The beampattern of a conventional frequency diverse array (FDA) with linear frequency pattern (i.e. the centre frequency of each antenna element is linearly increasing) is coupled in range and angle. FDA with random frequency pattern can decouple the range and angle with high probability only when the number of elements is sufficiently large. The ambiguity of frequency patterns that are random permutations of a linear FDA is examined. An eigenvector‐based criterion to identify possible ambiguity in target localisation using such arrays is proposed. Numerous results are presented to verify the effectiveness of the proposed ambiguity identification criterion.

In this letter, a 3-D synthetic aperture radar with a frequency diverse array (3-D-FDA-SAR) is proposed for 3-D imaging. By applying an FDA to the 3-D-SAR system, the proposed 3-D-FDA-SAR can achieve real 3-D imaging using a simple hardware structure and a simple imaging algorithm. Specifically, the bandwidth needed for the range resolution is dispersed in space by the FDA, and the spatial resources (large aperture) needed for the along-track resolution are replaced by time resources. Therefore, the transmission and processing of wideband signals can be avoided, so the imaging algorithm only comprises a phase compensation step based on the back projection (BP) imaging algorithm. A resolution analysis of the range, along-track and across-track is presented to illustrate the rationality of the bandwidth dispersion and resource replacement. The simulation and experimental results verify the effectiveness of the proposed strategy.

When it comes to road environment perception, millimeter-wave radar with a camera facilitates more reliable detection than a single sensor. However, the limited utilization of radar features and insufficient extraction of important features remain pertinent issues, especially with regard to the detection of small and occluded objects. To address these concerns, we propose a camera-radar fusion with radar channel extension and a dual-CBAM-FPN (CRFRD), which incorporates a radar channel extension (RCE) module and a dual-CBAM-FPN (DCF) module into the camera-radar fusion net (CRF-Net). In the RCE module, we design an azimuth-weighted RCS parameter and extend three radar channels, which leverage the secondary redundant information to achieve richer feature representation. In the DCF module, we present the dual-CBAM-FPN, which enables the model to focus on important features by inserting CBAM at the input and the fusion process of FPN simultaneously. Comparative experiments conducted on the NuScenes dataset and real data demonstrate the superior performance of the CRFRD compared to CRF-Net, as its weighted mean average precision (wmAP) increases from 43.89% to 45.03%. Furthermore, ablation studies verify the indispensability of the RCE and DCF modules and the effectiveness of azimuth-weighted RCS.

Roadside perception systems, also known as roadside units (RSUs), are critical in Vehicle-to-Everything (V2X) applications, yet spatio-temporal asynchrony between multiple sensors severely compromises the accuracy of fusion. In this paper, a spatio-temporal synchronization method for millimeter-wave (MMW) radar and camera fusion is proposed, integrating target matching based on dynamic time warping (DTW) with spatio-temporal parameter estimation. Leveraging the advantages of DTW in time-series alignment to calculate the similarity between radar and visual trajectories enables target matching and parameter estimation in sparse scenes. This method was validated on a real-world dataset containing over 30 pedestrian trajectories, covering scenarios with varying densities ranging from one to six pedestrians. The results indicate a temporal offset of 0.116 s between the camera and radar. Following synchronization, the average spatial deviation decreased from 1.4358 to 0.1074 m in the x-direction (i.e., across the road) and from 3.0732 to 0.1775 m in the y-direction (i.e., along the road). Consequently, this method provides an efficient solution for deploying roadside perception systems in sparse traffic environments.