Space time smoothing algorithm for DOA estimation of coherent signals

Ding, Zhoupeng; Li, Hongtao; Kuang, Qi; Xu, Chunbao

doi:10.1109/icsp54964.2022.9778598

Cited by 3 publications

(3 citation statements)

References 7 publications

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“…n (M+N) (t)] T represents a complex Gaussian white noise vector, the noise component can be considered insignificant. Based on array signal processing theory, the narrowband far-field signal satisfies the following conditions [27]:…”

Section: A Space-time Correlation Algorithmmentioning

confidence: 99%

“…where 𝑹𝑹 𝑥𝑥𝑠𝑠 (𝑡𝑡) = 𝑨𝑨𝜶𝜶𝑹𝑹 𝑠𝑠 𝜶𝜶 𝐻𝐻 𝑨𝑨 𝐻𝐻 . Second, to eliminate 𝑒𝑒 j𝜔𝜔𝜔𝜔 , we employ the covariance technique to obtain the covariance matrix without the 𝑒𝑒 j𝜔𝜔𝜔𝜔 term [27]:…”

Section: B Enhanced Spatial Smoothingmentioning

confidence: 99%

“…The practical application of the model lies in the field of medical imaging, where it enhances the resolution and quality of various imaging techniques, including ultrasonic imaging and nuclear magnetic resonance imaging [16]. Drawing inspiration from the literature [27,28], we proposed an Enhanced Spatial Smoothing (ESS) algorithm based on the space-time correlation matrix. First, it is necessary to de-noise the received signal.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Coherent DOA Estimation Algorithm with Co-Prime Arrays for Low SNR Signals

Zhang,

Cao,

Wang

2023

Sensors

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The Direction of Arrival (DOA) estimation of coherent signals in co-prime arrays has become a popular research topic. However, traditional spatial smoothing and subspace algorithms fail to perform well under low signal-to-noise ratio (SNR) and small snapshots. To address this issue, we have introduced an Enhanced Spatial Smoothing (ESS) algorithm that utilizes a space-time correlation matrix for de-noising and decoherence. Finally, an Estimating Signal Parameter via Rotational Invariance Techniques (ESPRIT) algorithm is used for DOA estimation. In comparison to other decoherence methods, when the SNR is −8 dB and the number of snapshots is 150, the mean square error (MSE) of the proposed algorithm approaches the Cramér–Rao bound (CRB), the probability of resolution (PoR) can reach over 88%, and, when the angular resolution is greater than 4°, the estimation accuracy can reach over 90%.

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Section: A Space-time Correlation Algorithmmentioning

confidence: 99%

Section: B Enhanced Spatial Smoothingmentioning

confidence: 99%