On improvement of joint estimation of DOA and PPS coefficients impinging on ULA

Rakovi, Predrag; Simeunovi, Marko; Djurovi, Igor

doi:10.1016/j.sigpro.2016.12.015

Cited by 11 publications

(7 citation statements)

References 21 publications

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“…With respect to the parameter estimation of PPSs whose orders P exceeded two, we first used PD to reduce the PPS order to two and then employed the combined dictionary algorithm to estimate the two highest-order parameters, a P and a P−1 . When considering a third-order PPS, y(n) = exp(j(a 3 (n ) 3 + a 2 (n ) 2 + a 1 (n ) + a 0 )) + υ(n) (24) where (a 3 , a 2 , a 1 , a 0 ) = (2.75e-6, 1.25e-3,π/8, π/3), the SNR varied between −10dB and 13dB, the total number of samples was set as N = 515, and the sampling interval was = 1.6477s. First, we used PD to reduce the PPS order to two, where PD 1 is defined as PD 1 [n; τ ] = exp j(6a 3 τ (n ) 2 + 4a 2 τ (n )…”

Section: Simulations and Results Analysismentioning

confidence: 99%

A Sparse Decomposition-Based Algorithm for Estimating the Parameters of Polynomial Phase Signals

Ou¹,

Zhao²,

Song

et al. 2019

IEEE Access

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In this paper, we propose an algorithm in which fast sparse decomposition is used to estimate the polynomial phase signal (PPS) parameters. In the proposed algorithm, a fast sparse decomposition is applied to the second-order PPSs, which outperforms three other algorithms in decomposition rate and convergence. The proposed algorithm also estimates the PPS parameters more accurately than other sparse decomposition algorithms. For PPSs whose order exceeds two, the order can be reduced to two via phase differentiation, after which parameters can be estimated using fast sparse decomposition. The simulation results show that the proposed algorithm is characterized by a lower signal-to-noise ratio and higher estimation accuracy than high-order ambiguity function and other recently proposed algorithms. INDEX TERMS Polynomial phase signals (mc-PPSs), parameter estimation, high-order ambiguity function (HAF), sparse decomposition, the combined dictionary.

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Section: Simulations and Results Analysismentioning

confidence: 99%

A Sparse Decomposition-Based Algorithm for Estimating the Parameters of Polynomial Phase Signals

Ou¹,

Zhao²,

Song

et al. 2019

IEEE Access

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“…To reduce calculation complexity, the phase differentiation (PD)-based methods are used as alternatives to the PB approach [9][10][11][12]. The PD, realized by auto-correlation function, decrements signal order reducing the search dimension at the same time.…”

Section: Monocomponent Signalsmentioning

confidence: 99%

“…The ULA is the most popular one and it is used to form other geometries. In the literature, the DOA is estimated by using eigenvalues [7][8], meta-heuristic search algorithms [6], phase differentiation [9][10][11][12][13], compressive sensing [14], time-frequency distributions [15], etc.…”

Section: Introductionmentioning

confidence: 99%

“…Signals received by the ULA are represented by the polynomial phase signal (PPS) model and a recently developed DOA-quasi maximum likelihood (DOA-QML) algorithm [15,29] is coupled with the proposed normalization strategy. When Gaussian noise is considered, the DOA-QML algorithm has excellent performance compared to other state-of-the-art estimators [6,[9][10][11][12]. Thus, the proposed extension is important for covering challenging noise environments.The algorithm consists of two stages.…”

Section: Introductionmentioning

confidence: 99%

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Estimating DOA and PPS parameters of signal received by ULA in heavy noise environment

Djurović

Simeunović

2020

Journal of Electrical Engineering

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Estimation of the direction-of-arrival (DOA) and parameters of polynomial phase signal (PPS) impinging on the uniform linear array (ULA) of sensors in heavy-tailed noise environments is considered in this paper. To estimate signal parameters, a recently proposed quasi maximum-likelihood (QML) estimator is adopted. The proposed algorithm consists of two successive steps: (1) noise influence mitigation by using the proposed normalization strategy and (2) signal parameters estimation using the DOA-QML approach. The algorithm performance is evaluated for both monocomponent and multicomponent signals.

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“…Various high-resolution methods have been proposed during the past several decades such as multiple signal classification (MUSIC) [1], estimating signal parameter via rotational invariance techniques (ESPRIT) [2], maximum likelihood (ML) [3], etc. What's more, the methods mentioned above are based on the prior knowledge of array configuration, and much research on classic configurations like uniform linear array (ULA) [4], uniform rectangular array (URA) [5], L-shaped array [6], nested array [7], co-prime array [8] and uniform-circular-array (UCA) [9]- [12] has been presented in recent literature.…”

Section: Introductionmentioning

confidence: 99%

Direction of Arrival Estimation for Uniform Circular Array via “Virtual Two-Dimensional Source Impinging on Virtual Multiple UCAs Equivalently” in the Presence of Gain-Phase Errors

2019

IEEE Access

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Direction-of-arrival (DOA) estimation in the presence of gain-phase errors for uniformcircular-array (UCA) is addressed in this paper. Thanks to the uniformity and cycle property of UCA, the long data vector constructed with the proper entries of the Hadamard product of autocorrelation matrix and its conjugate, can be regarded as the received data corresponding to virtual two-dimensional sources impinging on virtual multiple UCAs. This finding indicates that azimuths and elevations of virtual sources related to DOAs of original sources can be decoupled and estimated via multiple-UCAs-estimating signal parameter via rotational invariance techniques proposed in this paper. Furthermore, two novel methods named as spatial-filtering-method and parameter-estimation-method are proposed to cope with one-component, which affects the DOA estimation of adjacent sources. Compared with the one-component elimination method proposed by Cao and Ye, previously mentioned two methods have closed-form solutions and they do not require iteration. The proposed method requires neither the calibration sources nor multidimensional parameter search. Furthermore, it can cope with more sources and work for various apertures of UCA. Simulations verify the effectiveness of the proposed method.

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On improvement of joint estimation of DOA and PPS coefficients impinging on ULA

Cited by 11 publications

References 21 publications

A Sparse Decomposition-Based Algorithm for Estimating the Parameters of Polynomial Phase Signals

A Sparse Decomposition-Based Algorithm for Estimating the Parameters of Polynomial Phase Signals

Estimating DOA and PPS parameters of signal received by ULA in heavy noise environment

Direction of Arrival Estimation for Uniform Circular Array via “Virtual Two-Dimensional Source Impinging on Virtual Multiple UCAs Equivalently” in the Presence of Gain-Phase Errors

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