The ORLS-Based DoA Estimation for Unknown Mixtures of Uncorrelated and Coherent Signals Under Unknown Number of Sources

Yao, Guijin; Zhang, Hairong; Li, Ling; Hu, Fengye

doi:10.1109/lsp.2021.3084520

Cited by 6 publications

(11 citation statements)

References 9 publications

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“…It can be seen from this figure that the proposed MODE-SOMP performs identically to the standard SOMP algorithm that uses a priori known L. For comparison, the angular spectrum of the standard SOMP algorithm with L estimated by MDL technique is also plotted. It can be observed from the figure that the spectral peaks corresponding to true DoAs with the MDL approach are not as well-pronounced as those of the MODE-SOMP and standard SOMP with known L. This follows from inaccuracies in MDL estimates at low SNR [31,32]. Furthermore, the MDL technique has another drawback that it requires number of snapshots ≥ number of array sensors [31,32] and so it cannot be used in the single-snapshot case.…”

Section: Simulation Resultsmentioning

confidence: 98%

“…For example, the conventional subspace-based DoA estimation techniques require a priori knowledge of source order and they return inconsistent DoA estimates when an inaccurate estimate of source order is provided to them [11,12]. In this context, information theoretic approaches such as the AIC and MDL techniques have been widely used for model-order estimation [31,32]. These algorithms utilize the number of identical smallest eigenvalues of the received signal's sample covariance matrix to estimate the number of signal sources L. However, in practical scenarios with low SNR and limited number of measurement snapshots, the AIC and MDL techniques both tend to estimate a wrong number of sources [31,32].…”

Section: Model-order Estimation Errorsmentioning

confidence: 99%

“…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%

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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: Simulation Resultsmentioning

confidence: 98%

Section: Model-order Estimation Errorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Hamid

Wyne

Butt

2023

Sensors

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“…We consider an antenna array composed of M elements impinged by P ( P < M ) far‐field uncorrelated signals [11]. Without losing generality, here we use a Uniform Circular antenna Array (UCA) to illustrate the model.…”

Section: Problem Formulationmentioning

confidence: 99%

Number of targets detection method with FCM‐based granulation–degranulation mechanism

Sha

Mao

et al. 2022

Electronics Letters

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In this letter, the authors elaborate on a novel method to detect the number of targets in low SNR and small snapshots through using the Fuzzy C‐Means clustering based granulation‐degranulation mechanism. In the developed scheme, the eigenvalues of the array output correlation matrix are regarded as a time series, and granulated into a pair of ordered information granules. The information about the number of targets is included in the information granules. Subsequently, the degranulation mechanism is used to modify the cost function of the granulation mechanism to improve the quality of the information granules, which can increase the detection accuracy of the number of targets. Finally, the number of targets is determined by the prototype matrix and partition matrix generated by the enhanced version of the granulation. Explicit analysis and derivation of the proposed scheme are presented.

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“…Then, a subarray sampling approach for DOA estimation is proposed, where the covariance matrix of the whole array is estimated from the subarrays using a neural network [37]. An recursive order method is applied for the DOA estimation with an unknown number of targets and combines two spatial modified Yule-Walker systems in [38]. In [39], a harmonic retrieval joint multiple regression method is proposed for the DOA estimation against unknown spatially colored noise in the radar system.…”

Section: Introductionmentioning

confidence: 99%

Efficient DOA Estimation Method for Reconfigurable Intelligent Surfaces Aided UAV Swarm

Chen,

Zheng

et al. 2022

Preprint

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The conventional direction of arrival (DOA) estimation methods are performed with multiple receiving channels. In this paper, a changeling DOA estimation problem is addressed in a different scenario with only one full-functional receiving channel. A new unmanned aerial vehicle (UAV) swarm system using multiple lifted reconfigurable intelligent surface (RIS) is proposed for the DOA estimation. The UAV movement degrades the DOA estimation performance significantly, and the existing atomic norm minimization (ANM) methods cannot be used in the scenario with array perturbation. Specifically, considering the position perturbation of UAVs, a new atomic norm-based DOA estimation method is proposed, where an atomic norm is defined with the parameter of the position perturbation. Then, a customized semi-definite programming (SDP) method is derived to solve the atomic norm-based method, where different from the traditional SDP method, an additional transforming matrix is formulated. Moreover, a gradient descent method is applied to refine the estimated DOA and the position perturbation further. Simulation results show that the proposed method achieves much better DOA estimation performance in the RIS-aided UAV swarm system with only one receiving channel than various benchmark schemes.

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The ORLS-Based DoA Estimation for Unknown Mixtures of Uncorrelated and Coherent Signals Under Unknown Number of Sources

Cited by 6 publications

References 9 publications

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

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

Number of targets detection method with FCM‐based granulation–degranulation mechanism

Efficient DOA Estimation Method for Reconfigurable Intelligent Surfaces Aided UAV Swarm

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