Design of a linear non-uniform antenna array for a 77-GHz MIMO FMCW radar

Schmid, Christian M.; Feger, Reinhard; Wagner, Christoph; Stelzer, Andreas

doi:10.1109/imws2.2009.5307896

Cited by 26 publications

(14 citation statements)

References 5 publications

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“…[55]. The first FMCW MIMO radar transceiver operating at 77 GHz has been implemented in SiGe technology in 2008 [56], whereas the production of the first MIMO FMCW radar, operating according to a TDM strategy and equipped with an array of colocated antennas, started in 2009 [57], [58]. As far as we know, the last device represents the first compact MIMO radar system based on a MMIC in SiGe, operating at 77 GHz and radiating ultra-wideband signals.…”

Section: B a Brief History Of The Colocated Mimo Radar Technologymentioning

confidence: 99%

Machine Learning and Deep Learning Techniques for Colocated MIMO Radars: A Tutorial Overview

DAVOLI¹,

Guerzoni²,

Vitetta³

2021

Preprint

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<p>Radars are expected to become the main sensors in various civilian applications, ranging from health-care monitoring to autonomous driving. Their success is mainly due to the availability of both low cost integrated devices, equipped with compact antenna arrays, and computationally efficient signal processing techniques. An increasingly important role in the field of radar signal processing is played by machine learning and deep learning techniques. Their use has been first taken into consideration in human gesture and motion recognition, and in various healthcare applications. More recently, their exploitation in object detection and localization has been also investigated. The research work accomplished in these areas has raised various technical problems that need to be carefully addressed before adopting the above mentioned techniques in real world radar systems. In this manuscript, a comprehensive overview of the machine learning and deep learning techniques currently being considered for their use in radar systems is provided. Moreover, some relevant open problems and current trends in this research area are analysed. Finally, various numerical results, based on both synthetically generated and experimental datasets, and referring to two different applications are illustrated. These allow readers to assess the efficacy of specific methods and to compare them in terms of accuracy and computational effort.</p>

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Section: B a Brief History Of The Colocated Mimo Radar Technologymentioning

confidence: 99%

Machine Learning and Deep Learning Techniques for Colocated MIMO Radars: A Tutorial Overview

DAVOLI¹,

Guerzoni²,

Vitetta³

2021

Preprint

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show abstract

“…and the joint density needed for evaluation of the WWB can be obtained as p(y, θ) = p(y|θ)p θ (θ). Performing the calculations demanded by (6) in an analogue fashion to the treatment of the conditional model in [21], we obtain, 8…”

Section: Construction Of the Wwb With Random Initial Phasementioning

confidence: 99%

“…9 The authors are aware that this choice of test-point matrix conflicts with the condition M ≥ q suggested in [15]. This condition is unnecessary for the derivation of the covariance inequality, and is only a necessary condition for a maximum-rank bound in (6). We make the choice of rank-1 bound for the sake of computational efficiency and because it seems satisfactory for the purpose of having a tight lower bound in (10).…”

Section: Construction Of the Wwb With Random Initial Phasementioning

confidence: 99%

Constrained optimal design of automotive radar arrays using the Weiss-Weinstein Bound

González-Huici

Mateos-Nunez

Greiff³

et al. 2018

2018 IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM)

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We propose a design strategy for optimizing antenna positions in linear arrays for far-field Direction of Arrival (DoA) estimation of narrow-band sources in collocated MIMO radar. Our methodology allows to consider any spatial constraints and number of antennas, using as optimization function the Weiss-Weinstein bound formulated for an observation model with random target phase and known SNR, over a pre-determined Field-of-View (FoV). Optimized arrays are calculated for the typical case of a 77GHz MIMO radar of 3Tx and 4Rx channels. Simulations demonstrate a performance improvement of the proposed arrays compared to the corresponding uniform and minimum redundancy arrays for a wide regime of SNR values.

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“…As such, high-resolution target direction-of-arrival (DOA) estimation requires a high number of antennas which are often infeasible for automotive radars due to the strict cost constraints. A cost-effective solution is to use sparse MIMO arrays [13]- [15]. Consider for simplicity a MIMO radar exploiting a sparse linear array (SLA) which is formed from a uniform linear array (ULA) with halfwavelength interelement spacing by selecting a subset of ULA antennas but maintaining the same aperture [13].…”

Section: Introductionmentioning

confidence: 99%

Four-Dimensional High-Resolution Automotive Radar Imaging Exploiting Joint Sparse-Frequency and Sparse-Array Design

Sun

Zhang

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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We propose a novel automotive radar imaging technique to provide high-resolution information in four dimensions, i.e., range, Doppler, azimuth, and elevation, by exploiting a joint sparsity design in frequency spectrum and array configurations. Random sparse stepfrequency waveform is proposed to synthesize a large effective bandwidth and achieve high range resolution profiles. This concept is extended to multi-input multi-output (MIMO) radar by applying phase codes along the slow time to synthesize a two-dimensional (2D) sparse array with a high number of virtual array elements which enable high-resolution direction finding in both azimuth and elevation. The 2D sparse array acts as a sub-Nyquist sampler of the corresponding uniform rectangular array (URA), and the corresponding URA response is recovered by completing a lowrank block Hankel matrix. The proposed imaging radar provides point clouds with a resolution comparable to light detection and ranging (LiDAR) but with a much lower cost and is insensitive to weather conditions.

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Design of a linear non-uniform antenna array for a 77-GHz MIMO FMCW radar

Cited by 26 publications

References 5 publications

Machine Learning and Deep Learning Techniques for Colocated MIMO Radars: A Tutorial Overview

Machine Learning and Deep Learning Techniques for Colocated MIMO Radars: A Tutorial Overview

Constrained optimal design of automotive radar arrays using the Weiss-Weinstein Bound

Four-Dimensional High-Resolution Automotive Radar Imaging Exploiting Joint Sparse-Frequency and Sparse-Array Design

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