A Two-Layer Connected Component Algorithm for Target Extraction Using K-means and Morphology

Efficient target localization is a requirement and challenge in various applications such as autonomous vehicle systems, defence, and indoor positioning. Radars and lidars are used in such applications where the range-doppler matrix representations are common and found to be very useful. In this paper, we address the problem of target localization from a range-doppler two-dimensional (2D) matrix in latency-sensitive radar systems which support hardware acceleration. The proposed algorithms consist of three key steps, namely (a) initial background noise filtering and binarization, (b) artifact filtering, and (c) 2D target localization. We use K-means clustering and morphology to filter background noise and artifacts respectively. To implement the 2D target localization effectively, we propose a novel two-layer connected components algorithm, which reduces the computations and hence latency in target localization compared to the conventional connected components algorithm up to 64.37%. Simulation results are provided to demonstrate the performance of the algorithm in the presence of additive noise, which is compared with existing methods.