Circular Convolutional Learned ISTA for Automotive Radar DOA Estimation

Radar is imperative for many automotive applications in detecting targets. Accurate direction of arrival (DOA) estimation is essential for maximizing the reliability of radar by improving the angular resolution. And a lightweight algorithm with a small memory footprint is desired considering that limited computational resources are accessible for automotive radar. Conventionally, iterative algorithms such as iterative shrinkage thresholding algorithm (ISTA) were used for DOA estimation. However, algorithms like ISTA can require many iterations to converge, and a lot of manual parameter tuning is required to obtain optimal performance. Learned ISTA (LISTA) has been used to approximate ISTA with fewer iterations without the necessity of manual tuning by unfolding the iterative algorithm as a neural network. But directly using LISTA is not suitable for DOA estimation due to the large size of the matrices that need to be learned. The large number of learning parameters require a lot of training data, a long training time, and heavy computation. This work proposes to use circular convolutions to reduce the number of learning parameters in the model as well as computation. We show that the circular convolution-based ISTA has better performance metrics than the traditional ISTA.