Tensor Train Decomposition Based Noise Reduction and Enhanced Parameter Estimation for FMCW MIMO Radar Systems

Abstract

Frequency modulated continuous wave (FMCW) radar is widely used in autonomous driving and industrial inspection due to its high-resolution target location and velocity estimation capability. However, the plethora of connected devices in automotive applications introduces electromagnetic interference and brings challenges to location-aware services, primarily due to the issue of low signal-to-noise ratio (SNR) caused by mixed noise contamination. Conventional matrix-based signal processing methods exhibit performance deterioration when handling higher-order signals under low SNR conditions. To address this challenge, this paper proposes a tensor decomposition-based framework that jointly performs noise reduction and parameter estimation for four-dimensional signals in FMCW multiple-input multiple-output (MIMO) radar systems. Specifically, the framework exploits the inherent low-rank structure and multidimensional correlations of the received signals through tensor train decomposition to effectively separate noise subspace. A data smoothing processor then reconstructs an augmented signal tensor to resolve rank deficiency caused by coherent signals. Finally, an enhanced rotational subspace algorithm is employed to jointly decouple the distance, velocity, and angle parameters by exploiting the structural fitting to the restored signal. Both simulation and field experiments under real-world noise demonstrate that our proposed framework achieves significant noise reduction while improving target SNR and parameter estimation accuracy. These advancements make the proposed framework a robust solution for high-precision MIMO FMCW radar applications in dynamic, noise-polluted environments.