Abstract
The traditional frequency-modulated continuous wave (FMCW) multiple-input multiple-output (MIMO) radar two-dimensional (2D) super-resolution (SR) estimation algorithm for target localization has high computational complexity, which runs counter to the increasing demand for real-time radar imaging. In this paper, a fast joint direction-of-arrival (DOA) and range estimation framework for target localization is proposed; it utilizes a very deep super-resolution (VDSR) neural network (NN) framework to accelerate the imaging process while ensuring estimation accuracy. Firstly, we propose a fast low-resolution imaging algorithm based on the Nystrom method. The approximate signal subspace matrix is obtained from partial data, and low-resolution imaging is performed on a low-density grid. Then, the bicubic interpolation algorithm is used to expand the low-resolution image to the desired dimensions. Next, the deep SR network is used to obtain the high-resolution image, and the final joint DOA and range estimation is achieved based on the reconstructed image. Simulations and experiments were carried out to validate the computational efficiency and effectiveness of the proposed framework.
Highlights
Introduction and Carmine ClementeFrequency-modulated continuous wave (FMCW) has achieved great success in the field of communications and has broad prospects in applications such as altimeters [1], vehicle radars [2,3,4] and synthetic aperture radars (SARs) [5,6,7,8,9]
We propose a fast joint DOA and range estimation framework based on a very deep super-resolution (VDSR) neural network to accelerate the estimation process without precision loss
We propose a framework of fast joint DOA and range estimation via Nytrom and VDSR
Summary
Frequency-modulated continuous wave (FMCW) has achieved great success in the field of communications and has broad prospects in applications such as altimeters [1], vehicle radars [2,3,4] and synthetic aperture radars (SARs) [5,6,7,8,9]. We propose a fast joint DOA and range estimation framework based on a VDSR neural network to accelerate the estimation process without precision loss. The proposed framework splits the estimation process into two parts: In the first part, to solve the problem that the traditional 2D-MUSIC algorithm incurs a high computational cost during covariance decomposition, the Nystrom method [22] is introduced to use the covariance of partial data and obtain an approximate signal subspace. This procedure avoids the calculation of the original covariance matrix.
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