How to accurately acquire and track the positions of multiple vehicles in a traffic system is a challenging problem. Especially in the clustering detection problem, the uncertainty of vehicle states (including number, spatial location, etc.) in traffic scenarios leads to the fact that the predefined single clustering condition cannot match the variable clustering scenarios, which causes serious distortion of clustering results. As a result, this paper proposes a scene-adaptive multi-target clustering algorithm for millimetre-wave radar point cloud data, where spatial dimension is proposed to be clustered with density eigenvalues, and speed dimension fast clustering is carried out in a single frame based on sorted difference, and then fusion correction is made to the results of the two clusters by using the simulated spindle to solve the difficulties in obtaining the multi-target adaptive clustering conditions of the road as well as the problem of over-segmentation of the clusters of some large targets. The algorithm firstly, calculates the adaptive threshold of the original radar data set. Then, a density-based spatial clustering algorithm (DBSCAN) is used to perform primary clustering and outlier removal on the radar data, and secondary clustering in the velocity dimension. Finally, the spindle fusion clustering algorithm is applied to fuse the two clustering results. The proposed algorithm is experimentally validated using data collected from a 77GHz TI AWR1843 radar platform. The experimental results show that the improved algorithm improves the clustering accuracy by about 17.48% compared to the conventional DBSCAN algorithm in traffic multi-target scenarios.
The mapping of the radar echo dataset into a graph signal offers a novel perspective for solving the radar target localization problem. However, the published graph-based methods are mostly applicable to the uniform array configuration. In this paper, we propose an enhanced graph-based target localization method that can be applicable to the non-uniform frequency diversity array radar to fill this gap. Following the previous studies, we establish a space-domain graph model for the echo signal acquired from a non-uniform frequency diversity array radar. Subsequently, we employ the graph signal processing method to solve the target localization problem. Numerical simulations demonstrated that the proposed graph-based localization method provides a high resolution and accurate estimation, surpassing conventional methods.
Frequency diversity array (FDA) radar can automatically scan an area of interest without phase shifters by utilizing its range-angle-dependent beampattern, which is more convenient in terms of system implementation relative to the conventional phased array (PA) radar. However, the FDA cannot track a target continuously as the PA does because of the periodicity characteristic shown in the FDA’s beampattern. We address the problem of stable tracking of multiple moving targets, aiming at pursuing a high-resolution target imaging approach. First, we establish an inverse synthetic aperture radar (ISAR) imaging model applicable to multiple repeated subpulses based FDA ISAR (MRS-FDA-ISAR) radar. In the procedure of moving target imaging, the proposed MRS-FDA-ISAR scheme is able to not only avoid the problem of range-angle-coupling but also achieve high-level energy accumulation by compensating the phase of the target. Finally, the back projection algorithm is utilized to achieve high-resolution two-dimensional imaging of multiple moving targets. Numerical experiments demonstrated the effectiveness of the proposed approach and it is shown that this approach is superior to conventional ISAR imaging methods due to its high-level energy utilization and relatively low hardware overhead.
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