MMSE-Based MDL Method for Robust Estimation of Number of Sources Without Eigendecomposition

Huang, Lei; Li, Teng; Mao, Erke; So, Hing Cheung

doi:10.1109/tsp.2009.2024043

Cited by 46 publications

(20 citation statements)

References 12 publications

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“…The distance between two adjacent sensors is d. As shown in Figure 1, the first sensor is regarded as the reference point for the array, assuming that there are K uncorrelated narrowband far-field signals impinging on this array from distinct directions θ 1 , … , θ K We also assume that the number of sources K is known a priori or has been estimated by the methods shown in [16,17]. Under such a scenario, the received signal vector x n ∈ ℂ…”

Section: System Modelmentioning

confidence: 99%

Estimation of DOA for Noncircular Signals via Vandermonde Constrained Parallel Factor Analysis

Lin¹,

Yuan²,

Du³

et al. 2018

International Journal of Antennas and Propagation

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We provide a complete study on the direction-of-arrival (DOA) estimation of noncircular (NC) signals for uniform linear array (ULA) via Vandermonde constrained parallel factor (PARAFAC) analysis. By exploiting the noncircular property of the signals, we first construct an extended matrix which contains two times sampling number of the received signal. Then, taking the Vandermonde structure of the array manifold matrix into account, the extended matrix can be turned into a tensor model which admits the Vandermonde constrained PARAFAC decomposition. Based on this tensor model, an efficient linear algebra algorithm is applied to obtain the DOA estimation via utilizing the rotational invariance between two submatrices. Compared with some existing algorithms, the proposed method has a better DOA estimation performance. Meanwhile, the proposed method consistently has a higher estimation accuracy and a much lower computational complexity than the trilinear alternating least square-(TALS-) based PARAFAC algorithm. Finally, numerical examples are conducted to demonstrate the effectiveness of the proposed approach in terms of estimation accuracy and computational complexity.

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Section: System Modelmentioning

confidence: 99%

Estimation of DOA for Noncircular Signals via Vandermonde Constrained Parallel Factor Analysis

Lin¹,

Yuan²,

Du³

et al. 2018

International Journal of Antennas and Propagation

View full text Add to dashboard Cite

show abstract

“…1) The incoming signals are mutually independent, zero-mean and stationary processes; 2) The noise is zero-mean, additive white Gaussian, and statistically independent of all impinging sources; 3) In order to avoid the phase ambiguities, the inter-element spacing d should be within a quarter wavelength; 4) The number of sources K , K 1 and K 2 are known or accurately estimated [29][30][31], and the sensors number satisfies K < 2M + 1 and K 2 < M + 1.…”

Section: Signal Modelmentioning

confidence: 99%

Localization of mixed far-field and near-field sources under unknown mutual coupling

Xie

Tao

Rao

et al. 2016

Digital Signal Processing

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“…The array measurements are modeled as where s(t) = [s 1 (t), · · · , s d (t)] T is the signal vector, n(t) = [n 1 (t), · · · , n M (t)] T contains the additive sensor noise, and A = [a(θ 1 , φ 1 ), · · · , a(θ d , φ d )] is the steering matrix, which consists of d array steering vectors a(θ i , φ i ), i = 1, · · · , d, with θ i and φ i representing the elevation and azimuth angles of the ith signal. Here, d is known to the receiver or estimated by an information theoretic criterion, such as the Akaike information criterion (AIC) [20], minimum description length (MDL) [21], or their computationally efficient variants [22,23]. The array steering vector is given as…”

Section: Problem Formulationmentioning

confidence: 99%

“…We define the augmented measurement matrix x (nc) (t) as (23) to correspond with [17,18]. Substituting (1) and (9) into (23) yields…”

Section: A Motivationmentioning

confidence: 99%

Direction-of-arrival estimation for noncircular sources via structured least squares–based esprit using three-axis crossed array

Shi

Cheng

2015

IEEE Trans. Aerosp. Electron. Syst.

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A structured least squares (SLS)-based ESPRIT algorithm for direction-of-arrival (DOA) estimation of strictly second-order noncircular signals using a crossed array is devised in this paper. Unlike the conventional ESPRIT for noncircular signals, which employs the least squares or total least squares, the proposed solution is able to exploit the overlapping structure of the subarray configurations to efficiently solve the shift-invariant equations (SIEs), ending up with the SLS solution for a three-axis crossed array. Moreover, an additional constraint requiring the SIEs of the three linear subarrays directed along the x-, y-, and z-axes to share the same set of eigenvectors is applied to solve the rank-deficiency problem. Consequently, the proposed approach provides more accurate DOA estimates, and its superiority over existing ESPRIT schemes is demonstrated via computer simulations.

show abstract

MMSE-Based MDL Method for Robust Estimation of Number of Sources Without Eigendecomposition

Cited by 46 publications

References 12 publications

Estimation of DOA for Noncircular Signals via Vandermonde Constrained Parallel Factor Analysis

Estimation of DOA for Noncircular Signals via Vandermonde Constrained Parallel Factor Analysis

Localization of mixed far-field and near-field sources under unknown mutual coupling

Direction-of-arrival estimation for noncircular sources via structured least squares–based esprit using three-axis crossed array

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