2D DOA estimation of coherent sources based on reconstruction of Toeplitz matrix sets

Jin, Aisuo; Zhang, Fei

doi:10.1109/cac51589.2020.9327264

Cited by 3 publications

(1 citation statement)

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“…Therefore, research into DOA estimation methods for coherent wideband signals holds significant practical importance. Many algorithms have been developed to counteract the effect of coherence [ 21 , 22 , 23 , 24 , 25 , 26 ], such as the widely used algorithm known as spatial smoothing pre-processing (SSP) [ 27 ], which divides the entire array into a series of overlapping subarrays to obtain a new data covariance matrix with recovered rank. In this paper, in order to take full advantage of the covariance matrix of individual subarrays and the mutual covariance matrix of different subarrays, an enhanced spatial smoothing algorithm is used to more effectively counteract the effects of coherence.…”

Section: Introductionmentioning

confidence: 99%

Direction of Arrival Estimation of Coherent Wideband Sources Using Nested Array

Tang

Deng

et al. 2023

Sensors

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Due to their ability to achieve higher DOA estimation accuracy and larger degrees of freedom (DOF) using a fixed number of antennas, sparse arrays, etc., nested and coprime arrays have attracted a lot of attention in relation to research into direction of arrival (DOA) estimation. However, the usage of the sparse array is based on the assumption that the signals are independent of each other, which is hard to guarantee in practice due to the complex propagation environment. To address the challenge of sparse arrays struggling to handle coherent wideband signals, we propose the following method. Firstly, we exploit the coherent signal subspace method (CSSM) to focus the wideband signals on the reference frequency and assist in the decorrelation process, which can be implemented without any pre-estimations. Then, we virtualize the covariance matrix of sparse array due to the decorrelation operation. Next, an enhanced spatial smoothing algorithm is applied to make full use of the information available in the data covariance matrix, as well as to improve the decorrelation effect, after which stage the multiple signal classification (MUSIC) algorithm is used to obtain DOA estimations. In the simulation, with reference to the root mean square error (RMSE) that varies in tandem with the signal-to-noise ratio (SNR), the algorithm achieves satisfactory results compared to other state-of-the-art algorithms, including sparse arrays using the traditional incoherent signal subspace method (ISSM), the coherent signal subspace method (CSSM), spatial smoothing algorithms, etc. Furthermore, the proposed method is also validated via real data tests, and the error value is only 0.2 degrees in real data tests, which is lower than those of the other methods in real data tests.

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