Abstract
A novel sparse automotive multiple-input multiple-output (MIMO) radar configuration is proposed for low-complexity super-resolution single snapshot direction-of-arrival (DOA) estimation. The physical antenna effects are incorporated in the signal model via open-circuited embedded-element patterns (EEPs) and coupling matrices. The transmit (TX) and receive (RX) array are each divided into two uniform sparse sub-arrays with different inter-element spacings to generate two MIMO sets. Since the corresponding virtual arrays (VAs) of both MIMO sets are uniform, the well-known spatial smoothing (SS) algorithm is applied to suppress the temporal correlation among sources. Afterwards, the co-prime array principle between two spatially smoothed VAs is deployed to avoid DOA ambiguities. A performance comparison between the sparse and conventional MIMO radars with the same number of TX and RX channels confirms a spatial resolution enhancement. Meanwhile, the DOA estimation error due to the mutual coupling (MC) is less pronounced in the proposed sparse architecture since antennas in both TX and RX arrays are spaced larger than half wavelength apart.
Highlights
A UTOMOTIVE radars are becoming indispensable parts of autonomous vehicles and advanced driver assistant systems (ADASs)
Lower root mean square error (RMSE) values by the sparse multiple-input multiple-output (MIMO) radar confirms that it achieves a better angular resolution in comparison with the conventional MIMO radar
In order to examine the performance of the proposed MIMO radar in case of multiple simultaneous sources, even more than two, a Monte Carlo simulation is conducted for a fixed SNR value and constant angular separations among sources
Summary
A UTOMOTIVE radars are becoming indispensable parts of autonomous vehicles and advanced driver assistant systems (ADASs). A novel MIMO radar configuration with nonuniform sparse TX and RX arrays is proposed for superresolution single snapshot DoA estimation in the presence of MC. The TX and RX antennas are divided into two sub-arrays to generate two sets of MIMO radars, whose resultant VAs are sparse but uniform. Both VAs suffer from aliasing due to spatial undersampling, a spatial smoothing (SS) algorithm [16] can be applied separately on each VA for correlation suppression of coherent sources. To the authors’ best knowledge, this is the first time to propose a non-uniform sparse MIMO radar for single snapshot DoA estimation for which traditional FFT and sub-space based algorithms can be applied. (b) FIGURE 2: Sparse MIMO radar configuration (a) with nonuniform TX and RX arrays; (b) two MIMO sets and signal processing steps for an enhanced DoA estimation
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