Direction of Arrival Estimation Based on Fourth-Order Cumulant Using Propagator Method

Palanisamy, P.

doi:10.2528/pierb09081806

Cited by 19 publications

(17 citation statements)

References 21 publications

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“…, θ d . The output of the array is given as follows: Porat and Friedlander [4] proposed fourth order M 2 × M 2 cumulant matrix for DOA estimation which can be written as follows [4], [5]:…”

Section: Preliminaries and Problem Formationmentioning

confidence: 99%

“…Signal and noise subspaces estimated using higher order statistics are more precise in the presence of spatially correlated Gaussian noise because all the cumulants of order greater than two are zero for additive Gaussian noise [4]. Several efforts have been made to investigate DOA estimation problem by using fourth order cumulants [4], [5], [6], [7], [8]. Many authors applied ESPRIT principle on different pairs of cumulant matrices [6], [7], [8] and achieved more or less the same results.…”

Section: Introductionmentioning

confidence: 99%

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Multiple invariance cumulant ESPRIT for DOA estimation

Ahmed

Khan

Tufail

2014

2014 International Conference on Robotics and Emerging Allied Technologies in Engineering (iCREATE)

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In this paper, cumulant based direction of arrival (DOA) estimation using multiple invariances is proposed which results in Multiple Invariance Cumulant ESPRIT (MICE) algorithm. In all previous formulations of cumulant based ESPRIT, only one invariance is exploited for DOA estimation. The cumulant matrix (if chosen properly) inherits the multiple invariance property if multiple displacement invariances are present in the sensor array. DOA estimation can be improved by exploiting these invariances simultaneously. A subspace fitting based fitness function is developed which simultaneously incorporates these multiple invariances. MICE depends on the effective minimization of this fitness function. Newton's method based minimization of this fitness function leads to the cumulant counterpart of (second order) Multiple Invariance ESPRIT algorithm (MI ESPRIT). Genetic Algorithm based minimization of this fitness function has also been investigated and shown to have various advantages. Simulation results are presented to show the effectiveness of the proposed method.

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Section: Preliminaries and Problem Formationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multiple invariance cumulant ESPRIT for DOA estimation

Ahmed

Khan

Tufail

2014

2014 International Conference on Robotics and Emerging Allied Technologies in Engineering (iCREATE)

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show abstract

“…• indicates the Frobenius norm, and the optimal solution P is given by (17) It can be seen from function (17) that the M DOAs of the incoming signals can be obtained by means of one-dimensional (1 D) − spectrum-peak search over . However, to further reduce the computational burden, we can improve the function (17) to derive a more efficient search-free modification estimator in computation based on polynomial rooting [32].…”

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confidence: 99%

A Fourth-Order Cumulants Orthonormal Propagator Rooting Method Based on Toeplitz Approximation

Shi

Guan

et al. 2020

Preprint

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A novel Toeplitz fourth-order cumulantsAorthonormal propagator rooting method of direction-of-arrivalnestimation for uniform linear arrayois proposed in this paper. Specifically, the modified (i.e., reduced-dimension)vmatrix is achieved at first via removing the redundant information encompassed in the primaryematrix, then the lmatrix which possesses Toepltiz structure can be recovered by utilizing the Toepltiz approximation method. To reduce the computational complexity, an effective method based on the polynomial rooting technology is adopted. Finally, the DOAs of incident signals can be estimated by exploiting orthonormal propagator rooting method. The theoretical analysis coupled with simulation results show that the proposed resultant algorithm can reduce the computational complexity signify- cantly, as well as improve the estimation performance in both spatially-white noise environment and spatially-color noise environment.

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“…이를 이용하여 표적의 방위 추정 및 성능분석을 수 행하는 연구가 진행되었고, [16][17][18][19] [18] x   s  n, 그러므로 표적신호의 수신정보 에 대한 확률밀도 함수는 Eq. (2)와 같다.…”

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Error analysis of acoustic target detection and localization using Cramer Rao lower bound

Park¹,

Cho²,

Kang³

2017

The Journal of the Acoustical Society of Korea

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In this paper, an algorithm to calculate both bearing and distance error for target detection and localization is proposed using the Cramer Rao lower bound to estimate the minium variance of their error in DOA (Direction Of Arrival) estimation. The performance of arrays in detection and localization depends on the accuracy of DOA, which is affected by a variation of SNR (Signal to Noise Ratio). The SNR is determined by sonar parameters such as a SL (Source Level), TL (Transmission Loss), NL (Noise Level), array shape and beam steering angle. For verification of the suggested method, a Monte Carlo simulation was performed to probabilistically calculate the bearing and distance error according to the SNR which varies with the relative position of the target in space and noise level.

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Direction of Arrival Estimation Based on Fourth-Order Cumulant Using Propagator Method

Cited by 19 publications

References 21 publications

Multiple invariance cumulant ESPRIT for DOA estimation

Multiple invariance cumulant ESPRIT for DOA estimation

A Fourth-Order Cumulants Orthonormal Propagator Rooting Method Based on Toeplitz Approximation

Error analysis of acoustic target detection and localization using Cramer Rao lower bound

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