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1. One-Bit DoA Estimation via Sparse Linear Arrays

   Sedighi, S.; Shankar, B.; Soltanalian, M.; Ottersten, B. (January 2020, 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP))

   Parameter estimation from noisy and one-bit quantized data has become an important topic in signal processing, as it offers low cost and low complexity in the implementation. On the other hand, Direction-of-Arrival (DoA) estimation using Sparse Linear Arrays (SLAs) has recently gained considerable interest in array processing due to their attractive capability of providing enhanced degrees of freedom. In this paper, the problem of DoA estimation from one-bit measurements received by an SLA is considered and a novel framework for solving this problem is proposed. The proposed approach first provides an estimate of the received signal covariance matrix through minimization of a constrained weighted least-squares criterion. Then, MUSIC is applied to the spatially smoothed version of the estimated covariance matrix to find the DoAs of interest. Several numerical results are provided to demonstrate the superiority of the proposed approach over its counterpart already propounded in the literature. 

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2. One-Bit Normalized Scatter Matrix Estimation For Complex Elliptically Symmetric Distributions

   https://doi.org/10.1109/ICASSP40776.2020.9053956  

   Liu, Chun-Lin; Vaidyanathan, P. P. (May 2020, Proc. IEEE Int. Conf. Acoust. Speech, and Signal Proc)

   null (Ed.)

   One-bit quantization has attracted attention in massive MIMO, radar, and array processing, due to its simplicity, low cost, and capability of parameter estimation. Specifically, the shape of the covariance of the unquantized data can be estimated from the arcsine law and onebit data, if the unquantized data is Gaussian. However, in practice, the Gaussian assumption is not satisfied due to outliers. It is known from the literature that outliers can be modeled by complex elliptically symmetric (CES) distributions with heavy tails. This paper shows that the arcsine law remains applicable to CES distributions. Therefore, the normalized scatter matrix of the unquantized data can be readily estimated from one-bit samples derived from CES distributions. The proposed estimator is not only computationally fast but also robust to CES distributions with heavy tails. These attributes will be demonstrated through numerical examples, in terms of computational time and the estimation error. An application in DOA estimation with MUSIC spectrum is also presented. 

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3. Deep Learning-Enabled Improved Direction-of-Arrival Estimation Technique

   https://doi.org/10.3390/electronics12163505  

   Jenkinson, George; Abbasi, Muhammad_Ali Babar; Molaei, Amir Masoud; Yurduseven, Okan; Fusco, Vincent (August 2023, Electronics)

   This paper provides a simple yet effective approach to improve direction-of-arrival (DOA) estimation performance in extreme signal-to-noise-ratio (SNR) conditions. As an example, a multiple signal classification (MUSIC) algorithm with a deep learning (DL) approach is used. First, brief research into the existing DOA estimation techniques is provided, followed by a demonstration of a simulation environment created on the MATLAB platform to generate and resolve signals from a uniform rectangular array of antenna elements. Following that is an attempt to improve the estimation accuracy of these signals by training various DL approaches, including multi-layer perceptron and one- and two-dimensional convolutional neural networks, using the generated dataset. Key findings include the cases where the developed DL approach can resolve signals and provide accurate DOA estimations that the MUSIC algorithm cannot. 

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4. Convolutional Beamspace for Array Signal Processing

   https://doi.org/10.1109/ICASSP40776.2020.9054051  

   Vaidyanathan, P. P.; Chen, Po-Chih (May 2020, Proc. IEEE Int. Conf. Acoust. Speech, and Signal Proc)

   null (Ed.)

   A new type of beamspace for array processing is introduced called convolutional beamspace. It enjoys the advantages of traditional beamspace such as lower computational complexity, increased parallelism of subband processing, and improved resolution threshold for DOA estimation. But unlike traditional beamspace methods, it allows root-MUSIC and ESPRIT to be performed directly for ULAs without any overhead of preparation, as the Vandermonde structure and the shift-invariance are preserved under the transformation. The method produces more accurate DOA estimates than traditional beamspace methods, and for correlated sources it produces better estimates than element-space methods. 

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5. Direction Finding with 2D Arrays Using Spatial Sigma-Delta ADCs

   https://doi.org/10.1109/SPAWC51858.2021.9593242  

   Pirzadeh, Hessam; Rao, Shilpa; Lee Swindlehurst, A. (September 2021, 2021 IEEE 22nd International Workshop on Signal Processing Advances in Wireless Communications (SPAWC))

   In many multiple-input multiple-output (MIMO) communication applications, two-dimensional (2D) rectangular arrays are used and the angular field of interest is different in the azimuth and elevation angle domains. In this paper, we show how to exploit scenarios with users confined to narrow elevation angles by means of 2D rectangular arrays with low-resolution spatial Σ∆ sampling in only one (i.e., the vertical) dimension. We analyze the 2D directions-of-arrival (DoA) estimation performance of MUSIC for such arrays, and illustrate the resulting advantage of the Σ∆ approach over standard one-bit receivers. 

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