Adjacent co-prime array for DOA estimation of real-valued sources

Liu, Li; Xu, Jia; Huang, Zuzhen; Wang, Guan; Li, Teng

doi:10.1109/radar.2017.7944324

Cited by 8 publications

(5 citation statements)

References 10 publications

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“…Compared to the method in [29] which announce to have the largest SCDA aperture of N 2 + N − 1, our method also has 20 more lags when N = 10. In this section, we compare the SDCA aperture of the proposed arrays with D = 6, 8, 10 and two recently proposed sparse arrays named ADCA [28], and NADiS [29], which also aim to construct the SDCA utilizing both the correlation and the conjugate correlation statistics. Table 1 shows their respective coarray aperture where the numbers of physical sensors are chosen to be 10 to 20.…”

Section: Proposed Sparse Arraymentioning

confidence: 99%

“…Some empirical results have been proposed, a customized 6-sensor sparse array in [27] provide the largest SDCA aperture among the 6-sensor arrays. And an adjacent coprime array (ADCA) in [28] can generate N 2 /2 + 3N + 1 virtual sensors within the SDCA using N physical sensors. In addition, [29] has proposed a systematical methodology for designing a SDCA-based sparse array, which announced to have the largest SDCA aperture of N 2 +N −1 with N sensors.…”

Section: Introductionmentioning

confidence: 99%

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Sparse Array Design Exploiting the Augmented Conjugate Correlation Statistics for DOA Estimation

Song

Shen

2020

IEEE Access

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With the augmentation of the non-zero conjugate correlation statistics of some modulating signals, enlarged degrees of freedom (DOFs) can be achieved with conventional sparse arrays as proposed by many literatures. But little research has been carried out on sparse array design with the augmented statistics. In this paper, a novel sparse array geometry which exploits both the correlation and conjugate correlation statistics is proposed. It stems from the prototype nested array with transposed subarrays. The sensor locations are determined systematically, and the closed-form expression for the achieved aperture under given number of physical sensors is derived. In our scheme, a new hole-free virtual array called the sum difference coarray is constituted with the proposed sparse array, in which all the lags are consecutive. Due to the output of the coarray is obtained by averaging the repeated lags generated by the addition and subtraction of the physical sensor location, the hole-free structure of the coarray enables all the lags engaged in averaging and makes the output of the coarray more stable. Moreover, the coarray has larger aperture than existing sparse arrays, so that bringing more degrees of freedom. In the end, simulations are conducted to demonstrate the superior performance of the proposed scheme over other existing structures.

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Section: Proposed Sparse Arraymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sparse Array Design Exploiting the Augmented Conjugate Correlation Statistics for DOA Estimation

Song

Shen

2020

IEEE Access

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“…Though the SCA of a CPA is well illustrated in [16]- [17], it is not sufficiently exploited to improve the DOFs. Here, inspired by the design of RAS-NA, the RAS-CPA is arranged as…”

Section: B the Design Of Ras-cpamentioning

confidence: 99%

A Reversed and Shift Sparse Array Scheme based on the Difference and Sum Co-array

Zhou,

Li,

Wei

2020

Preprint

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The reversed and shift (RAS) sparse array scheme, which is based on the difference and sum co-array (DSCA) and remarkably enhances the capability of identifying sources, is proposed. For the original nested array (NA) or co-prime array (CPA), there exists a large overlap between its difference coarray and sum co-array, which prevents it from obtaining high degrees of freedom (DOFs). Motivated by this fact, the RAS scheme is designed for reducing the overlap while increasing the DOFs for direction of arrival (DOA) estimation. The proposed scheme is effective for both NA and CPA. The closed-form expression for the relationship between the number of physical sensors and the number of consecutive DSCA sensors is derived. Compared with some representative DSCA based sparse arrays, the proposed one can achieve longer consecutive virtual array. Simulation experiments are carried out to exhibit the enhanced DOA estimation performance of RAS scheme.Index Terms-Direction of arrival (DOA) estimation, nested array (NA), co-prime array (CPA), difference and sum co-array (DSCA).

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“…Design of MRAs cannot be predicted although a MRA for N≤17 sensors are created in [8]. Reducing the number of the sensors for the underdetermined environment is discussed in research publications [9]- [25]. Larger aperture virtual array or co-prime array are used for the AOA estimation for an underdetermined system with nonlinear uniform linear array (NULA) with the use of a lesser number of sensors [5], [25].…”

Section: Introductionmentioning

confidence: 99%

“…Reducing the number of the sensors for the underdetermined environment is discussed in research publications [9]- [25]. Larger aperture virtual array or co-prime array are used for the AOA estimation for an underdetermined system with nonlinear uniform linear array (NULA) with the use of a lesser number of sensors [5], [25]. Minimum hole array (MHA) [9] is another method for virtual sensors or arrays in the underdetermined system.…”

Section: Introductionmentioning

confidence: 99%

Hybrid basis vector based underdetermined beamforming algorithm in optimized antenna reconfiguration

Prasad¹,

Maheshappa²

2021

IJEECS

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Optimized positioning of antenna to obtain the best beam forming solution is adopted in this research. Non-uniform linear array-based beamforming algorithms have the challenge of placing the array of antennas in positions that would implement best beamforming outputs. This paper attempts to obtain the optimized beam forming by tuning the sparse Bayesian learning based algorithm. The parameters used for tuning involve choosing the hybrid basis vector for creating the steering vector while at the same time developing the optimized position of the antennas. Basis vectors are the building blocks of the steering vector developed for the beamforming algorithm that finds the angle of arrival in antennas. Reconfiguration of antennas is carried out using particle swarm optimization (PSO) algorithm and the basis vectors are generated using two different ways. One by cumulating similar basis vectors and another by cumulating two different basis vectors. The performance of accurate detection of angle of arrival in the beamforming algorithm is analyzed and results are discussed. This basis vector and antenna distance optimization is adopted on the sparse Bayesian learning paradigm. Performance evaluation of these optimizations in the algorithm is realised by validating the mean square error (MSE) versus signal to noise ratio (SNR) graphs for both the cumulative basis vector and hybrid basis vector cases.

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Adjacent co-prime array for DOA estimation of real-valued sources

Cited by 8 publications

References 10 publications

Sparse Array Design Exploiting the Augmented Conjugate Correlation Statistics for DOA Estimation

Sparse Array Design Exploiting the Augmented Conjugate Correlation Statistics for DOA Estimation

A Reversed and Shift Sparse Array Scheme based on the Difference and Sum Co-array

Hybrid basis vector based underdetermined beamforming algorithm in optimized antenna reconfiguration

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