Fast Joint DOA, Range, and Velocity Estimation Method for FMCW MIMO Radar via PARAFAC

Considering the ever-increasing data size, the traditional subspace-based FMCW MIMO radar parameter estimation method is no longer suitable, due to its high computation complexity in eigenvalue decomposition and multidimensional spectrum peak search, which makes it difficult to process the data in real-time. In order to solve such problems, this paper takes TI FMCW MIMO radar with a large data size as the research object, derives and establishes a tensor domain data model, and designs a fast joint estimation method for direction-of-arrival (DOA), velocity, and range, using the compressed parallel factorization (PARAFAC) technique. Firstly, a data model conforming to the TI radar hardware is established, and its tensor domain model is obtained. Then, a compressed tensor model is obtained using Tucker3 decomposition. After that, the trilinear decomposition problem is solved by trilinear alternating least squares (TALS), following by its decompression to the original tensor dimension. Finally, the parameter estimation is achieved by phase extraction. To validate the computational efficiency and effectiveness of the proposed method, simulations and experiments were conducted.