An amplitude weighted cross-correlation detector is proposed for the detection of the Doppler-spread target (DST), based on the frequency modulated continuous waveform (FMCW) radar. The concurrent range-Doppler matrices (RDM) make cross-correlation optimized with higher accumulation gain and significantly reduced computation effort. A novel measurement of signal sparseness is defined. The quantitative analysis of the proposed detector, compared with the energy integration detector, is realized based on the signal sparseness. Two generalizations of proposed detector are discussed for the number of antennas greater than two. Data from a walking pedestrian are got and used to verify the effectiveness of the proposed detector.

Multi-modality three-dimensional (3D) object detection is a crucial technology for the safe and effective operation of environment perception systems in autonomous driving. In this study, we propose a method called context clustering-based radar and camera fusion for 3D object detection (ConCs-Fusion) that combines radar and camera sensors at the intermediate fusion level to achieve 3D object detection. We extract features from heterogeneous sensors and input them as feature point sets into the fusion module. Within the fusion module, we utilize context cluster blocks to learn multi-scale features of radar point clouds and images, followed by upsampling and fusion of the feature maps. Then, we leverage a multi-layer perceptron to nonlinearly represent the fused features, reducing the feature dimensionality to improve model inference speed. Within the context cluster block, we aggregate feature points of the same object from different sensors into one cluster based on their similarity. All feature points within the same cluster are then fused into a radar–camera feature fusion point, which is self-adaptively reassigned to the originally extracted feature points from a simplex sensor. Compared to previous methods that only utilize radar as an auxiliary sensor to camera, or vice versa, the ConCs-Fusion method achieves a bidirectional cross-modal fusion between radar and camera. Finally, our extensive experiments on the nuScenes dataset demonstrate that ConCs-Fusion outperforms other methods in terms of 3D object detection performance.

Range-spread target (RST) detection is an important issue for high-resolution radar (HRR). Traditional detectors relying on manually designed detection statistics have their performance limitations. Therefore, in this work, two deep learning-based detectors are proposed for RST detection using HRRPs, i.e., an NLS detector and DFCW detector. The NLS detector leverages domain knowledge from the traditional detector, treating the input HRRP as a low-level feature vector for target detection. An interpretable NLS module is designed to perform noise reduction for the input HRRP. The DFCW detector takes advantage of the extracted high-level feature map of the input HRRP to improve detection performance. It incorporates a feature cross-weighting module for element-wise feature weighting within the feature map, considering the channel and spatial information jointly. Additionally, a nonlinear accumulation module is proposed to replace the conventional noncoherent accumulation operation in the double-HRRP detection scenario. Considering the influence of the target spread characteristic on detector performance, signal sparseness is introduced as a measure and used to assist in generating two datasets, i.e., a simulated dataset and measured dataset incorporating real target echoes. Experiments based on the two datasets are conducted to confirm the contribution of the designed modules to detector performance. The effectiveness of the two proposed detectors is verified through performance comparison with traditional and deep learning-based detectors.

3D object detection is one of the core technologies for autonomous driving. Relying on a single type of sensor is inadequate for effectively perceiving the driving environment, and multi-sensor fusion solutions mitigate this limitation. This article presents a novel approach for early and middle multimodal fusion, referred to as Bi-RC Fusion, that not only maximizes the utilization of raw data from heterogeneous sensors in the early fusion, but also facilitates bi-directional assistance between radar and camera through our module in the middle fusion stage, which consists of two parallel sets of multi-head cross-attention. In the final fusion stage, we have developed an adaptive feature fusion module to enhance the efficiency of cross-modal integration. Additionally, we have designed an efficient multi-scale feature fusion module to learn the fused feature representations across different scales. We have conducted extensive experiments on the nuScenes dataset, including robustness experiments particularly designed for challenging weather conditions. The experimental results demonstrate that our proposed method significantly enhances the performance of 3D object detection, particularly regarding metrics related to velocity and attribute errors.