An Efficient Sparse Sensing Based Interference Mitigation Approach For Automotive Radar
Tai Fei, Honghao Guang, Yuliang Sun, Christopher Grimm, Ernst Warsitz
Abstract
In this paper, a computationally efficient approach called block Kronecker compressed sensing (BKCS) algorithm is proposed to mitigate the mutual interference between two automotive radar systems in a 2-dimensional (2D) compressed sensing framework. Within the 2D framework, the receive signals of radar are jointly considered along both fast time and slow time dimensions, so that the signal sparsity can be better conserved than the one in 1-dimension (1D) case. Compared with the conventional Kronecker compressed sensing, BKCS requires much less resource, i.e. storage and computation power. Its performance has been verified with simulation and real measurement. The numerical assessment has shown that BKCS overcomes the shortcoming in 1D CS methods, and significantly outperforms classical signal reconstruction algorithms such as linear predictive coding as well.