Direction finding algorithms for time reversal MIMO radars

Foroozan, Foroohar; Asif, Amir; Jin, Yunwai; Moura, J.M.F.

doi:10.1109/ssp.2011.5967724

Cited by 16 publications

(10 citation statements)

References 9 publications

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“…Indeed, several researchers have reported that taking advantage of the super-resolution focusing property of TR improves the detection accuracy [4,5] in radars. Previously, we have applied TR for radar target localization using both single-input/multiple-output (SIMO) [6,7] and MIMO radar systems [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Time reversal MIMO radar: Improved CRB and Angular Resolution Limit

Foroozan

Asif

Boyer

2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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The paper derives closed form (nonmatrix) expression for the deterministic Cramér-Rao bound (CRB) for the direction-of-arrival associated with a target embedded in a noisy, multipath channel using the time reversal (TR) MIMO system. By incorporating the TR built-in adaptive waveform processing feature to reshape the MIMO probing signals, we prove that the CRBs for the direction of arrival can be improved in ways not foreseen with the conventional MIMO radars. A second contribution of the paper is the analytical derivation of the Angular Resolution Limit (ARL) defined as the minimal separation between two targets to be separately resolved by the MIMO radar. At high signal-to-noise ratio, we show that the TR ARL inherits the properties of the TR CRB and is superior to its conventional counterpart by a factor proportional to the order of the channel multipath.

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Section: Introductionmentioning

confidence: 99%

Time reversal MIMO radar: Improved CRB and Angular Resolution Limit

Foroozan

Asif

Boyer

2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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“…Asif’s research team has published many articles on this subject by active time reversal (ATR) in radar domain [ 32 , 34 , 35 , 36 , 37 , 38 , 39 ]. In [ 32 ] and [ 34 ], a TR DOA estimator for a passive target in a Ground Penetrating Radar (GPR) and a mobile communication network are studied, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, TR/range estimator is added and the active array source location for a single target is accomplished [ 35 ]. In [ 36 ], a TR-based DOA estimation framework for multiple-input multiple-output (MIMO) radars is presented. Reference [ 37 ] introduces TR into the Angle-Doppler estimation in MIMO radars and the closed-form expressions of Cramér-Rao bound (CRB) for DOA and Doppler frequency of a moving target in MIMO and TR/MIMO are derived in [ 38 ].…”

Section: Introductionmentioning

confidence: 99%

DOA Estimation for Underwater Target by Active Detection on Virtual Time Reversal Using a Uniform Linear Array

Hai-xia

Wang

Liu

et al. 2018

Sensors

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Aiming at addressing the problem caused by multipath effects in direction of arrival (DOA) estimation for underwater targets, a method based on the active detection on virtual time reversal (ADVTR) Capon algorithm is proposed. Unlike the conventional passive target estimation method ignoring the multipath effects but only considering the direct wave, the proposed method is closer to the actual situation in that the multipath signal propagation model is fully taken into account; in addition, active detection (AD) and virtual time reversal (VTR) processes are added, which use active detection to estimate channels, and virtual time reversal to realize focusing in a computer after the source-receive array (SRA) receives the reflected signal of the target. The combination of the two methods can greatly improve the energy of SRA and the precision of target direction estimation. With the popular acoustic field simulation tool Bellhop, the model proposed in this paper is verified. Compared with the conventional Capon method without time reversal, the simulation results show that the ADVTR Capon estimation method is far better, in terms of resolution and suppressing the sidelobes. It is suitable for the target DOA estimation under low signal-to-noise ratio (SNR) conditions. Further, we also show the ADVTR Capon estimation method works well in a real tank experiment.

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“…Wideband source localization using Beamforming was considered in [3], [4]. DOA estimation using Capon algorithm was originally developed by [5]. Based on time reversal, Capon algorithm or what is called Minimum Variance Distortionless Response (MVDR) and MUltiple SIgnal Classification (MUSIC) were introduced in [6] for MIMO radars.…”

Section: Introductionmentioning

confidence: 99%

DOA estimation in MIMO systems with Compressive Sensing for future handsets

Alawsh

Muqaibel

Sharawi

2015

2015 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT)

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Future handsets are characterized by their small size which limits the number of antennas and the spacing between them. This paper mainly focuses on Direction-ofArrival (DOA) estimation in Multiple-Input-Multiple-Output (MIMO) systems based on Compressive Sensing (CS) with practical limitations. The main objective is to strike an optimal balance between complexity and accuracy. A comparison between important DOA estimation algorithms is presented including: Beamforming, Capon, MUSIC, and -Singular Value Decomposition ( -SVD). When considering two antennas with practical antenna spacing limitations, simulation shows that the Root Mean Squared Error (RMSE) of the estimated DOAs is approximately the same for all algorithms in the presence of a single source. The smallest RMSEs are achieved when the spacing between the antennas relative to the wavelength = 1/3. The -SVD algorithm is recommended due to the narrow beamwidth and high resolution.

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Direction finding algorithms for time reversal MIMO radars

Cited by 16 publications

References 9 publications

Time reversal MIMO radar: Improved CRB and Angular Resolution Limit

Time reversal MIMO radar: Improved CRB and Angular Resolution Limit

DOA Estimation for Underwater Target by Active Detection on Virtual Time Reversal Using a Uniform Linear Array

DOA estimation in MIMO systems with Compressive Sensing for future handsets

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