A closed-form phase-comparison ML DOA estimator for automotive radar with one single snapshot

Huang, Chung Jung; Dai, Chia Wei; Tsai, Tsung Yu; Chung, Wei Ho; Lee, Ta-Sung

doi:10.1587/elex.10.20130086

Cited by 9 publications

(9 citation statements)

References 7 publications

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“…where ‖z‖ W 2 ≜ z H Wz. A closed form solution of the problem in Equation 15 can be obtained as follows:…”

Section: The Iaa-apes Algorithmmentioning

confidence: 99%

“…In many practical applications, for example, in sonar processing, due to physical constraints, e.g., sound speed, only a very small number of snapshots or, in the worst case, a single snapshot is available for DOA estimation [13,14]. Another application in which the number of available snapshots is a critical parameter is the DOA estimation in automotive radar systems (see, e.g., [15]). In the single-snapshot scenario, the adaptive algorithms that require calculating the inverse of the estimated noise covariance matrix, e.g., the Sample Covariance Matrix (SCM), cannot be used since the estimate is rank deficient.…”

Section: Introductionmentioning

confidence: 99%

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Single-snapshot DOA estimation by using Compressed Sensing

Fortunati

Grasso

Gini

et al. 2014

EURASIP J. Adv. Signal Process.

128

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This paper deals with the problem of estimating the directions of arrival (DOA) of multiple source signals from a single observation vector of an array data. In particular, four estimation algorithms based on the theory of compressed sensing (CS), i.e., the classical ℓ 1 minimization (or Least Absolute Shrinkage and Selection Operator, LASSO), the fast smooth ℓ 0 minimization, and the Sparse Iterative Covariance-Based Estimator, SPICE and the Iterative Adaptive Approach for Amplitude and Phase Estimation, IAA-APES algorithms, are analyzed, and their statistical properties are investigated and compared with the classical Fourier beamformer (FB) in different simulated scenarios. We show that unlike the classical FB, a CS-based beamformer (CSB) has some desirable properties typical of the adaptive algorithms (e.g., Capon and MUSIC) even in the single snapshot case. Particular attention is devoted to the super-resolution property. Theoretical arguments and simulation analysis provide evidence that a CS-based beamformer can achieve resolution beyond the classical Rayleigh limit. Finally, the theoretical findings are validated by processing a real sonar dataset.

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“…where ‖z‖ W 2 ≜ z H Wz. A closed form solution of the problem in Equation 15 can be obtained as follows:…”

Section: The Iaa-apes Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Single-snapshot DOA estimation by using Compressed Sensing

Fortunati

Grasso

Gini

et al. 2014

EURASIP J. Adv. Signal Process.

128

View full text Add to dashboard Cite

show abstract

“…where U and V contain the corresponding left and right singular vectors, respectively, Λ is the diagonal matrix of singular values sorted in descending order, and (⋅) stands for the conjugate transpose. From the decomposition in (4)-(10), we have S, rank(S) = , and thus the best rank-approximation of S according to (10), denoted byŜ, isŜ…”

Section: Proposed Methodsmentioning

confidence: 99%

“…Therefore, the problem of parameter estimation in the case of small number of samples has been addressed in the literature [7][8][9], where the advanced concept of compressed sensing (CS) is applied. In particular, in the worst case such as automotive radar systems [10], only a single snapshot is available for parameter estimation of multiple spatial sources. That is to say, the problem of single snapshot DOA estimation is important in certain application and corresponding DOA estimation algorithms in singlesnapshot case have been recently proposed [11][12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Utilizing Principal Singular Vectors for 2D DOA Estimation in Single Snapshot Case with Uniform Rectangular Array

Pei

2015

International Journal of Antennas and Propagation

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The problem of azimuth and elevation directions of arrival (DOAs) estimation using a uniform rectangular array (URA) in single snapshot case is addressed in this paper. Using the principal singular vectors of the observed data matrix, an iterative procedure based on the linear prediction property, and weighted least squares is proposed for finding the DOAs with lower computational complexity. Furthermore, the azimuth and elevation parameters are automatically paired. Computer simulations are included to demonstrate the effectiveness of the proposed algorithm.

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“…DOA estimation is an important aspect in radar system [2], and some DOA estimation algorithms have been proposed in monostatic MIMO radar. In [3], the root finding technique based on Capon estimator is applied to DOA estimation, and the angle estimation performance is similar to RD-capon method.…”

mentioning

confidence: 99%

Non circular ROOTMUSIC algorithm for monostatic MIMO radar

Huang

Wang

Yang

2014

IEICE Electron. Express

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In this paper, a novel non circular ROOTMUSIC algorithm for direction of arrival (DOA) estimation in monostatic MIMO radar is proposed. The properties of noncircular signals can be used to form a new virtual array, which enhances the angular resolution. Then a reducedimensional transformation matrix is designed to convert the received data into a lower dimensional space, and the DOAs can be obtained by Root-MUSIC algorithm. Compared with RD-ESPRIT method and Root-MUSIC algorithm, the proposed method has better angle estimation performance and handles more targets than RD-ESPRIT. Simulation results are presented to verify the effectiveness of the proposed algorithm. Keywords: DOA estimation, MIMO radar, non circular signal, ROOTMUSIC Classification: Microwave and millimeter wave devices, circuits, and systems References

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A closed-form phase-comparison ML DOA estimator for automotive radar with one single snapshot

Cited by 9 publications

References 7 publications

Single-snapshot DOA estimation by using Compressed Sensing

Single-snapshot DOA estimation by using Compressed Sensing

Utilizing Principal Singular Vectors for 2D DOA Estimation in Single Snapshot Case with Uniform Rectangular Array

Non circular ROOTMUSIC algorithm for monostatic MIMO radar

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