Effort Considerations of Compressed Sensing for Automotive Radar

“…This leads to a high artefact suppression and to a high signal-to-noise ratio. The required iteration number depends on the amount of missing data as shown in [15]. Even with a reduced iteration number a sufficient reconstruction is feasible, as further iterations only lead to minor improvements.…”

Section: Compressed Sensing-based Reconstructionmentioning

confidence: 99%

Compressed Sensing based Single Snapshot DoA Estimation for Sparse MIMO Radar Arrays

Roos¹,

Hügler²,

Torres³

et al. 2019

2019 12th German Microwave Conference (GeMiC)

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The angular resolution of a radar system can be enhanced with an increasing antenna aperture. Instead of using more antenna elements, the distances in the aperture can be increased with a sparse array. To mitigate the high side lobes originating from the sparse array, the missing antenna elements can be reconstructed by means of compressed sensing. In this paper a sparse antenna array with a low side lobe level is determined with a genetic algorithm and a cost function. An investigation is performed what difference in the radar cross section of two targets in the same range-Doppler cell can be achieved. Additionally, instead of considering point targets only, a target vehicle is measured with a 77 GHz MIMO radar.

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“…Sparse sensor arrays equipped with sparse signal recovery algorithms, which deploys iterative [10] or VOLUME 4, 2016 probabilistic [11] approaches, are proposed to achieve a fine angular resolution using a single snapshot. Nonetheless, these methods are considered computationally heavy for realtime processing [12], [13]. Hence, a novel MIMO radar configuration for super-resolution single snapshot DoA estimation compatible with low complexity algorithms is highly desirable for the automotive industry.…”

Section: Introductionmentioning

confidence: 99%

Sparse Automotive MIMO Radar for Super-Resolution Single Snapshot DOA Estimation With Mutual Coupling

Amani¹,

Jansen

Filippi

et al. 2021

IEEE Access

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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.

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Effort Considerations of Compressed Sensing for Automotive Radar

Cited by 6 publications

References 9 publications

Automotive Radar — From First Efforts to Future Systems

Automotive Radar — From First Efforts to Future Systems

Compressed Sensing based Single Snapshot DoA Estimation for Sparse MIMO Radar Arrays

Sparse Automotive MIMO Radar for Super-Resolution Single Snapshot DOA Estimation With Mutual Coupling

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