A low-complexity joint TOAs and AOAs parameter estimator using dimension reduction for FMCW radar systems

We propose a low-complexity of joint time delay of arrivals (TOAs) and angles of arrivals (AOAs) using dimension reduction for frequency modulated continuous-wave (FMCW) radar systems. In the road environment, the FMCW radar can obtain the position of other vehicles accurately since the radar system is able to estimate spatial-temporal parameters such as TOAs and AOAs of multiple targets. The requirement of FMCW radar is increasing in terms of the parameter accuracy and resolution over time. In order to accomplish high accuracy and resolution of the parameters such as range, velocity, and angle, conventional two dimensional- discrete Fourier transform (2D-DFT) have reduced performance of the parameter estimation. To enhance the estimation performance of parameter, proposed high resolution based methods, which are two dimensional-multiple signal classification (2D-MUSIC), is utilized. However, conventional high-resolution methods are not used in real-time systems due to their high complexity situation. In order to meet resolution ability and automotive radar system requirements, we present a joint TOAs and AOAs parameter estimator using dimension reduction with combination of the DFT and the MUSIC algorithm for low-complexity FMCW radar systems. In experimental results of multi-target environments, in terms of the estimation performance, the proposed method is better than 2D-MUSIC.