Intelligent Reflecting Surface-Enhanced Target Detection in MIMO Radar

Lu, Wei; Deng, Bin; Fang, Qiqing; Wen, Xiaoqiao; Peng, Shixin

doi:10.1109/lsens.2021.3052753

Cited by 53 publications

(20 citation statements)

References 10 publications

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“…The authors in [20] utilized an RIS to promote the received signal-to-noise ratio (SNR) for enhanced detection capabilities of a singleantenna radar. The MIMO radar case was considered in [21] and [22], where the phase shifts of the RIS were optimized to improve the radar performance. However, the aforementioned schemes all consider a single target scenario, which cannot be directly applied to the multi-target scenario.…”

Section: Separation Between Adjacent Antennas D Umentioning

confidence: 99%

MetaRadar: Multi-target Detection for Reconfigurable Intelligent Surface Aided Radar Systems

Zhang¹,

Zhang²,

Di³

et al. 2022

Preprint

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As a widely used localization and sensing technique, radars will play an important role in future wireless networks. However, the wireless channels between the radar and the targets are passively adopted by traditional radars, which limits the performance of target detection. To address this issue, we propose to use the reconfigurable intelligent surface (RIS) to improve the detection accuracy of radar systems due to its capability to customize channel conditions by adjusting its phase shifts, which is referred to as MetaRadar. In such a system, it is challenging to jointly optimize both radar waveforms and RIS phase shifts in order to improve the multi-target detection performance. To tackle this challenge, we design a waveform and phase shift optimization (WPSO) algorithm to effectively solve the multitarget detection problem, and also analyze the performance of the proposed MetaRadar scheme theoretically. Simulation results show that the detection performance of the MetaRadar scheme is significantly better than that of the traditional radar schemes.

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Section: Separation Between Adjacent Antennas D Umentioning

confidence: 99%

MetaRadar: Multi-target Detection for Reconfigurable Intelligent Surface Aided Radar Systems

Zhang¹,

Zhang²,

Di³

et al. 2022

Preprint

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“…[8], at the UE side [12], or as a separate reflector [13], [14]), wireless settings (multiple-input multiple-output (MIMO) [14], [15], multiple-input single-output (MISO) [10], and singleinput single-output (SISO) [9], [11]), etc. Furthermore, RISs can be used to assist the radar systems to improve the target detection capabilities [16], [17]. In [7], Cramér-Rao lower bounds (CRB) on the localization error were established for the near-field of a continuous RIS and the effects of the RIS size were studied.…”

Section: Introductionmentioning

confidence: 99%

RIS-Enabled Self-Localization: Leveraging Controllable Reflections With Zero Access Points

Keykhosravi¹,

Seco‐Granados²,

Alexandropoulos³

et al. 2022

Preprint

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Reconfigurable intelligent surfaces (RISs) are one of the most promising technological enablers of the next (6th) generation of wireless systems. In this paper, we introduce a novel use-case of the RIS technology in radio localization, which is enabling the user to estimate its own position via transmitting orthogonal frequency-division multiplexing (OFDM) pilots and processing the signal reflected from the RIS. We demonstrate that user localization in this scenario is possible by deriving Cramér-Rao lower bounds on the positioning error and devising a lowcomplexity position estimation algorithm. We consider random and directional RIS phase profiles and apply a specific temporal coding to them, such that the reflected signal from the RIS can be separated from the uncontrolled multipath. Finally, we assess the performance of our position estimator for an example system, and show that the proposed algorithm can attain the derived bound at high signal-to-noise ratio values.

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“…Performance of transmitter localization done using sector power measurements for every senso then derived Cramer Rao Bound (CRB) [9] , [10] for sector power estimation using DOA and mean square error is derived for analytical expression. Main objective of this paper, estimating DOA of various sensors for sensing [11], [12] the vacant spectrum and thereby facilitate channel allocation to secondary user. Here, we make use of an…”

Section: Introductionmentioning

confidence: 99%

An adaptive learning algorithm for spectrum sensing based on direction of arrival estimation in cognitive radio systems

2021

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<p class="Abstract">In cognitive radio systems, estimating primary user direction of arrival (DOA) measurement is one of the key issues. In order to increase the probability detection multiple sensor antennas are used and they are analysed by using subspace-based technique. In this work, we considered wideband spectrum with sub channels and here each sub channel facilitated with a sensor for the estimation of DOA. In practical spectrum sensing process interference component also encounters in the sensing process. To avoid this interference level at output of receiver, we used an adaptive learning algorithm known as Normalised Least Absolute Mean Deviation (NLAMD) algorithm. Further to achieve better performance a bias compensator function is applied in weight coefficient updating process. Using this hybrid realization, the vacant spectrum can be sensed based on DOA estimation and number of vacant locations in each channel can be identified using maximum likelihood approach. In order to test at the diversified conditions different threshold parameters 0.1, 0.5, 1 are analysed.</p>

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Intelligent Reflecting Surface-Enhanced Target Detection in MIMO Radar

Cited by 53 publications

References 10 publications

MetaRadar: Multi-target Detection for Reconfigurable Intelligent Surface Aided Radar Systems

MetaRadar: Multi-target Detection for Reconfigurable Intelligent Surface Aided Radar Systems

RIS-Enabled Self-Localization: Leveraging Controllable Reflections With Zero Access Points

An adaptive learning algorithm for spectrum sensing based on direction of arrival estimation in cognitive radio systems

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