Underdetermined DOA estimation with unknown source number in nonuniform noise

Jiang, Guangyao; Mao, Xingpeng; Wang, Mianzhi; Liu, Yongtan; Nehorai, Arye

doi:10.1109/radar.2018.8378773

Cited by 1 publication

(4 citation statements)

References 16 publications

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“…Then we let the above chosen values and the values in a s form a vectorŷ 1 in order of lags. After replacing only two items inŷ 1 , which means we only replace b 1 1 and b −1 1 with b 1 2 and b −1 2 respectively, we getŷ 2 . Similarly, after replacing only the values b .…”

Section: B Pseudo Data Setmentioning

confidence: 99%

“…. For example, if m 1 > 2, we formŷ N n −1 by replacing b 1 1 and b −1 1 inŷ 1 with b 1 3 and b −1 3 . At last, we only replace the value inŷ 1 , which corresponds to lag 0, with the second minimun value in the N n covariance values to formŷ 2N n −6 .…”

Section: B Pseudo Data Setmentioning

confidence: 99%

“…Any new formed vector will be linearly dependent with them, i.e., any new formed vector can be expressed by a linear combination of the vectors in the pseudo data set. For example, if m 1 > 3, we can form a new vectorŷ 2N n −5 by replacing b 1 1 and b −1…”

Section: B Pseudo Data Setmentioning

confidence: 99%

“…Underdetermined direction-of-arrival (DOA) estimation, i.e., estimating K DOAs from N < K sensors, is a problem of significance in engineering and science [1]- [4]. One way to deal with this problem is to construct a virtual array that offers more degrees of freedom (DOFs) than the physical array.…”

Section: Introductionmentioning

confidence: 99%

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Underdetermined Direction-of-Arrival Estimation Using Difference Coarray in the Presence of Unknown Nonuniform Noise

et al. 2019

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The coarray techniques, e.g., nested and coprime arrays, can significantly improve degrees of freedom (DOFs) via constructing a so-called difference coarray, which enables underdetermined directionof-arrival (DOA) estimation within reach in the presence of unknown nonuniform noise. There are repeated lags in the difference coarray, which also contain useful statistical information. In this paper, the repeated lags are properly used for DOA estimation algorithm design in unknown nonuniform noise environments. Specifically, the number of repeated lags in the difference coarray is rigorously given. Then these repeated lags and unique lags are judiciously rearranged to form a pseudo data set, which is composed of linearly independent vectors. Based on the pseudo data set, we propose two algorithms for DOA estimation in the presence of unknown nonuniform noise. One is a searching algorithm without source number knowledge (SASNK), and the other is a multi-snapshot compressive sensing method (MSCS) with better DOA estimation performance. The MSCS also does not require source number information. Numerical results are included to showcase the effectiveness of the proposed algorithms. INDEX TERMS Direction-of-arrival (DOA) estimation, nested array, coprime array, repeated lags, pseudo data set, unknown nonuniform noise.

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Section: B Pseudo Data Setmentioning

confidence: 99%

Section: B Pseudo Data Setmentioning

confidence: 99%

Section: B Pseudo Data Setmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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