STNet: A Space-Time Network Solution for Gridless DOA Estimation With Small Snapshots for Automotive Radar System

In order to play the key role of automotive millimeter wave radar in intelligent vehicle systems, direction-of-arrival (DOA) estimation is an essential problem to be solved. For practical intelligent driving applications, DOA estimation requires both real-time performance and high accuracy. Due to unique advantages, deep learning (DL) based methods have attracted more attention. Most of the existing DL-based methods require a large number of snapshots, but only a few snapshots can be guaranteed in practical applications. Moreover, they usually model DOA estimation as a multi-label classification task. The output represents the position of signal DOA on the discrete grid, and the resolution will be limited by the grid. In this paper, a new space-time Network (STNet) is proposed, which models DOA estimation as a regression task to achieve the effect of gridless estimation. We design a space correlation extraction module (SCEM) and a time correlation extraction module (TCEM), using the covariance matrix of the received signal and the original received signal as inputs respectively, treat them as different types of data. In these two modules, skip connection dense blocks (SCDBs) and long short-term memory (LSTM) networks are adopted to process two different forms of data. Through such processing, we retain sufficient information, obtain more features for the regression task, and ensure the estimation effect of using a small number of snapshots. The experimental results indicate that the STNet shows obvious performance gain in the case of small snapshots, achieves gridless estimation effect, and demonstrates excellent adaptability in situations where target DOAs are closely positioned.