Application of Sparse Representation to Bartlett Spectra for Improved Direction of Arrival Estimation

Compaleo, Jacob; Gupta, I.J.

doi:10.3390/s21010077

Cited by 4 publications

(8 citation statements)

References 38 publications

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“…In [34], we presented a high-resolution sparse representation-based DOA estimation method, the SDSR method, that uses the conventional Bartlett spectra as a forward model and starting point. Initially, the observed Bartlett spectra, b, is generated.…”

Section: Review Of Sdsr Methodsmentioning

confidence: 99%

“…x(β) of length M represents the signal strength of the individual emitters that leads to the desired match between the observed spectra and combined spectra of the individual emitters and β is the sparse representation regularization parameter. Our method for selecting β is reported in [34]. The additional constraint that all entries of vector x(β) must be positive was also included.…”

Section: Review Of Sdsr Methodsmentioning

confidence: 99%

“…In [34], we showed that the SDSR is an unbiased and efficient estimator for samestrength signals in a plethora of scenarios. As mentioned, the proposed SDSR method utilizes the conventional Bartlett spectra as a forward model and starting point.…”

Section: Review Of Sdsr Methodsmentioning

confidence: 99%

“…In [34], we presented a sparse-representation approach for DOA estimation that utilizes Bartlett's method as a forward model and starting point. We call this approach the Spectral Domain Sparse Representation (SDSR) method.…”

Section: Introductionmentioning

confidence: 99%

“…In [34], we focused solely on scenarios where all signals had equal strength. We showed that the SDSR method outperforms the MUSIC algorithm when angular separation between signals is small, SNR is low, and a small number of snapshots are used for DOA estimation.…”

Section: Introductionmentioning

confidence: 99%

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Spectral Domain Sparse Representation for DOA Estimation of Signals with Large Dynamic Range

Compaleo

Gupta

2021

Sensors

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Recently, we proposed a Spectral Domain Sparse Representation (SDSR) approach for the direction-of-arrival estimation of signals incident to an antenna array. In the approach, sparse representation is applied to the conventional Bartlett spectra obtained from snapshots of the signals received by the antenna array to increase the direction-of-arrival (DOA) estimation resolution and accuracy. The conventional Bartlett spectra has limited dynamic range, meaning that one may not be able to identify the presence of weak signals in the presence of strong signals. This is because, in the conventional Bartlett spectra, uniform weighting (window) is applied to signals received by various antenna elements. Apodization can be used in the generation of Bartlett spectra to increase the dynamic range of the spectra. In Apodization, more than one window function is used to generate different portions of the spectra. In this paper, we extend the SDSR approach to include Bartlett spectra obtained with Apodization and to evaluate the performance of the extended SDSR approach. We compare its performance with a two-step SDSR approach and with an approach where Bartlett spectra is obtained using a low sidelobe window function. We show that an Apodization Bartlett-based SDSR approach leads to better performance with just single-step processing.

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Section: Review Of Sdsr Methodsmentioning

confidence: 99%

Section: Review Of Sdsr Methodsmentioning

confidence: 99%

Section: Review Of Sdsr Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spectral Domain Sparse Representation for DOA Estimation of Signals with Large Dynamic Range

Compaleo

Gupta

2021

Sensors

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Sparse Bayesian learning for wideband direction-of-arrival estimation via beamformer power outputs in a strong interference environment

Zhang

Yang

et al. 2022

JASA Express Letters

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Sparse Bayesian learning (SBL) offers a useful tool for wideband direction-of-arrival (DOA) estimation, but its performance is limited in the presence of strong interferences. To solve this problem, this letter attempts to extend the SBL to estimate DOAs via the beamformer power outputs (BPO) because the beamformer can efficiently suppress the interferences. A Bayesian probabilistic model effective for the BPO is proposed. Based on this, a BPO-based SBL method is put forward by adopting the variational Bayesian inference to estimate the DOAs from the BPO. Simulation and experimental results confirm the good performance of the proposed method.

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Atomic Network-Based DOA Estimation Using Low-Bit ADC

Sheng

Chen

Yao³

et al. 2021

Electronics

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In the direction of arrival (DOA) estimation problem, when a low-bit analog to digital converter (ADC) is used, the estimation performance severely deteriorates. In this paper, the DOA estimation problem is considered in a low-cost direction finding system with low-bit ADC. To eliminate quantization noise, we propose a novel network ADCnet, which is a composition of fully connected layers and exponential linear unit (ELU) layers, and the input signals are the received signals using low-bit ADC. After the ADCnet, an AtomicNet is also proposed to estimate the DOA from the denoised signals, where atomic vectors are corresponding to the steer vectors. A loss function considering both the reconstruction performance and the sparsity is proposed in the AtomicNet. Different from the exiting atomic norm-based methods, the proposed method can avoid an optimization problem and estimate the DOA with lower computational complexity. Simulation results show that the proposed method outperforms the existing methods in the DOA estimation performance using low-bit ADC.

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Application of Sparse Representation to Bartlett Spectra for Improved Direction of Arrival Estimation

Cited by 4 publications

References 38 publications

Spectral Domain Sparse Representation for DOA Estimation of Signals with Large Dynamic Range

Spectral Domain Sparse Representation for DOA Estimation of Signals with Large Dynamic Range

Sparse Bayesian learning for wideband direction-of-arrival estimation via beamformer power outputs in a strong interference environment

Atomic Network-Based DOA Estimation Using Low-Bit ADC

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