Abstract
This paper proposes a low-complexity spectral partitioning (SP) based MUSIC algorithm for automotive radar. For short-range radar (SRR), the range accuracy and resolution of 24 GHz ISM band radar should be improved because the requirements of automotive sensing become more demanding over time. To improve the performance of range estimation, high resolution based algorithms such as the estimation of signal parameters via rotational invariance techniques (ESPRIT) and multiple signal classification (MUSIC) have been proposed. However, in a low signal-to-noise ratio (SNR), the high resolution algorithm shows degraded performance to estimate the parameters. To solve this problem, SP based high resolution algorithms have been studied recently. However, for real-time operation, the conventional SP method cannot be applied to automotive radar due to its high complexity. Therefore, a low complexity SP based MUSIC is designed to maintain the performance of the range accuracy and resolution in a low SNR environment. While the complexity of the proposed algorithm is less than the SP-MUSIC, Monte-Carlo simulation results and experimental results show that the estimation performance of the proposed method is similar to that of SP-MUSIC in terms of various parameters.DOI: http://dx.doi.org/10.5755/j01.eie.23.4.18719
Highlights
Driver assistance systems are one of the major applications to create a safe and convenient automotive environment
The proposed spectral partitioning (SP)-multiple signal classification (MUSIC) with low complexity has similar range resolution performance compared to conventional MUSIC-based high-resolution algorithm, but has significantly lower processing time performance
In the low signal-tonoise ratio (SNR) environment, the performance of the proposed algorithm is improved compared to the conventional MUSIC algorithm in single target and multiple targets
Summary
Driver assistance systems are one of the major applications to create a safe and convenient automotive environment. Since automotive requirements are gradually increasing, automotive radar needs improved range resolution and maximum distance. To improve the performance of range resolution, high resolution based algorithms such as the estimation of signal parameters via rotational invariance techniques (ESPRIT) [4], multiple signal classification (MUSIC) [5], and the autoregressive method [6] have been proposed. In the case of low SNR, a high resolution algorithm that exploits spectral partitioning (SP) [7] has been developed to improve the estimation performance. In this paper, in order to satisfy the required accuracy requirements and reduce complexity compared with the conventional estimator, we propose a low-complexity SP based MUSIC algorithm, which is the most widely used algorithm, for vehicle FMCW radar
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