An Adaptive Lp Norm Minimization Algorithm for Direction of Arrival Estimation

Liu, Lutao; Rao, Zejing

doi:10.3390/rs14030766

Cited by 11 publications

(12 citation statements)

References 17 publications

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“…In many underwater deployment scenarios, sensor array systems have to face different problems such as gain and phase uncertainties [ 26 ], non-uniform noise [ 27 ], faulty sensors in the array [ 28 , 29 , 30 ], unknown source order [ 31 , 32 ], resolution of closely spaced sources [ 33 , 34 ], off-grid DoAs [ 35 , 36 ], availability of limited snapshots [ 20 , 23 ], and low SNR [ 37 , 38 ].…”

Section: Introductionmentioning

confidence: 99%

Joint Model-Order and Robust DoA Estimation for Underwater Sensor Arrays

Hamid

Wyne

Butt

2023

Sensors

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The direction-of-arrival (DoA) estimation algorithms have a fundamental role in target bearing estimation by sensor array systems. Recently, compressive sensing (CS)-based sparse reconstruction techniques have been investigated for DoA estimation due to their superior performance relative to the conventional DoA estimation methods, for a limited number of measurement snapshots. In many underwater deployment scenarios, the acoustic sensor arrays must perform DoA estimation in the presence of several practical problems such as unknown source number, faulty sensors, low values of the received signal-to-noise ratio (SNR), and access to a limited number of measurement snapshots. In the literature, CS-based DoA estimation has been investigated for the individual occurrence of some of these errors but the estimation under joint occurrence of these errors has not been studied. This work investigates the CS-based robust DoA estimation to account for the joint impact of faulty sensors and low SNR conditions experienced by a uniform linear array of underwater acoustic sensors. Most importantly, the proposed CS-based DoA estimation technique does not require a priori knowledge of the source order, which is replaced in the modified stopping criterion of the reconstruction algorithm by taking into account the faulty sensors and the received SNR. Using Monte Carlo techniques, the DoA estimation performance of the proposed method is comprehensively evaluated in relation to other techniques.

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Section: Introductionmentioning

confidence: 99%

Joint Model-Order and Robust DoA Estimation for Underwater Sensor Arrays

Hamid

Wyne

Butt

2023

Sensors

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“…In [14], a reweighted 1 -SVD is utilized for sparse reconstruction to improve the estimation performance. In [15], an improved l p norm minimization DOA estimation method is proposed to separate two closely spaced sources. Ref.…”

Section: Introductionmentioning

confidence: 99%

Off-Grid DOA Estimation Using Sparse Bayesian Learning for MIMO Radar under Impulsive Noise

Zhang

Yang

et al. 2022

Sensors

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Direction of arrival (DOA) estimation is an essential and fundamental part of array signal processing, which has been widely used in radio monitoring, autonomous driving of vehicles, intelligent navigation, etc. However, it remains a challenge to accurately estimate DOA for multiple-input multiple-output (MIMO) radar in impulsive noise environments. To address this problem, an off-grid DOA estimation method for monostatic MIMO radar is proposed to deal with non-circular signals under impulsive noise. In the proposed method, firstly, based on the property of non-circular signal and array structure, a virtual array output was built and a real-valued sparse representation for the signal model was constructed. Then, an off-grid sparse Bayesian learning (SBL) framework is proposed and further applied to the virtual array to construct novel off-grid sparse model. Finally, off-grid DOA estimation was realized through the solution of the sparse reconstruction with high accuracy even in impulsive noise. Numerous simulations were performed to compare the algorithm with existing methods. Simulation results verify that the proposed off-grid DOA method enables evident performance improvement in terms of accuracy and robustness compared with other works on impulsive noise.

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“…The direction of arrival (DOA) estimate is widely utilized in radar array systems, smart antenna, adaptive beamforming, and other applications as a key technology of passive radar [1][2][3][4][5][6][7][8][9]. In recent years, with the rapid development of massive multiple-input multiple-output (MIMO) technology, the DOA estimation method for massive antenna array has become a focus of research [10][11][12][13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Low-Complexity One-Bit DOA Estimation for Massive ULA with a Single Snapshot

Fan

Wang

et al. 2022

Remote Sensing

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Existing one-bit direction of arrival (DOA) estimate methods based on sparse recovery or subspace have issues when used for massive uniform linear arrays (MULAs), such as high computing cost, estimation accuracy depending on grid size, or high snapshot-number requirements. This paper considers the low-complexity one-bit DOA estimation problems for MULA with a single snapshot. Theoretical study and simulation results demonstrate that discrete Fourier transform (DFT) can be applied to MULA for reliable initial DOA estimation even when the received data are quantized by one-bit methods. A precise estimate is then obtained by searching within a tiny area. The resulting method is called one-bit DFT. This method is straightforward and simple to implement. High-precision DOA estimates of MULA can be obtained with a single snapshot, and the computational complexity is significantly less than that of existing one-bit DOA estimation methods. Moreover, the suggested method is easily extensible to multiple snapshot scenarios, and increasing the number of snapshots can further improve estimation precision. Simulation results show the effectiveness of the one-bit DFT method.

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An Adaptive Lp Norm Minimization Algorithm for Direction of Arrival Estimation

Cited by 11 publications

References 17 publications

Joint Model-Order and Robust DoA Estimation for Underwater Sensor Arrays

Joint Model-Order and Robust DoA Estimation for Underwater Sensor Arrays

Off-Grid DOA Estimation Using Sparse Bayesian Learning for MIMO Radar under Impulsive Noise

Low-Complexity One-Bit DOA Estimation for Massive ULA with a Single Snapshot

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