MIMO adaptive beamforming for nonseparable multipath clutter mitigation

Yu, Jason; Krolik, Jeffrey L.

doi:10.1109/taes.2014.130451

Cited by 16 publications

(8 citation statements)

References 34 publications

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“…For localization application, we incorporate the range observation. In most space time processing applications, the range observation is assumed to be statistically independent with the space-time observations [12,13]. Therefore, the overall information matrix is given by…”

Section: Crlb On Target Localizationmentioning

confidence: 99%

CRLB performance for bistatic MIMO radar

Krolik

2015

2015 IEEE Radar Conference (RadarCon)

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This paper derives the Cramer-Rao Lower Bound (CRLB) on angular/Doppler frequency estimation and target localization using a bistatic multi-input multi-output (MIMO) radar system. The bistatic radar configuration provides flexible design on receiver and transmitter radar platforms, which allows enhanced mobility and reduced maintenance cost compared to the monostatic counterpart. The CRLB predicts the maximum achievable performance on angular/Doppler frequency estimation and target localization in terms of radar system parameters and the prior distribution of the target velocity, offering fundamental insights on designing practical bistatic MIMO radar system. A simulation based use case is exemplified for the proposed CRLB analysis.

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Section: Crlb On Target Localizationmentioning

confidence: 99%

CRLB performance for bistatic MIMO radar

Krolik

2015

2015 IEEE Radar Conference (RadarCon)

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“…The majority of these scenes are characterized by multipath propagation conditions in both azimuth and elevation [20,21]. Therefore, automotive radars are required to operate also in such multipath-dominated conditions [22][23][24][25] reliably.…”

Section: Introductionmentioning

confidence: 99%

Misspecified Cramér-Rao Bound for Multipath Model in MIMO Radar

Moshe

Bilik

Tabrikian

2022

2022 IEEE 12th Sensor Array and Multichannel Signal Processing Workshop (SAM)

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Autonomous driving and advanced active safety features require accurate high-resolution sensing capabilities.Automotive radars are the key component of the vehicle sensing suit. However, when these radars operate in proximity to flat surfaces, such as roads and guardrails, they experience a multipath phenomenon that can degrade the accuracy of the direction-of-arrival (DOA) estimation. Presence of multipath leads to misspecification in the radar data model, resulting in estimation performance degradation, which cannot be reliably predicted by conventional performance bounds. In this paper, the misspecified Cramér-Rao bound (MCRB), which accounts for model misspecification, is derived for the problem of DOA estimation in the presence of multipath which is ignored by the estimator. Analytical relations between the MCRB and the Cramér-Rao bound are established, and the DOA estimation performance degradation due to multipath is investigated. The results show that the MCRB reliably predicts the asymptotic performance of the misspecified maximum-likelihood estimator and therefore, can serve as an efficient tool for automotive radar performance evaluation and system design.

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“…Unluckily, in a number of recent applications, MIMO radars operate in extremely complex, highly dynamic and time varying scenarios, affected by multipath propagation, clutter and interference, and in the presence of extended targets. In such conditions, deterministic algorithms may fail, since they are unable to achieve acceptable estimation accuracy and are prone to generate ghost targets [11]. When this occurs, machine learning (ML) and deep learning (DL) techniques represent an appealing alternative or the only viable technical solution.…”

Section: Introductionmentioning

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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MIMO adaptive beamforming for nonseparable multipath clutter mitigation

Cited by 16 publications

References 34 publications

CRLB performance for bistatic MIMO radar

CRLB performance for bistatic MIMO radar

Misspecified Cramér-Rao Bound for Multipath Model in MIMO Radar

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

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