-resolution two-dimensional SAR images. As a multi-parameter, multi-channel SAR, polarimetric SAR (PolSAR) provides rich scattering information for topographic mapping, ocean exploration, polar observation, target identification, and many other fields. Compared to single-polarization SAR, multi-polarization SAR greatly improves the potential information of the data by extending the one-dimensional information. However, the above tasks cannot be carried out without clean SAR echo signal. The radio frequency interference (RFI) signals, seriously affect the subsequent tasks of PolSAR, and there is a great deal of potential information between polarized data. Therefore, this paper proposes a framework for combining multiple polarization data to improve low-rank based methods’ performance. Based on the proposed framework, one experiment is conducted on real PolSAR data, the experiment uses the PCA method to verify the applicability of the proposed framework in interference suppression. At last, the result verifies the framework achieves better suppression of low-rank based method.

In the community of automotive millimeter wave radar, the recently developed concept of four-dimensional (4D) radar can provide high-resolution point clouds image with enhanced imaging performance. Currently, the density of point clouds for single-frame image is usually too sparse to satisfy the demands of target classification and recognition due to the limitation of Doppler and angle resolutions. To address the aforementioned issues, a novel algorithm is proposed for 4D high-resolution imagery generation of point clouds with extremely high Doppler and angle resolutions in this paper. For high Doppler resolution with high-dynamic, a novel velocity ambiguity resolution algorithm is proposed using a dual pulse repetition frequency (dual-PRF) waveform design embedded in an innovative time-division multiplexing & Doppler-division multiplexing MIMO (TDM-DDM-MIMO) framework. Meanwhile, an attractive complex-valued deep convolutional network (CV-DCN) of super-resolution direction-of-arrival (DOA) estimation is proposed only using single-frame data. To be specific, a spatial smoothing operator on array data is applied as input of the network, and a CV-DCN is designed to learn the transformation of the spatial spectrum from the end-to-end to effectively protect the spectrum extraction. Furthermore, experimental analysis is performed to confirm the effectiveness of the proposed super-resolution DOA estimation algorithm. Finally, the 4D high-resolution imagery of point clouds is obtained by experiments in the parking lot.

With the surge of vehicles and transportation, sensing obstacles and warning drivers to avoid accidents have become a great concern in recent years. In the current roadworthy electromagnetic environment, the number of frequency modulated continuous wave (FMCW) millimeter-wave (MMW) automotive radars has exploded due to their unique advantages in environmental sensing. However, the frequency band of the automotive radars is limited from 77 to 81 GHz, hence the burgeoning of radars on the road is bound to cause mutual interference and jeopardize further target detection and parameter estimation. In this paper, two basic schemes are considered to mitigate mutual interference of automotive radars. First, we consider the sparse characteristics of the mutual interference in the two-dimensional (2-D) time domain and employ a sparse interference extraction (SIE) method to tackle the mutual interference. Next, we further consider the low-rank property of the useful echoes across multiple channels and propose a novel three-dimensional (3-D) tensor decomposition (TD) method to decompose the received signals into mutual interference and useful echoes. Several numerical simulations are fulfilled to test the robustness of the proposed TD method, especially for multiple input and multiple output (MIMO) systems under complex electromagnetic circumstances. Furthermore, more experiments are implemented to demonstrate its feasibility in practical applications in comparison to multiple state-of-the-art methods.

As a wideband radar system, the synthetic aperture radar (SAR) usually conflicts with several electromagnetic systems, such as frequency modulation (FM), TV, and other communication systems. These signals, which are radio frequency interference (RFI) for radar systems, severely interfere with SAR systems to generate a high-resolution image. Some previous parametric methods focused on the time-varying RFI model; however, they cannot realize the comparable effectiveness and efficiency against semi-parametric methods. However, previous semiparametric methods did not focus on the time-varying RFI case. Hence, in this letter, a graph Laplacian clustering (GLC) semiparametric algorithm is proposed to suppress RFIs by constructing the Laplacian embedding connections between different pulses of signals. As a result, locally time-varying interferences are clustered in a nonlinear low-dimensional manifold and can be effectively mitigated. The real SAR data with measured RFIs are provided to demonstrate the effectiveness and efficiency of the proposed algorithm.