DOA estimation using multiple measurement vector model with sparse solutions in linear array scenarios

Hosseini, Seyyed Moosa; Sadeghzadeh, R. A.; Virdee, Bal S.

doi:10.1186/s13638-017-0838-y

Cited by 4 publications

(1 citation statement)

References 15 publications

(19 reference statements)

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“…The MMV case is often encountered in practical applications, such as source localization and direction-of-arrival estimation (DOA) [19]. In the MMV case, the sparse signals at all snapshots share the same support [20]. Such joint sparsity has been exploited to improve the probability of successful recovery [21].…”

Section: A Review Of Existing Literature and Motivationmentioning

confidence: 99%

MIMO Radar Imaging With Multiple Probing Pulses for 2D Off-Grid Targets via Variational Sparse Bayesian Learning

Wen

Chen

Duan³

et al. 2020

IEEE Access

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Spatial sparsity of the target space has been successfully exploited to provide accurate rangeangle images by the methods based on sparse signal reconstruction (SSR) in Multiple-Input Multiple-Output (MIMO) radar imaging applications. The SSR based method discretizes the continuous target space into finite grid points and generates an observation model utilized in image reconstruction. However, inaccuracies in the observation model may cause various degradations and spurious peaks in the reconstructed images. In the process of the image formation, the off-grid problem frequently occurs that the true locations of targets that do not coincide with the computation grid. In this paper, we consider the case that the true location of a target has both range and angle-varying two-dimensional (2D) off-grid errors with a noninformative prior. From a variational Bayesian perspective, an iterative algorithm is developed for joint MIMO radar imaging with orthogonal frequency division multiplexing (OFDM) linear frequency modulated (LFM) waveforms and 2D off-grid error estimation of off-grid targets. The targets during multiple probing pulses are modeled as Swerling II case and a unified generalized inverse Gaussian (GIG) prior is adopted for the target reflection coefficient variance at all snapshots. Furthermore, an approach to reducing the computational workload of the signal recovery process is proposed by using singular value decomposition. Experimental results show that the proposed algorithm is insensitive to noise and has improved accuracy in terms of mean squared estimation error under different computation grid interval. INDEX TERMS sparse Bayesian learning, multiple-input multiple-output (MIMO) radar imaging, multiple probing pulses, orthogonal frequency division multiplexing (OFDM), two-dimensional (2D) off-grid error.

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Section: A Review Of Existing Literature and Motivationmentioning

confidence: 99%